Bilişim Teknolojileri Anabilim Dalı
Adana Alparslan Türkeş University of Science and Technology128
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Phishing website detection based on a novel artificial intelligence technique
Due to the increasing use of Internet, researchers widely study to prevent cybercrimes. One of the cybercrimes is phishing websites. As cybercriminals employ increasingly sophisticated tactics, there is a growing need for innovative approaches to detect and prevent these threats. In this thesis, the time-varying mirrored S-shaped transfer function is applied for BGWO, BPSO, and BHHO, and the proposed models are performed on the phishing websites dataset. The proposed models have promising results on the phishing websites dataset
Şarkı sözlerinden yeni şarkı türetme: Derin öğrenme yöntemleri ile sanatsal üretim
Şarkı sözü yazımı yaratıcı bir süreçtir. Geleneksel olarak bu süreç ilham, sezgi ve bireysel yaratıcılığa dayanır. Ancak yapay zekâ teknolojilerinin gelişmesiyle birlikte bu süreç yeni bir döneme girmiştir. Son yıllarda, özellikle yaratıcı metin üretimi alanında, Doğal Dil İşleme (NLP) kapsamında önemli ilerlemeler kaydedilmiştir. Özellikle GAN mimarileri, özgünlük ve rastlantısallık gerektiren içeriklerin üretiminde dikkat çekmektedir. Bununla birlikte, GAN'ların metin üretimine uyarlanması, görüntü üretimine kıyasla daha karmaşık bir süreçtir. Türkçe gibi sondan eklemeli dillerde hem anlamlı hem de yapısal olarak tutarlı metinler üretmek daha da zordur. TextGAN, SeqGAN, LeakGAN, RankGAN, MaliGAN ve RelGAN gibi mimariler, Türkçe şarkı sözü üretimi bağlamında sistematik olarak değerlendirilmemiştir. Bu durum, çalışmanın temel problemini oluşturmaktadır. Bu tez çalışmasının amacı, mevcut şarkı sözlerini analiz ederek, özgün ve sanatsal değeri yüksek içerikler üretebilen en uygun yapay zekâ modelini belirlemektir. Bu kapsamda, derin öğrenme modellerinin yanı sıra duygu analizi, ritmik yapı analizi ve yaratıcı metin üretimi teknikleri kullanılmıştır. Elde edilen sonuçlara göre, düşük BLEU ve Jaccard skorları, modellerin referans metinlerle yüzeysel düzeyde benzerlik kurmakta zorlandığını göstermektedir. Buna karşılık, RelGAN modeli Cosine Similarity metriğinde en yüksek skoru (0.0693) elde ederek, anlamsal olarak en tutarlı çıktıları üretmiştir. MaliGAN modeli ise düşük perplexity değeri (9588.23) ile dil modelleme açısından öne çıkmıştır. Ancak, çeşitlilik metriklerinin (TTR ve Unique Word Ratio) 1.0 değerine ulaşması, anlamlı tekrarların eksikliğini ve doğal akışın zayıf olduğunu göstermektedir. Edebi analizlere göre, yalnızca RelGAN modeli tematik bütünlük ve duygu yoğunluğu açısından tatmin edici şarkı sözleri üretebilmiştir. Sonuç olarak sayısal başarı ve edebi değerlendirme birlikte ele alındığında, RelGAN en dengeli ve başarılı model olarak öne çıkmıştır. Türkçe gibi eklemeli yapıya sahip dillerde şarkı üretimi için GAN modelleri hâlen sınırlı kalmakta olup, gelecekte Transformer tabanlı yapılarla desteklenmesi önerilmektedir.
Renkli görüntüler için kriptografik şema
Due to the extensive use of multimedia applications, image coding standards are one of the ways that supported multimedia spreading. Internet is full of digital documents that are uploaded by thousands every second through social media or any websites for commercials or business. Encryption and decryption techniques are required for digital multimedia distribution. Today, privacy and authentication services are crucial for data dissemination through the Internet. This work focuses on image security integrating encryption with multimedia compression systems. Also, image encryption algorithms combining with JPEG encoding are also proposed. Three approaches for integrating encryption with image compression system are proposed. These are Color Plane Permutation, Discrete Cosine Transform (DCT) coefficients, and sign encryption of DCT coefficients. The proposed approaches are supported by selective encryption, are a portion of the coefficients from either the final results or intermediate steps of a compression system. The securities of the proposed approaches are also analyzed. Simulations results show that the proposed system is secure, low cost, and compatible with direct bit-rate control. These properties make the study suitable for multimedia applications with the real-time operation and image transmission.
Kablosuz sensör ağlarında saldırı tespiti
Given the possibility of using intelligent agents for the development of intrusion detection systems, obtained through a bibliographic survey, this work will first seek to develop the following hypothesis: The use of intelligent agents in the development of intrusion detection and prevention solutions in wireless sensor networks is quite suitable for such equipment, as they favor the development of solutions. In addition, the use of intelligent agents allows a better development of these systems, to provide satisfactory computational performance, adaptability, scalability, shorter time latency and load balancing. The general objective of this paper is to propose a framework, based on agents, for detecting intrusion in wireless sensor networks, with the best possible adaptation resources of network devices
Bireyleri, kuruluşları ve toplumun davranış kalıplarını verilerin analiz edilmesi ve jeosuzayal bilgilere dayanarak çözümler bulması için güçlendirecek büyük veri odaklı elektronik devlet
In the information era, it is more crucial than ever to use big data effectively in order to increase the efficiency, accountability, and openness of electronic government (e-Government). This essay investigates the revolutionary potential of big data-driven e-Government in altering societal behavioral patterns in Turkey. We aim to provide examples of how e-Government may employ these technologies to improve public services, inform decision-making, and actively engage citizens in governmental activities. Modern big data analytics, machine learning techniques, and predictive models are used to achieve this. This research examines the various aspects of big data applications in Turkish e-Government, such as how they support tailored public services, efficient resource management, sound policy-making, and effective citizen participation. We go into more detail on how these applications affect people's and organizations' behavioral habits. The findings indicate that big data-driven e-Government holds great promise for rethinking the citizen-government interaction model and fostering a society that is better data-informed in Turkey. As a result, this might promote organizational performance, foster the development of new social norms in communities, and make it possible for individuals to participate actively and meaningfully in governance in Turkey. The study emphasizes how careful management and ethical use of big data in e-Government can contribute to building a more inclusive, transparent, and ABSTRACT BIG DATA-DRIVEN ELECTRONIC GOVERNMENT TO EMPOWER INDIVIDUALS, ORGANIZATIONS AND SOCIETY BEHAVIORAL PATTERNS TO ANALYZING DATA AND FINDING SOLUTIONS BASED ON THE GEOSPATIAL INFORMATION ALI, Ali Hussein Ali M.S., Electrical and Computer Engineering, Altınbaş University, Supervisor: Asst. Prof. Dr. Sefer Kurnaz Date: June / 2024 Pages: 54 viii participatory government while being aware of the challenges that are inevitably there in Turkey.
Akıllı araç görüşünde derin evrişimli sinir ağı kullanılarak şerit segmentasyonu ve yol tespiti
This thesis will delve into the intricacies of designing, training, and evaluating a deep CNN architecture for lane segmentation and subsequently harness the discriminative power of SVMs for road detection. Through a comprehensive investigation of these methodologies, this thesis aims to contribute to the advancement of smart car vision systems, ultimately paving the way for safer and more reliable autonomous driving solutions in an ever-evolving urban landscape. As society becomes increasingly reliant on digital communication and data, the ability to understand, process, and generate human language has become a critical component in various applications, from virtual assistants and sentiment analysis to machine translation and content recommendation systems.
Değiştirilmiş KNN algoritmasını ve özellik ağırlıklandırmayı kullanarak DNS trafiği tabanlı izinsiz giriş tespiti
All computer networks can be the target of attacks, from the smallest, such as home networks, to the largest and most complex, such as corporate networks. To make the situation even worse, programs can easily be obtained through the Internet (or by other means), and then used for malicious actions, making any inexperienced person have the same skills as a professional It is necessary to propose a new database that contains a portion of the traffic that is present on an existing actual network environment in order to carry out testing in a real environment. This is necessary in order to accomplish the goal of conducting testing in a real environment The main objective is to create a network user control mechanism in order to recognize the user's legitimacy through their actions.
Öğrenci devamının kaydedilmesi ve tanınması derin öğrenmeyi kullanan yüzleri
There are several methods available to monitor student attendance in classes, such as biometric, radiofrequency, face recognition, and paper-based systems. However, the face recognition-based approach has been found to be both efficient and secure. In this study, a threshold to confidence has been implemented through Euclidean distance values to enhance the identification process. The Local Binary Pattern Histogram (LBPH) algorithm has been utilized for this purpose, as it has been demonstrated to be more effective than other methods such as Eigenfaces and Fisher faces. The Haar cascade method has been used for facial detection due to its robustness. The system's performance has been assessed in various scenarios, including recognition rates, false-positive rates, and detecting unknown individuals with or without a threshold. The system has demonstrated an impressive 79% recognition rate for students, with a 24% false-positive rate, and can identify students wearing glasses or a beard. The LBPH algorithm and Haar Cascade method contribute to the system's exceptional performance. The recognition rate for unregistered individuals in facial recognition technology is noteworthy even without the use of a threshold value, sitting at a commendable 64%. Moreover, the rate of false positives is impressively low, remaining at approximately 15% and 31%.
Elektrokardiyogram kayıtlarına dayanarak kalp hastalıklarının sınıflandırılmasına yönelik sinir ağları
The precise and timely detection of cardiac arrhythmias is crucial for healthcare practitioners since it significantly impacts patient outcomes. This study is centered on the enhancement of electrocardiogram (ECG) signal classification, with a specific emphasis on deep learning and the proposed model. The complex patterns included in electrocardiogram (ECG) data, commonly used in clinical practice, pose challenges to conventional classification methods. The efficacy of the technique is evidenced by its ability to generate notable results. The classifier has been trained and validated using a comprehensive electrocardiogram (ECG) dataset that has undergone preprocessing. The performance measures (accuracy, precision, recall) underscore the exceptional ability of the system to identify cardiac arrhythmias. The validation of the model on an independent dataset reveals its capacity to generalize, maintaining a high level of accuracy and providing valuable insights into arrhythmia. The findings are supported by a comprehensive classification report, confusion matrices, and ROC analysis. This work showcases the potential of deep learning in transforming the detection of cardiac arrhythmias, using the model as an illustrative example. The results of this study contribute to the enhancement of ECG classification techniques, hence enhancing the accuracy and reliability of diagnoses and patient treatment in the field of cardiology.
Optik fiberlerin ve modernin katkıları internet üzerinde öğretimi geliştirme teknikleri corona Covid-19 pandemisi sırasında
Internet service has become an indispensable aspect of daily life, extending its necessity to homes, institutions, and organizations worldwide. With the surge in technological advancements, increased demand, and a growing number of internet users, traditional copper wires have proven inadequate to meet the evolving needs. The progression of internet technology from the first to the fifth generation underscores the imperative to adapt and keep pace with developed nations. In response to these demands, optical fibers have emerged as a pivotal solution, providing high-speed data transmission essential for millions of users globally. This is particularly crucial given the substantial data volumes, often exceeding hundreds of gigabytes. Optical fibers find diverse applications, with Fiber To The Home (FTTH) standing out as a specialized conduit for efficiently transferring internet services from companies to homes and workplaces. Moreover, optical fibers play a pivotal role in the deployment of submarine and terrestrial cables, fostering global connectivity through the World Wide Web (WWW). This not only ensures seamless data flow but also supports critical applications such as Fiber To The Home, connecting nations and facilitating robust internet services worldwide. The shift towards optical fibers is imperative in the context of evolving internet technology, with the fifth generation becoming increasingly prevalent. This transition demands the adoption of means, media, and high-speed transmission lines capable of handling the demands of modern internet usage. Optical fibers, with their capacity for high-speed data flow, emerge as a key enabler in this scenario. In conclusion, the utilization of optical fibers is instrumental in addressing the escalating demands for high- vii speed and reliable internet services. From FTTH applications to the establishment of global connectivity through submarine and terrestrial cables, optical fibers play a central role in meeting the evolving needs of the digital age.
Çay bitkisindeki hastalıkların sinir ağları kullanılarak tespiti
This thesis investigates the automated detection of leaf illnesses in tea plants by combining optimization algorithms with cutting-edge computer methods, particularly neural networks. The need for agriculture is growing to maintain crop health with guarantee maximum yields, it is critical to promptly and accurately diagnose plant diseases. With Convolutional Neural Networks (CNNs) serving as the main architecture, the aim of this thesis is to build a reliable and effective technique that can identify several diseases that harm tea plants. The methodology entails the procurement from differing dataset of tea plant leaves, comprising the healthy and unhealthy specimens. Preprocessing the dataset takes into account variables including disease severity, illumination, and image resolution in order to improve model performance. The most recent optimization techniques, such as the mayfly and pelican optimization algorithms (MA), are used in this study to train the neural network using this well selected dataset. The method's performance in disease detection is estimated using metrics like accuracy, MSE, F-score and recall, and sensitivity. The CNN-POAMA model, which was proposed, attained values of 94.5%, 0.035, 0.91, 0.93, and 0.92, respectively. The findings of this study have important ramifications for the farming sector as well as for the growth of automated technologies that have the possible to totally convert the way tea plants manage illness. The model's scalability and potential for real-world application demonstrate how well neural networks and optimization algorithms work together to solve challenging agricultural problems.
Kardiyovasküler hastalık tahmininin geliştirilmesi için entegratif makine öğrenmesi yaklaşımları: XGBoost ve ANFIS algoritmalarının karşılaştırmalı analizi
Cardiovascular diseases (CVDs) are the leading cause of death globally, underscoring the need for advanced detection and diagnostic methods to enhance patient outcomes. This study investigates the efficacy of two machine learning algorithms, XGBoost and the Adaptive Neuro-Fuzzy Inference System (ANFIS), in predicting heart disease across diverse datasets. Utilizing datasets from the UCI Machine Learning Repository, including Switzerland, Cleveland, Hungarian, Long Beach VA, and Statlog Heart, standard preprocessing techniques such as imputation, standardization, one-hot encoding, and SMOTEENN were applied to ensure consistent modeling conditions. Both models underwent extensive training and optimization. XGBoost excelled, particularly achieving 100% accuracy in the Switzerland and Statlog datasets, while ANFIS demonstrated its strength in modeling complex patterns, notably achieving perfect accuracy in the Cleveland dataset. Performance evaluations using accuracy, precision, recall, F1 score, F2 score, and ROC-AUC score highlighted XGBoost's consistent high precision and recall, vital for reliable CVD diagnosis. In contrast, ANFIS showed potential in clinical settings with its high F2 scores, emphasizing the reduction of false negatives. The study highlights the advantages of using advanced machine learning models like XGBoost and ANFIS in cardiovascular diagnostics, suggesting further research with larger and more varied datasets to refine these models and advance medical diagnostics using machine learning.
İş zekası araçları ile Türkiye'de pazar analizi ve finansal değerlendirmenin geliştirilmesi
The Turkish market emerges as a key player amid global shifts, owing to its unique cultural blend and strategic location. Amid economic intricacies, robust market analysis becomes vital for stakeholders to navigate complexities and seize opportunities. Market research provides a compass for decision-making, enabling identification of trends and mitigation of risks. Business Intelligence (BI) emerges as a transformative tool, blending technology and strategy to harness data for informed decision-making at all organizational levels. As businesses strive for agility and resilience, BI adoption becomes not just a competitive edge but a strategic necessity for sustainable growth in a dynamic landscape. This research provides a thorough method to examine Turkey's market and financial dimensions, utilizing the capabilities of Business Intelligence tools. Its objective is to connect data with practical insights, serving various stakeholders invested in understanding the Turkish economy. Keywords: Macro Economy, Business Intelligence, Financial KPI, Market Analysis, Machine Learning
Makine öğrenme metodolojilerini kullanarak yetkisiz erişime dayanıklı, sağlam bir bilgisayar ağ mimarisi geliştirin
As the use of IT spreads rapidly into new areas, the necessity to ensure the security of these systems has grown. Cyberattacks have also become much more sophisticated as a result of the widespread availability of information technology. Consequently, traditional security measures like SIDS have failed to identify new types of assaults. Intrusion Detection Systems (IDS) make it possible to track and gather harmful data inside a network. The majority of IDSs rely on signatures to identify potential threats. They use a set of rules—either manually entered by the administrator or created automatically by the system—to identify and respond to known threats. To maintain the availability of services at all times, network security experts focus on both preventing and responding to intrusion attempts. To find and categorize suspicious actions, security professionals employ tools like IDS. Hence, to protect privacy, security, and the ongoing delivery of services, it is crucial that the IDS continually keeps up-to-date with the most recent intrusion attack signatures. Important factors to consider while evaluating IDS performance are its speed and its capacity to learn new assaults. This study demonstrates how several Machine Learning techniques may be evaluated using the Knowledge Discovery and Data Mining (KDD) dataset, which is also called Knowledge Discovery in Databases. The primary focus is on creating a comprehensive and representative dataset for experimentation, with a strong emphasis on KDD. For this analysis, we have chosen to use the K-Nearest Neighbor (KNN) and Multilayer Perceptron (MLP) classifiers. The KNN classifier has shown the highest accuracy in recognizing and classifying all types of KDD dataset attacks (DOS, R2L, U2R, NORMAL, and PROBE), both for binary class (NORMAL vs. ABNORMAL) and multi-class scenarios. The experimental findings utilizing the proposed KNN and MLP models showed that the accuracy of binary classification using KNN and MLP was 99% and 97% respectively. Furthermore, the multi-class classification produced improved results compared to earlier work, with reported high-level accuracies of 92% and 87% respectively. This thesis investigates the effectiveness of using deep learning techniques, namely MNN and MLP designs, for network flow-based intrusion detection.
Sağlık IoT cihazlarının güvenliğinin sağlanması ve SIEM entegrasyonunun sağlanması: aAdresleme
This thesis explores the growing use of Internet of Things (IoT) devices in healthcare, which has transformed patient care with advanced monitoring and treatment options. However, this integration poses security challenges, demanding robust protective measures. The study focuses on integrating Security Information and Event Management (SIEM) systems with healthcare IoT devices as a solution. Through a thorough literature review, it sheds light on the current state of IoT security, emphasizing the need for improved protective measures. The main issue addressed is the vulnerability of healthcare IoT devices to security breaches and the associated risks to patient data and device functionality. To address this, the research suggests a comprehensive security framework tailored for these devices. This framework, drawn from literature and best practices, includes crucial security measures such as authentication, data encryption, access controls, and anomaly detection. Integrating SIEM systems into this framework provides real-time threat detection and swift incident response, bolstering the overall security of healthcare IoT devices. The thesis highlights the importance of this integration, offering a roadmap for secure, efficient, and effective implementations of healthcare IoT
Bulut bilgisayar: Bilgi teknolojisi dış kaynak kullanımı için daha iyi bir araç
A demonstrate for giving and utilizing IT administrations online is called cloud computing. This permits businesses to concentrate on their center competencies whereas taking off the complex errands of foundation administration to specialized suppliers. Cloud computing offers an approach to IT outsourcing that is more spry, temperate, and scalable. The appearance of cloud computing has reshaped the scene of data innovation outsourcing, advertising organizations a compelling elective to conventional models. This proposal see into the transformative potential of cloud computing as a predominant implies of outsourcing IT administrations. This consider investigates the benefits that cloud computing offers to the outsourcing worldview by implies of a comprehensive audit of suitable inquire about and case thinks about. It looks at how cloud-based solutions surpass traditional outsourcing contracts in terms of "scalability, cost-effectiveness, accessibility, reliability, security, innovation, and data recovery". Furthermore, this thesis assesses the suggestions of cloud computing on trade procedures, organizational structures, and IT administration hones. It examines the vital contemplations for receiving cloud administrations, including vendor selection, migration strategies, governance systems, and administrative compliance. By synthesizing hypothetical bits of knowledge with observational prove, this study gives a factual understanding of how cloud computing revolutionizes IT outsourcing.
Yüz tanıma sistemleri verimlilik karşılaştırması
Bu çalışmada, yüz tanıma teorilerine ve algoritmalarına artan bir ilgi olduğu belirtiliyor. Video gözetimi, suç tanımlama, bina erişim kontrolü ve insansız araçlar gibi endüstri uygulamalarında bu teknolojilerin önemli rol oynadığı vurgulanıyor. Hayatın birçok alanında kullanıma giren bu yüz tanıma sistemlerinin kendi içinde ayrıldığı yerel yaklaşımlar, bütünsel yaklaşımlar ve hibrit yaklaşımlar adlı sınıfları hakkında detaylı bilgi verilmektedir. Bu teknikler, yüz görüntülerini sadece belirli yüz özellikleri veya tüm yüz özellikleri kullanarak açıklamak için kullanılmaktadır. Sistemler arasındaki verimlilik düzeyleri karşılaştırılmaktadır. Literatür taramasında elde edilen çeşitli araştırma bulguları birbiriyle kıyaslanarak 3 yaklaşım öne sürülen teknikler gözden geçirilmekte ve verimlilik düzeyleri üzerine tartışma sunulmaktadır. Yaklaşımların hayatımızdaki yerleri, yüz tanıma hizmeti sunulurken ortaya sergiledikleri performansı; doğru eşleşmesi, tutarlılığı, sağlamlığı, yapısal durumu, birbirlerinden farkları bakımından avantajları ve dezavantajları listelenmektedir. Görüntü işleme ve bilgisayarlı görme alanındaki çalışmaların hızla yaygınlaştığı son yıllarda, tarım, tıp, eğitim, sağlık ve güvenlik gibi birçok alanda bilgisayarlı görme uygulamaları geliştirilmekte ve günlük yaşantımızda kullanılmaktadır. Özellikle yüz ve nesne tanıma işlemleri, farklı uygulamalarla her geçen gün dahada yaygınlaşmaktadır. Bu yüz tanıma uygulamaları, kullanıcılardan yüz bilgileri toplamak suretiyle veri ve bilgi tabanları oluşturmaktadır. Öne çıkan bir uygulama ise işyerlerinde personel giriş ve çıkışlarını takip etmek amacıyla geliştirilen yüz tanıma tabanlı personel kontrol ve takip sistemidir. Bu sistem, kartlı veya manuel kayıt tutma gibi yöntemlerin yerine daha hızlı, etkili ve doğru bir çözüm sunmaktadır. Giriş ve çıkışlarda kameralar kullanılarak personellerin yüzleri tespit edilir ve kimlik doğrulaması yapılır. Bu sayede personellerin işe geliş, gidiş saatleri ve fazla mesai bilgileri otomatik olarak takip edilebilir hale gelir. Geliştirilen bu sistem, özellikle hijyen ve sağlık koşulları açısından kart veya parmak izi gibi geleneksel yöntemlerin zorlayıcı olduğu durumlarda etkili bir çözüm sunmaktadır. Biyometri, bireyleri birbirinden ayırt etmeyi mümkün kılan fiziksel ve davranışsal özellikleri inceleyen bir bilim dalıdır. Bu kapsamda, biyometrik sistemler, bireylerin kimliklerini belirlemek amacıyla özel biyometrik özellikleri kullanarak tasarlanmış sistemlerdir. Bu sistemler arasında yer alan yüz tanıma sistemleri, bireyleri tanımlamak için yüz özelliklerini kullanır. Yüz tanıma sistemleri genellikle güvenlik ve personel devam kontrolü gibi alanlarda, cep telefonlarında, sosyal medyada, bina giriş ve kontrol alanlarında yaygın olarak kullanılmaktadır. Bu çalışmanın amacı mevcut yüz tanıma sistemlerinin uygulama alanları hakkında bilgi vermek, yüz tanıma sistemlerinin sınıflarını incelemek ve verimliliklerini karşılaştırmaktır. Anahtar Kelimeler: Yüz Tanıma Sistemleri, Güvenlik Sistemleri, Biyometrik Sistemler, Kişi Tanımlama, Verimlilik
Şehir içi trafik sıkışıklığını azaltmak için bir araba paylaşım uygulaması tasarımı
Urban areas across the globe are negatively impacted by traffic congestion, and Istanbul is no exception. The city's traffic situation is especially daunting due to its large population of 16 million individuals who reside and work there [1]. To address this issue, carpooling has been identified as an effective method for decreasing traffic congestion and promoting sustainable transportation. As a result, we created a carpooling application tailored for Istanbul with the goal of mitigating traffic congestion. The app permits drivers and passengers to generate personal profiles, seek out carpooling companions, and communicate with one another to coordinate rides. In addition, the app offers services like booking rides, processing payments, and assessing and evaluating experiences to guarantee safety and liability. Overall, to ensure the success of the project, the app will be designed to address the specific needs of Istanbul's urban population and to provide a viable solution to the city's traffic congestion problem using the database schema and API architecture using the PHP programming language and MySQL database management system. The app will be also scalable and can be adapted to other urban areas facing similar traffic problems and compatible with multiple platforms.
Derin öğrenme aracılığıyla hibrit tespit geliştirmeleriyle ağ ve sunucu giriş tespiti
Network security and the identification of possible threats depend heavily on anomaly detection in network traffic. In order to determine which machine learning models are most useful in detecting network traffic anomalies, this research compared and evaluated a number of different machine learning models. Deep Learning with 10 epochs, Logistic Regression, Random Forest with a Filter method, Random Forest with a Wrapper method, and Random Forest with a Wrapper method were the models evaluated. A number of metrics were used for evaluation, including cross-validation, accuracy, F1-Score, ROC (Receiver Operating Characteristic), and precision- recall. The results showed that the Random Forest with the Wrapper approach and the Deep Learning model outperformed other assessment criteria. With a ROC score of 0.9716, the Deep Learning model had the best performance, clearly demonstrating its superior ability to differentiate between regular and abnormal network traffic. It also displayed outstanding precision-recall scores of 0.9621, indicating accurate anomaly identification with a low incidence of false positives. The Deep Learning model also attained a strong F1-Score of 0.8405, which represents a balanced mix of recall and precision.
Siber güvenliği geliştirmek dijital görüntülerle oluşan siber tehditlerin azaltılması
This thesis introduces a cutting-edge Virus Detection App designed to address cyber threats embedded in digital images, with a particular focus on the underlying algorithms, specifically leveraging artificial intelligence (AI). The core of the application revolves around the implementation of the InceptionV3 deep learning model, a powerful convolutional neural network (CNN) renowned for its image recognition capabilities. The algorithmic workflow begins with the preprocessing of digital images. Images are resized to a standardized 300x300 pixel format to insure thickness in the input data. The preprocessing step is pivotal for optimizing the model's performance by furnishing invariant input data for analysis. Transfer literacy is a crucial element of the algorithmic approach. The InceptionV3 model,pre-trained on the ImageNet dataset, serves as a point extractor. By using the knowledge acquired during its expansive training on a different set of images, the model can discern intricate patterns and features in the digital images applicable to malware, contagions, and trojans. This transfer of knowledge significantly accelerates the training process and enhances the model's capability to generalize across different image datasets. The comprehensive scanning point, a highlight of the operation, involves the methodical operation of the trained model to a stoner- named set of images. The model's prognostications are decrypted, and implicit pitfalls are linked. The choice of a top- 3 vaticination strategy offers a nuanced understanding of the model's confidence in its assessments. Continual literacy is eased through a stoner- touched off model training process. The model is streamlined on a dataset, icing its rigidity to evolving cyber pitfalls. The use of a thick subcaste with a double sigmoid activation function enhances the model's perceptivity to relating infected images while minimizing false cons. This exploration underscores the critical part of sophisticated algorithms, embedded in artificial intelligence, in creating a robust and adaptable Contagion Discovery App. The admixture of preprocessing, transfer literacy, and continual training contributes to the efficacity of the operation in relating and mollifying cyber pitfalls in digital images.
Ad hoc ağlarda siber güvenlik tehditlerinin tespitinin değerlendirilmesinde yapay zeka kullanımı
This study explores the use of Artificial Intelligence (AI) to enhance cybersecurity in Mobile Ad-hoc Networks (MANETs), which are
Yüz tanıma sistemi için VGG tabanlı özellik çıkarma
•Facial recognition technologies are one of the main aspects of many things for example; security, biometrics, and social media. That is where we go ahead to present a feature extraction for our face recognition system based on the VGG approach. We assemble a collection of facial images and then process them to keep all the images consistent and properly set to avoid poor-quality images. The prioritized model exemplifies the use of VGG16, employed to extract high-level features from faces, that follow identification by the classification algorithm. System efficiency is evaluated concerning indicators of quality, for instance, accuracy precision, recall, and F1-Score. The results show that our model, based on feature extraction using VGG, has high accuracy and an accuracy rate with an LR model is 91%, ANN 0.87, SVM 0.89, KNN 0.74, DT0,39, GB0.75, and RF 0.74for FR. The results show that our proposed works well and is efficient in facial recognition functions. We believe that this kind of research takes facial recognition technology to a new level of development and will be a great example for other studies.
Üstün pnömoni tespiti için bok böceği ve fick yasası kullanılarak hibrit optimizasyonla geliştirilmiş yeni bir derin öğrenme çerçevesi
Pneumonia is a leading cause of global mortality, particularly among children and the elderly, and is characterized by inflammation of the air sacs in the lungs. Early and accurate detection is crucial to mitigating its spread and improving patient outcomes. Traditional diagnostic methods, including physical exams, blood tests, and chest X-ray analysis by radiologists, are effective but often time-intensive, resource-heavy, and prone to human error. These limitations underscore the need for advanced, efficient diagnostic solutions. This study leverages deep learning techniques, specifically Convolutional Neural Networks (CNN) and MobileNet architectures, to enhance pneumonia detection using chest X-ray images. Deep learning excels in image recognition tasks through automatic feature extraction, making it a powerful tool for medical diagnostics. The research applies these models to a large dataset of chest X-rays labeled as NORMAL or PNEUMONIA, utilizing rigorous preprocessing techniques, including normalization and augmentation, to improve data quality and diversity. Hybrid optimization techniques inspired by the dung beetle optimizer and Fick's law of diffusion are employed to optimise model performance, effectively navigating complex parameter spaces. Evaluation metrics such as accuracy, precision, recall, and F1-score validate the models. Results demonstrate the enhanced MobileNet model's superior performance, achieving an accuracy of 98.19%, significantly outperforming the CNN baseline. This highlights the potential of deep learning and innovative optimization methods in advancing pneumonia diagnostics. The findings emphasize the broader implications of artificial intelligence in healthcare, offering a pathway for developing robust diagnostic tools that expedite accurate and timely disease detection. This research contributes to alleviating the global pneumonia burden and underscores the need for continued innovation in machine learning to enhance medical outcomes.
Etkin botnet adli tıp için rastgele orman entegrasyonu ve boyut azaltma ile uyarlanabilir karar ağacı
Adaptive Decision Tree with Random Forest Integration and Dimensionality Reduction for Efficient Botnet Forensics proposes a new method for botnet detection by combining an adaptive decision tree with random forest integration. As response variables are highly dimensioned in botnet detection, multistep and time-consuming detection processes are major challenges. With an integrated method, we could first model the response variables as multiclass and regression formats to simplify the construction of decision trees. Then, based on the adaptive decision tree feature selection, we filter out non-obvious features to efficiently establish the random forest regression model under the appropriately sized feature space. Furthermore, to handle multilabel classification, a random forest is employed as the global model in Tree-2-Rule procedures to detect botnet-affected communication behaviors. Finally, real data experiments have been conducted based on the top datasets. The results show that the adaptive decision tree has excellent improvements in efficiency and accuracy. In future research, the GPU ninth-ordinal censored multistate diagnosis data is useful observed materials that do not destroy the random effect influence of the data. Also, whether the application of the random forest model could save more time in analyzing existing commercial status is an issue to be clarified in future development. Additionally, the development of the method proposed in this research requires further investigation. We could improve and then propose the preferable solution based on the research results. Our proposed solution can be utilized as a tool for efficient real-time bot incident investigations, in accordance with both academic and business objectives.
Derin öğrenme modeli ile zatürre tespiti
Zatürre, her bireyin yaşamının herhangi bir döneminde maruz kalabileceği bir akciğer enfeksiyonudur. Tedavi edilmediği takdirde ciddi sağlık sorunlarına ve hatta ölüme neden olabilir. Bu nedenle, hastalığın erken teşhisi büyük bir öneme sahiptir. Zatürrenin tespitinde çeşitli yöntemler kullanılmakla birlikte, en yaygın yöntemlerden biri akciğer röntgen görüntülerinin incelenmesidir. Bu görüntüler uzmanlar tarafından titizlikle değerlendirilir. Ancak bu sürecin doğruluğu, süresi ve harcanan emek önemli faktörler arasında yer almaktadır. Görüntü analizi alanında, yapay zekanın hızla gelişmesiyle birlikte önemli çalışmalar yapılmıştır. Bu tezde, akciğer röntgen görüntüleri kullanılarak makine öğrenmesi ile zatürre teşhisini destekleyen bir yöntem geliştirilecektir. Böylece, teşhis sürecinde zaman yönetimi, doğruluk oranı ve harcanan çabanın optimize edilmesi sağlanacaktır. Literatürdeki çalışmalar ve geliştirilen modeller dikkate alınarak, evrişimsel sinir ağı tabanlı yöntemler incelenecektir. Çalışmada kullanılan akciğer görüntüleri üç gruba ayrılmıştır: eğitim, doğrulama ve test. Sonuç olarak, evrişimsel sinir ağı modellerinin kullanılmasıyla elde edilen sonuçların, aynı konu üzerinde araştırma yapan araştırmacıların sonuçları kıyaslanarak literatüre katkıda bulunması ve doktorlara karar desteği sağlaması amaçlanmaktadır.
Eş odaklı güvenlik ile gelişmiş parola ölçerlerin tasarımında ve değerlendirmesinde sosyal etkinin araştırılması
Passwords have controlled the world of authentication. As a result of their extensive use, they have become a desired target for attackers. To thwart such assaults, many strategies have been used to increase password security. The websites and applications use password meters to assist users in creating complex passwords. The purpose of a password meter is to give the users knowledge about their password selection by identifying it as "weak," "medium," or "strong," for example. This study has taken into account social effect, or how the other influences a person's behavior and attitude. This social influence, often known as peer feedback, was taken into account while designing a peer feedback password meter. Participants who used the peer feedback meter and were instructed to create a special password had stronger passwords than those who used the conventional meter. In this research, many works related to methods of measuring password strength were reviewed, and algorithms used to measure password strength were also reviewed. Some of the ways in which the attacker hacked passwords were also mentioned. We aim to provide effective methods for evaluating passwords with five levels of integrity.
Daha temiz ortamlara doğru: Plastik şişe tespiti için derin öğrenme modellerinin çalışması
The widespread use of plastic bottles in our daily lives is contributing to significant environmental issues, particularly in marine ecosystems. A substantial quantity of plastic bottles is being carried back to the mainland from the sea by waves, often becoming trapped in coastal areas. The detrimental impact of plastic waste, including plastic bottles, on coastal ecology is a matter of concern. Thankfully, artificial intelligence (AI) has found diverse applications in various sectors, including environmental initiatives, offering promising solutions to combat these environmental challenges effectively. This report aims to provide a classification of bottle plastic using data images in various situations. We preprocess with the dataset and we apply different artificial intelligence algorithms to perform this classification. The CNN model achieved the highest ACC, reaching an impressive 99%.
Dijital pazarlamada yapay zeka destekli kişiselleştirmenin etik sonuçlari
The digital marketing industry is undergoing a transformation owing to the use of machine learning with Artificial intelligence (AI). This technology permits the industry to provide a highly personalized experience for consumers. As soon as it derives to AI, there's considerable discourse about engagement and devotion. The purpose is simple: AI bids a way to distinguish "stuff" at rate never before conceivable. Hitherto, despite the advent of these glowing outcomes, numerous ethical queries need answering. These ethical features have been inspected through thorough study of the literature and case studies to attain a modernized sorting of noticeable issues. These are: (1) the irresistible quantity of data being gathered and the confidentiality risks required; (2) the prejudices that algorithms can symbolize and the discrimination they can preserve; (3) the likely for consumer influence; (4) the economic disruption that AI might cause; (5) the transparency issues and the accountability shortfall that it generates. Although AI-powered personalization is able to produce additional pertinent and tempting consumer familiarities, it also presents serious ethical predicaments that must be addressed to guarantee responsible and evenhanded applications. The study concludes through recommendations for vendors and legislators to resolve the remunerations of AI personalization with the inevitability for ethical morals and consumer fortification, ultimately aiming to respect human principles and improve inclusive well-being.
Rastgele Orman'ın ötesinde: Yorumlanabilirlik gereksinimleri ile sigorta satın alma tahmini için gelişmiş topluluk ve hibrit modeller
This study focuses on creating machine learning models to predict whether people will buy insurance. We developed and tested sophisticated ensemble models that aim to be both highly accurate in their predictions and easy to understand: an important balance since insurance companies need to explain their decision-making processes to meet regulatory requirements. Using a dataset of 53,503 customer records, a comprehensive data preparation pipeline transformed 20 raw features into 100 engineered variables, thereby capturing temporal, behavioural, and categorical patterns. Class imbalance was addressed using Synthetic Minority Oversampling Technique (SMOTE) which ensures equitable model learning. Models which include Random Forest, Support Vector Machine (SVM), LightGBM, Graph Neural Networks (GNNs), and hybrid designs were compared, with Random Forest and LightGBM achieving near-perfect performance (F1-score: 1.0000 and 0.9999, respectively). GNNs, integrated by means of a k-NN similarity graph, showed scalability but small performance gains due to the sufficiency of original features. Since interpretability was a focus of this research, SHAP and LIME were used and their use revealed that temporal features such as "Days_Since_Purchase" and "Purchase_Year" were the main contributing factors. Also, optimization with multiple objectives using Optuna achieved a Pareto-optimal configuration (ROC-AUC: 1.0, F1-score: 0.9896, fidelity: 0.9843, sparsity: 3.0) which satisfies strict regulatory requirements (GDPR, NAIC). A case study on 1,000 customers showed the trade-off between high-performance LightGBM+GNN (ROC-AUC: 0.6211) and interpretable Decision Trees (ROC-AUC: 0.5380), with stakeholder feedback favouring transparency. The study recommends making temporal and monetary features priorities for customer segmentation and examining time-evolving GNN architectures and alternative graph constructions for future research to improve predictive power in noisier datasets. This framework provides a scalable, interpretable solution for insurance analytics, and that supports targeted marketing and compliance.
Eliminate entanglement in quantum information processing utilizing hybrid quantum-classical neural networks
Quantum entanglement is a physical phenomenon that lies at the heart of the contrast between quantum physics and classical physics, which distinguishes quantum mechanics as an essential feature. It happens when a collection of particles interacts with one another, participates, or creates a collection of particles in close spatial proximity in a dependent way in which the quantum state of one particle in this collection cannot be characterized independently of the other particles' quantum states. The states of quantum mechanics have several types of order, including Symmetry Protected Topology (SPT), in which matter has symmetry and a finite energy gap at zero temperature. Feedback set methods are used to derive results (how to identify specific quantum data source attributes) in a more consistent manner. The concept of quantum entanglement refers to that SPT states are symmetric short-range entangled states., since only "trivial" topological orders characterize short-range entangled states. The researcher presents and analyzes: Convolutional neural networks inspired by a quantum circuit-based technique, which is highly effective when applied within a hybrid neural network environment allowing them to be efficiently trained and executed on realistic quantum devices, this research simplifies and implements a quantum convolutional neural network (QCNN) in a hybrid neural network environment on the TensorFlow platform, where it functions as a suggested quantum equivalent to a conventional convolutional neural network. This thesis demonstrates how to detect certain characteristics of a quantum data source, such as a device's intricate simulation or a quantum sensor. With or without an excitation, a cluster state will operate as the quantum data source, which the QCNN will learn to identify. In this study, the researcher classed the SPT phase as a dataset. The QCNN system attained a validation accuracy of 100% Instead of 89.58% in its purely quantitative analog, we use three layers of quantum convolution followed by a classical densely connected neural network to identify an exciting cluster state within 10 epochs of time. This architecture should be quite successful in mitigating entanglement.
Hisse senetlerindeki trendlerindeki dalgalanmaları öngörmekderin öğrenme
Recent studies indicate that variations in the value of the stock market are difficult to anticipate due to the large number of unknowns and variables that affect its value on any given day. This includes the current market conditions, investor opinion toward a certain firm, and political developments. Hurriedly and arbitrarily, the pricing marketplaces are selected when setting the stock price. Due to the fact that it is not rare for the stock market to be dynamic and disorderly (due to several reasons), the stock market's direction is categorized as a random process, with more shifting possibilities in short time frames Therefore, an attempt to carry out a price forecast in the stock market can bring great benefits to investors, by increasing the level of information about the financial market, minimizing exposure to financial risk. In this sense, a computational technique called Artificial Neural Networks (ANNs) can be applied. The Artificial Neural Network (ANN) simulates on computers the functioning of the human brain in a simplified way.
Detection and classification of brain tumors in MRI images using deep convolutional neural network
Brain tumors is a chronic and inflammatory disease of the central nervous system (CNS), which causes white matter lesions in the brain and spinal cord, as a result of demyelination of axons The brain tumors. can be classified into four types: Remissive-recurrent (RRMS), Secondary Progressive (EMPS), Primary Progressive (PPME) and Recurrent Progressive (PRMS) Recent work in Machine Learning (Machine Learning) and Deep Learning (Deep Learning) has shown fruitful outcomes, as observed in clinical applications and medical image processing Manual segmentation of lesions on magnetic resonance images has become the standard despite being labour-intensive and subject to interobserver variability (e.g., interobserver variability) In this thesis, we aimed to test out these two novel CNN implementations in the hopes of noticing performance gains. Our approach differs from those of others in that we use a multi-layer CNN rather than the standard three-layer architecture (convolution + subsampling + classification). In order to automatically segment sclerotic lesions in a magnetic resonance image, it is necessary to: investigate the existing literature containing models for this task; detail the primary procedures involved in pre-processing the image. Evaluate MRI lesion segmentation strategies based on Convolutional neural networks; Incorporate a technique for automatic segmentation of lesions in MRIs of MS patients using magnetic resonance imaging analysis
Brain epileptic seizure diagnosis using electroencephalographic EEG signals
Monitoring frameworks employing image handling have gained significantly more attention among experts on the moving qualities like minimal costs, dependability, and flexibility toward merging with various innovations. Accordingly, the forest area fire area structure is arranged by creating revelation estimations in view of pictures and picture dealing with methods to segregate fire occasions in light of affirmation from Checking Focuses. In this examination task, the validation of a fire advance notice is supported by the proposed fire check assessment out. The fire check appraisal is proposed for the sales for fire picture and non-fire picture for the reduction of misdirecting cautions using picture managing moves close. A histogram evening out, RGB covering space, and YCbCr model-based structure is envisioned for data extraction from an information picture. The relationship of fire and non-fire symbolism is depicted using rule-based depiction. Histogram change gives the power makeover of fire covering pixels in the data picture, which is moreover overseen for the extraction of RGB parts. The erased RGB parts are sent off the change stage, where the image is exchanged over totally to a YCbCr covering model for secluding luminance from chrominance. Keywords: Machine Learning, EEG Signal, chronic disorder, Seizure Detection
Data mining and machine leaning methods for cyber security
There is drastic increase in needs of networking and data sharing in today's world. Such globalization of increased information technology and development there exists need of network security. Firewalls may provide some level of security but they never alert administrator for upcoming attacks. In order to find such abnormal behavior of network packets there is need of reliable detection system for improvement of efficiency and accuracy. Many research works focused on machine learning approach for enhancing the efficiency of the intrusion detection system and to detect malicious network activity automatically on the basis of network packet behaviors. The proposed model is designed using machine learning approach for detection of malicious activities of the network packets. For that CICDDoS2019 dataset is used. In this research, we proposed statistical methods are used obtain Z-scores, mean, median and mode for determining the significant features, training was performed for 80% of the dataset. The remaining 20% dataset was tested and validation using decision tree, K-NN and Gradient boosting algorithm. Keywords: Machine Learning, IDS, Cyber Attacks
Enhancing the lifespan of wsn by pdbac-leach protocol using clustering approach as an implementation strategy
The (WSN) are known as Wireless Sensor Network; it is a fundamental mechanism of the internet of things that has witnessed a universal avail in its utilise in numerous sectors such as the army, automation, agriculture, environment, etc. Regarding WSN, energy efficiency and message delivery reliability are crucial for applications like wireless detection and monitoring. This motivates researchers to do more and more investigations. Many different routing protocols, many of which are based on clustering and hierarchical topology, have been developed to improve performance in WSN to optimise the network's energy usage. There are several routing protocols out there, but most do not consider all the metrics necessary to prolong a network's life. This work introduces Probability Density Based Adaptive-LEACH (PDBAC-LEACH), an improved algorithm of a LEACH "Low Energy Adaptive Clustering Hierarchical" protocol designed to equalise the energy exhaustion of sensors nodes of the network to increase the WSN lifespan. The proposed protocol uses sensor node residual energy to select clusters heads (CHs). Therefore, only nodes with a high enough current could participate in the (CH) selection. From there, it prioritises both current energies as well as the distance to the BS (base station) to choose a root CH with increased current energy over the average energy of clusters head (CHs) and a decreased average distance to the BS (base station). All root of cluster head (CH) information is contained within this parent CH. With the help of the multi-hop procedure amongst CHs, the root clusters head (CH) collect data and then transmits BS (base station) in a compressed manner. MATLAB R2016b is used to simulate an enhanced algorithm. The model results described that the providing protocol is superior to previous works that employed LEACH Protocols to prolong network lifespan.
Derin öğrenme kullanılarak elektrokardiyogramlarda kardiyak aritmilerin tespiti
Machine learning algorithms, often known as machine learning, are used by medical diagnostic support systems to boost efficiency, accuracy, and turnaround time in patient care. Many modern medical monitoring tools have their roots in recent advancements in embedded machine learning applications. The latter have sensors for measuring biological signals in order to track the functioning of a specific organ in a subject. The primary purpose of these instruments is to gather signals, store them, and then analyze them so that a correct diagnosis may be made, or at least the symptoms of any underlying pathology can be identified. Within this framework, the work presented in this paper seeks to adopt novel methodologies for the analysis and diagnosis of Electrocardiogram (ECG) signals, with a particular emphasis on the detection of cardiac arrhythmia episodes.
Analysis of network security using cypher technique
Cryptographic procedures, such as encryption algorithms and cryptanalysis tools, necessitate a thorough understanding of mathematics. It's been used for a long time to teach these cryptographic concepts through hands-on experimentation supported by a theoretical framework. It's not enough to do theoretical hands-on trials if you're dealing with complex encryption approaches. There has been a huge improvement in educational programmes over the past few years, and cryptography is no exception. The goal of cryptographic programmes is to help students better comprehend the complex algorithms by presenting them visually in an understandable manner.
Optimal routing in internet of things networks using artificial intelligence
IoT will touch society more than past digital revolutions. It impacts global business, environment, and safety. IoT won't replace humans. Billion-connected Things require expertise. IoT enhances living, resource usage, and industry efficiency. If security precautions are not followed, the quantity of networked devices makes them vulnerable to intrusion, which can entail financial loss and death. Guard devices. Cybersecurity-certified devices are released. IoT creates commercial prospects. Benefits require infrastructure-to-endpoint security. Secure people and processes. Human less security wastes millions. Users voted this. Trusting devices and weak passwords. Most corporate users support weak-pass worded third-party devices on the internal network. "Every company will be invaded, we simply don't know when," said a consultant and strategic adviser. Home and corporate users should understand device vulnerabilities and risks before using them. AI, 5G, and edge computing improve IoT. We proposed sensor-coupled module chains and simulated annealing module placement for edge computing. Our simulation reduced energy and latency. Cloud and IoT are new ideas. Communication, distributed cognition, and on-demand massively parallel processing enable it. Cloud computing helps IoT. Complex IoT demands more computing. Understanding computer activity scheduling optimization approaches is vital. In a multi-cloud system with poor parallelism, enormous arrival rates, and variable runtimes, we investigated simulated annealing. Discrete event simulators evaluated system performance and cost. Simulations show this scheduling method increases performance and cost.
A machine learning approach to differentiate between acute asthma and bronchitis in preschool children
One of the common diseases that affects the lower respiratory tract infections (LRTI) of children around the world are acute asthma and bronchitis, it often occurs in preschool age and there are 12 clinical features overlapping between the two diseases, the most common of which are coughing, wheezing, runny nose, and shortness of breath. therefore, most people do not distinguish between two diseases, it is necessary to visit a specialist doctor to diagnose the cases and to receive the appropriate treatment for each case , due of the large number of cases that increase during weather fluctuations, such as high and low temperatures and environmental pollution such as fumes smoke and dust, etc., which need an accurate diagnosis, many junior doctors working in emergency halls face difficulties in diagnosing cases and the differentiation between the two diseases, so these doctors' resort to the consulting doctor to obtain a diagnosis that differentiates between the two diseases. In this study, we presented 3 machine learning models ( K - NN , Decision tree , MLP) and 2 deep learning models ( CNN, LSTM ), where we trained this models on a text dataset consisting of 512 real cases that collected by the paediatrician consultant at Fallujah Teaching Hospital for Women and Children in Iraq during four months started in march 2022 to June 2022 and after using all modern methods the final results showed that the CNN outperformed the rest of the models with an accuracy of (99.3506) and ROC – AUC ( 99.32 ) which was chosen as a binary classifier to this study .
Güç tüketimi tahmini için yapay sinir ağları algoritmalarının matlab uygulaması
At the same time as there is a drive to increase electrification efforts on a global scale, new technological innovations are leading to an increase in the amount of power that is consumed worldwide. As a result, we should anticipate an increase in the amount of electricity used inside the residential setting. It is becoming increasingly difficult to generate reliable projections about future power consumption as more information about the usage of energy throughout the world becomes available. Both the buyer and the seller stand to benefit from an accurate prognosis. A customer may be able to save money and reduce their carbon footprint with the assistance of a power forecast. It is essential for the effectiveness of the provider's attempts to regulate the flow of goods that they have a forecast that can be relied upon. Consequently, this type of modeling can make a contribution to the overall optimization of the supply chain in the residential electricity industry. As a result of the extensive interest in this issue, a broad variety of techniques to solving it have been investigated and assessed. In this study, we introduce the AAA-ANN and ANN-PSO algorithms for accurately predicting energy consumption. Both of these methods are based on artificial neural networks. In order to provide accurate projections of future power consumption, the algorithms underlying both approaches were developed in Matlab and then trained using data taken from the UCP database. The AAA-ANN method outperforms other approaches in terms of error curve analysis, training accuracy rate analysis, vitesting accuracy rate analysis, training error rate analysis, and testing error rate analysis. Afterc comparing AAA-ANN to ANN and ANN-PSO, it became abundantly evident that this particular method was the best approach for forecasting future power use.
A new technique for peak-to-average power ratio reduction in 5G communication
Recently, due to the ongoing growth rising demand for a wide range of services and an increase in the number of devices with the highest level of reliability and the shortest possible delay, there is a rising need to achieve high data rates. As a result, the fifth generation (5G) is developed to deliver improved services at a high data rate, is then developed as a result of this. Instead of filtering the full band, it does it for sub-carriers. Out-of-band emissions (OOB) are reduced using sub-band filtering, which also minimizes carrier interference (ICI) between nearby users. The UFMC's fast latency, strong frequency offset, and low out-of-band (OOB) emission help to improve spectrum efficiency. However, the high peak-to-average power ratio (PAPR) problem at UFMC could impede the functionality of high-power amplifiers and lead to nonlinear distortion. The universal filtered multicarrier (UFMC) waveform has quasi-orthogonal properties among subcarriers and is cheap in complexity. Additionally, unlike the orthogonal frequency division multiplexing (OFDM) system, it can achieve significantly greater out-of-band emission performance. As a result, we suggest the Whale Optimization Algorithm in this study (WOA), which helped to greatly enhance the performance of the UFMC system by achieving a large reduction in PAPR. The UFMC system's BER and PAPR performance is assessed through simulation. MATLAB is used to run every simulation.
Smart grid voltage control using AI based controllers
Electrical micro-grids are, as the name suggests, small electrical networks compared to conventional ones. They encompass the generation, distribution, and consumption of electricity in a small area. These new electrical system structures integrate advanced devices for the production and conversion of electrical energy, and incorporate modern and sophisticated control, automation, and communication strategies In the context of this research, the microgrid utilizes many power-generating sources but lacks a centralized controller or other kind of communication system. A controller is required to regulate the actual power output and reactive power output of several generations simultaneously. Both the actual and reactive power generated by these two diesel generators are under control. Consequently, the resilience of the Microgrid will be enhanced. In the case of a power outage, diesel generators will be utilized to keep the lights on, while alternative energy sources will be used to charge the batteries. Due to the absence of a clear link between the two generators, neural networks are an ideal tool for addressing non-linear problems.
Intellignce face recognition system
The purpose of this research is to utilize the libraries and models of the luxand platforms for the seek of facial recgnitions which pass through four main stages and other many secondary process to achieve the goal of recognition. The system capture the face image and start the process of analyess and face spotting to then for the stage of alignment where is the face features are detected then moving to the extraction stage where are calculation are done , all these calculations and numbers move to the fourth matching process where results are compared with previous stored information for the targeted face , all those processes utilize the locality preserving projections (LPP) .the PCA Linear Discrimination Analysis is used to solely structure the face spaces. The PCA, LDA, and LPP are classified of one of a kind diagrmaed model. The research is going to pass through the Luxaund technique.
Color correction of damaged archived videos content using superpixel technology
The study of an image on a pixel-by-pixel basis is not as fruitful as the examination of the image as a whole, despite the fact that the former requires a significant amount of work from the central processing unit of the computer (CPU). Pixels are combined into super pixels; The fundamental objective of this thesis project is to investigate the challenges that have been brought up in connection with the literary canon and to propose solutions that are applicable to these problems in a practical manner. The research of the many different extraction procedures that are based on clustering is going to receive a large amount of focus and attention from us moving forward. In addition, the efficiency of each and every conceivable option will be assessed, and additional suggestions for enhancing efficiency will be provided whenever it will be possible to do so.
Palm vein recognition by artificial neural network
After the great development in various fields of life, especially in technology, various methods have appeared recently to classify biometrics with artificial intelligence, such as fingerprint, eye print and palm vein fingerprint, where several methods were used to distinguish and met with great success. In this paper, we will classify the palm venous fingerprint of a Tongji dataset data set consisting of 3000 images distributed over 300 people for each person 10 images. This system went through three stages, the first stage was to obtain the area of interest and the pre-processing of the image, where in this stage a part of the palm was cut out to be processed after filtering it using Gauusian low filter converting the image to binary image, then the image pre-processing where the image was passed by the histogram equalization to get a clearer image and stronger features. The second stage is extracting features from the resulting image, where the Linear Discriminant Analysis was used for the purpose of extracting features, The classification was done using several algorithms, and we adopted the algorithm that gave the highest percentage. Three algorithms for machine learning and an algorithm for an artificial neural network were used in this research. Multilayer Perceptron Neural Network Classifier achieved 100%, while the rest of the algorithms, the result was 99%.
Web based GIS optimazation for coverage in wireless sensor network
In order to give flood forecasts and warnings, it is important to do research into the creation of a real-time GIS model that can be utilized for the visualization, modeling, and analysis of watershed management. This paradigm must be fully integrated with the Internet. To create an original prototype for a geographic information system (GIS), it is necessary to examine a diverse array of cutting-edge technologies. Due to these brand-new technologies, Internet-based computer modeling may now be carried out in a variety of distinct ways. Hydrology is researched in relation to the many technologies now available and their prospective uses. Using this framework, a number of geographical data structures may be included into a single conceptual framework that can accommodate a wide range of data models. To be useful for field monitoring data, it must have a rapid turnaround time and connect in real time with the geographic information system. As a result, it must be able to swiftly validate environmental simulation models and improve the accuracy of forecasts by analyzing hydrological flows and events in real time. As a last step, it should evaluate whether or not the explored concepts and skills are actual and interact with people
Efficient routing in vanet networks using clustering and segmentation algorithm
VANET is a type of network (MANET) that connects vehicles instead of cars. The VANET has wireless transmitters that can connect with other cars, infrastructure, and outside world as part of an intelligent transport systems (ITS) (V2X). DSRC and WAVE (wireless accessibility in vehicular contexts) protocols are utilized. VANETs are able to move nodes more quickly than MANETs. In VANETs, node movement is unpredictable and rapid, resulting in network availability, scalability, & structural instability. It decreases the network's Quality of Service (QoS), resulting in more communication errors We must show that bio-inspired routing algorithms outperform conventional routing algorithms due to the lack of research on bio-inspired routing algorithms for fixed-wing unmanned aerial vehicle networks. The absence of a three-dimensional environment, a suitable mobility model, and acceptable node speed ranges are all obstacles that must be addressed to solve the routing issue in VANET networks. This study's goal is to fill information gaps. With VANET mobility models, this research compares AdHoc, a bio-inspired routing algorithm, against AODV and OLSR, two common routing algorithms
Iot'de kötü yazılım tespiti için hibrit yöntem tabanlı derin inanç ağları
Malware is any software deliberately intended to reason disturbance to a computer, attendant, customer, or computer network, escape secluded information, improvement illegal admission to info or schemes, divest users access to info or which naively delays with the user's processer security and privacy. In this study, new method based DBN applied to detect malware attacks in IoTs. The presented method combined DBN with LDA which used to reduce the size of input features by selecting important features. then, the selected features wired to the DBN that trained to classify these features to normal and abnormal labels...
E-devlette siber güvenlik; Bir vaka çalışması kötü yazılım saldırısı tespiti
One of the new concepts offered to us by rapidly advancing technology in these days we live in the internet age is "Electronic Government (e-government)". E-government, state and citizen in the digital environment; is to establish a simple, fast, low-cost, uninterrupted and secure relationship. E-government, which helps citizens and governments in the internet age in order to fix the messy bureaucracy, takes the service transactions offered to the public to the citizens' feet instead of keeping them in archives, and performs these transactions in an easier, cheaper, more transparent and more modern way. In this study, malware attacks investigated in e government applications. The proposed method combined KNN based whale optimization algorithm and K-mean to enhance the performance of the model. furthermore, several classifiers such as DT, SVM and KNN applied to evaluate the results. In the last stage, the proposed method compared with several studies presented in this field and show that the presented biometric system is best with 99.54% accuracy.
A new deep learning-based framework for cyberscurity problems
In this study, we proposes new study based CNN-GA-random forest to detect the SQL injection attacks in IoTs. In the first stage, the CNN applied to extract high level features from input SQL inquiries. Then, the output of the CNN wired to the random forest. The random forest is robust classifier used in several classification and regression problems and presented remarkable results when compared with other classifiers. Then, the genetic algorithm applied to train the CNN to select best weight and basis of the model. The genetic algorithm is robust optimization algorithm and used in several fields to enhance the performance of the models such as design, classification, regression and estimation. The proposed system showed results with an accuracy of 99.93% compared to some studies.