Theses supervised by Doç. Dr. Sefer Kurnaz
24 theses · Altınbaş University
Low cost photovoltaic monitoring system based on lorawan network
Renewable energy, especially solar energy, is increasingly popular in both home and industrial sectors due to globalization. This thesis investigates the design and implementation of a low-cost photovoltaic (PV) monitoring system using the LoRaWAN network. Because it offers a virtual environment for modelling, evaluating, and experimenting with complex systems, the simulation of monitoring a PV system is essential for comprehending system behaviour. The system seeks to provide a low-cost solution for monitoring energy and environmental parameters in PV systems by using the features of LoRaWAN technology for long-range, low-power communication. This thesis offers a thorough analysis of the application of Internet of Things technology for the real-time monitoring of PV system performance. In order to improve the overall efficiency of PV systems, the goal is to offer a dependable and scalable system that facilitates ongoing monitoring, data collecting, and analysis. The system addresses the issues of cost-effectiveness, scalability, and remote monitoring in PV systems by including sensors to capture vital data, such as solar panel output, battery condition, and ambient variables. A LoRaWAN network, which offers long-range and low-power communication and supports data transfer, is perfect for off-grid and remote photovoltaic systems. By adding LoRaWAN technology, systems become more efficient and scalable, opening the door for widespread use of sustainable energy solutions. PV systems are now affordable enough for a broad spectrum of customers, including those who live in remote or underdeveloped areas. The system offers real-time data on PV system performance, facilitating preventive maintenance and well-informed decision-making, according to the findings of the field testing. The data from the DC voltage sensor and PZEM-004T V3 module provide crucial insights into the dynamic behavior of the electrical system under observation. The system's need for electrical power is implied by the continuous increase in active power consumption (Pac), which went from 933 to 2107 Watts. In the MATLAB/Simulink system, the measurement results are concurrently entered into the PV model enabling theoretical simulation and graphically shown as a dashboard. On the same software platform, the assessment and defect detection functionalities for PV modules within real-world operating circumstances are carried out and presented. The capabilities of visual monitoring, assessment, and defect detection of the suggested system have been shown with adequate confidence and accuracy. The suggested system has several benefits over the well-developed ones, including the ability to combine all visualisation monitoring, assessment, and fault detection inside the MATLAB/Simulink environment and to decrease both wire and hardware configuration.
Optimizing network performance rough image processing techniques in omputer science it
A fundamental apparatus for exploring digital protection risks in Internet of Things (IoT) networks is the Botnet of Things (BoT-IoT) dataset. This examination proposes an original way to deal with order BoT-IoT information by changing over the crude information into RGB images and afterward applying calculations, for example, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Irregular Woodland to a helpful classification model. The BoT-IoT dataset is at first changed over into RGB images, where each component vector addresses a pixel in the image. To protect the spatial data of the first information, these photographs are in this manner saved in the.png design. We might take benefit of the strong capacities of example acknowledgment and image regulation procedures for IoT information examination with this change interaction. The RGB images delivered from the BoT-IoT dataset were then sorted utilizing an aggregate classification method. The ensemble includes the most striking Irregular Woodland classifiers, KNN, and SVM, every one of which contributes unmistakable qualities to the general classification issue. To show up at the last classification choice, the estimates of discrete classifiers are shared utilizing a weighted democratic system. The recommended strategy is compelling, as proven by trial discoveries on the BoT-IoT dataset, where the ensemble model accomplishes a great exactness of generally 99.36%. What's more, the model performs well with regards to precision, review, and F1-score, exhibiting its adequacy in isolating noxious action from harmless movement in Internet of Things networks. Inside and out review was likewise finished to explain every classifier's commitment inside the ensemble setting. This examination uncovers the fitting properties of the classifiers: Arbitrary Timberland consolidates the limit of ensemble learning for upgraded speculation, KNN makes utilization of nearby neighbourhood data, and SVM succeeds at dealing with testing choice limits. In any case, this work offers a new way to deal with BoT-IoT information recording using ensemble learning strategies and RGB image models. The extraordinary exactness achieved features this strategy's capability to improve digital protection in Internet of Things networks. Through effective distinguishing proof and alleviation of pernicious activities, the proposed approach adds to the advancement of IoT security exploration and safeguards networked frameworks from arising digital threats.
Hybrid nature-inspired optimization for stability and disturbance rejection in tethered unmanned aerial vehicles.
The study proposes a new hybrid optimization model that is intended to be used in the design of a high-level attitude control system to be applied in tethered Unmanned Aerial Vehicles (UAVs). The suggested solution is based on a Fractional-Order Proportional-Derivative-Integral (FOPDD-I) controller alongside a hybrid optimum algorithm that will be a fusion of Harris Hawks Optimization, the Eagle Strategy, and Particle Swarm Optimization (HHHO-ES-PSO). A systematic comparison of the optimized FOPDD-I controller performance with a number of benchmark control schemes is made, such as the Conventional PID, Cascade PID, classical Active Disturbance Rejection Control (ADRC), and Advanced ADRC. Extensive simulations with MATLAB indicate that the HHHO-ES-PSO-tuned FOPDD-I controller has a better dynamic behavior, which is characterized by higher stability, quicker transient response, and better disturbance rejection than the traditional ones. The quantitative results of optimization show an increase in the proportional gain (K_p) by 56.5% and in the integral (K_i) gain by 65.2% and a reduction in the derivative gain (K_d) by 98.4% effectively reducing the yaw overshoot and oscillatory behavior. In addition, the parameters of the fractional-order were also adjusted in an adaptive manner, which resulted in adaptability gains of 12.5% and 14.7% in nonlinear dynamic conditions. Conversely, traditional PID and Cascade PID controllers only offered small gains in tuning, whereas classical and improved ADRC methods demonstrated moderate, but relatively small, gains in performance. The results of the study have also demonstrated that a significant portion of the value of applying a fractional-order control in conjunction with hybrid metaheuristic optimization lies in the fact that the suggested model, FOPDD-I controller, is a powerful and adaptable approach to tethered UAV attitude control in highly complex and uncertain operating conditions.
Eliptik eğri uygulaması nesnelerin interneti için kriptografi(IOT) güvenlik
The Internet of Things, or IoT, is essential to the fields of industry, healthcare, and information technology, among others. It is made up of numerous interconnected things that are in communication with one another. Because approved objects, in addition to users, may access data regarding the Internet of Things. For IoT applications and technology to be widely adopted, security is a necessary component. To improve the security of the IoT data, this study suggests an Elliptic Curve Cryptography approach. Two keys are used in Elliptic Curve Cryptography (ECC): a public key and a private key. The user uses the private key for encryption, and the public key is used for user identification during authentication. Similar to this, using the private key, the sender encrypts; if secrecy is desired, using the public key, one can decrypt the communication. Selecting the private key is a problem with every public key. Random selection of small values raises concerns about the overall algorithm's security. considering the values. This study suggests using the Cuckoo Search Algorithm to select values at random.
Mülteci ve göçmenlerin etkili tespiti Türkiye'nin çevresinde insanlar evrimsal sinir kullanıyor ağ
In the years following the outbreak of the Syrian Civil War in 2011, non-governmental organizations (NGOs) played a crucial role in assisting refugees and distributing relief to people in need so that
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.
Derin özelliklere sahip enerji verimli bir SDN-ıot mimarisi IoT destekli akıllı şehir için öğrenme tabanlı trafik tahmini
Thesis proposes an energy-efficient SDN-IoT architecture tailored for IoT-enabled smart cities. This architecture addresses the challenges of resource optimization and energy consumption management in the context of diverse IoT devices and dynamic traffic patterns. The architecture provides a framework for efficient network management and facilitates sustainable operation in smart city environments. A modified SVM (Support Vector Machine) algorithm is introduced and integrated into the proposed architecture for traffic prediction. By enhancing the traditional SVM algorithm with specific modifications tailored for IoT-generated data, the thesis contributes to improving the accuracy and reliability of traffic prediction in smart city networks. The modified SVM algorithm can effectively capture complex patterns and variations, enabling precise anticipation of traffic demands. The research presents a comprehensive evaluation of the proposed architecture's performance. Through extensive simulations and practical deployments in a smart city testbed environment, the paper assesses the energy efficiency, resource utilization, and predictive accuracy achieved by the architecture
Vıt-skınnet: Segmentasyon mekanizması ile etkili cilt kanserinin tespiti için yeni bir vizyon transformatör tabanlı ShufflenetV3 modeli
Melanoma, a deadly type of skin cancer, claims hundreds of lives every year. Skin cancer typically affects areas of skin that are exposed to sunlight on a frequent basis, such as the "legs, face, arms, and neck". By visually examining lesions with pigment on the skin, melanoma can be identified early and treated with a straightforward method of removal of the malignant cells. However, the examination of the skin by the naked eye alone has a restricted and inconsistent accuracy owing to the scarcity of dermatologists. This also leads the patients to undergo multiple biopsies and thus hampers the course of treatment. The automatic identification of lesions from the dermoscopy images involves several obstacles due to the intricate lesion characteristics and as a result of the detection backdrop. There is a dearth of research on major intra-class variations and inter-class similarities of lesion characteristics, and the prior solutions primarily concentrate on employing larger and more complicated systems for detecting the presence of skin cancer with much-enhanced detection accuracy. In order to address various issues with the traditional methods, it is therefore increasingly crucial to create a successful structure for the identification of skin cancer through the use of deep learning approaches. The implemented skin cancer detection and classification model has three crucial procedures to perform. The collection of dermoscopy images, the segmentation process for segmenting the images, and the detection phase are the three main stages of the implemented skin cancer detection system that is put into practice. First, the benchmark images are utilized to provide the images that are needed for the tests. Once the images are gathered, then the segmentation phase is executed. The segmentation step receives the gathered images as its input. Then, the Residual DenseUNet++ (ResDenseUNet++) is used to carry out an effective segmentation. At this point, the resultant segmented image is provided by the developed ResDenseUNet++, which is then considered for the later stage's inputs. Additionally, the segmented image is then given to the feature extraction process by which the gradient filter image and the texture pattern image are obtained. Additionally, the obtained image results are then inputted for the phase of skin cancer detection. During the detection phase, the newly developed ShufflenetV3 based on Vision Transformer (ViT-ShufflenetV3) is implemented and used to effectively recognize skin cancer. In many experimental validations, the recommended skin cancer identification system has ensured a more precise classification outcome regarding skin cancer than other traditional models.
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
Artık dikkat unet++ kullanılan segmentasyon mekanizması ile desteklenmiş etkili bir kademeli resnet tabanlı tüberküloz tespit çerçeves
Tuberculosis (TB) remains the only primary illness without accurate quick point-of-care diagnosis evaluations. Failing to stop TB transmission is mainly caused by being unable to identify and address all infected individuals with tuberculosis in the lungs in sufficient time, enabling the spread of TB among populations to keep happening. The present gold-standard methods for diagnosing TB are based in laboratories, and numerous tests across a period of time might be required until an outcome is obtained. TB triggered by Mycobacterium TB remains an infectious illness that is one of the most lethal in the entire globe. Several attempts are currently undertaken to precisely identify TB using "Chest X-ray (CXR)" images. Furthermore, novel diagnostic methods for diagnosing current TB illness, screening for dormant TB being infected and finding resistance to drug Mycobacterium TB isolates are now accessible. CXRs are assessed in medical practice by qualified doctors in the identification of TB. However, this is a laborious and arbitrary procedure. Differences in the diagnosis of illness based on radiographs are unavoidable. The development of a comprehensive point-of-care diagnostic is slow, while the identification of new biomarkers continues difficult. Despite successful methods for prevention, worldwide disease prevention requires a substantial proportion of prompt disease diagnosis and rapid treatment. Early identification of cases is based on the reliability of tests, affordability, availability, and difficulty, yet it also relies on legislative determination and donor commitment to provide viii ideal, long-term medical treatment to people most impacted by the TB and influenza outbreaks. As a result, diagnosing TB electronically using x-ray images is essential to assist patients as well as doctors. So, this work suggested an advanced detection framework for TB based on deep structure networks to effectively detect TB from patients' X-ray images. Initially, the X-ray images are gathered from the standard data sources. The collected images are given to the segmentation process, where Residual Attention Unet++ (RA-Unet++) is utilized for effectively segmenting the X-ray images. Then the segmented images are fed to Cascaded ResNet (C-ResNet) for providing a better detection performance. The evaluation of the recommended detection framework for TB based on deep structure networks is conducted to ensure the superior performance of the system. The results will ensure the excellent performance of the developed system by providing high-accuracy detection outcomes.
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.
Çeşitli trafik kaynaklarını kullanan performans bilgisayar ağına dayalı sanal özel ağ sisteminin geliştirilmesi
This study investigates the effects of implementing a Virtual Private Network (VPN on network performance, with a specific emphasis on throughput and time delay. The study entails the manipulation of connection types within three separate protocols, namely HTTP, FTP, and CBR, in order to examine the fluctuations in network performance metrics. The findings suggest that the integration of VPN has a minimal impact on the throughput of the Constant Bit Rate (CBR) protocol, whereas the File Transfer Protocol (FTP) and Hypertext Transfer Protocol (HTTP) protocols exhibit a decrease in throughput. Furthermore, the implementation of the VPN network results in a notable augmentation in the average time delay experienced across all protocols. The aforementioned findings provide significant contributions to the understanding of the intricate correlation between the implementation of VPNs and the performance of computer networks. These insights illuminate the complexities involved in ensuring security while simultaneously considering the potential compromises in network dynamics.
ITIL'ın müşteri analizindeki etkisi
The Information Technology Infrastructure Library (ITIL) is a widely recognized framework that provides best practices for IT service management (ITSM). Its principles are designed to enhance the quality of IT services, align IT operations with business objectives, and ultimately improve customer satisfaction. The purpose of this thesis is to explore the impact of ITIL principles on customer analysis, focusing on how these principles can be leveraged to better understand and meet customer needs within IT service contexts. The framework encourages the alignment of IT services with customer expectations and business goals, thereby fostering a culture of continuous improvement and service excellence. For instance, the implementation of ITIL processes has been shown to enhance service quality, increase reliability, and improve overall customer satisfaction. This alignment is not merely operational; it requires a strategic approach to understanding customer needs and integrating those insights into service design and delivery. Moreover, the ITIL framework facilitates the standardization of IT services, which can lead to more predictable and efficient service delivery. By adopting ITIL principles, organizations can create structured processes that allow for better incident management and service operation, ultimately leading to enhanced customer experiences. The focus on service operation within ITIL is particularly important, as it is during this phase that customers directly perceive the quality of IT services. The integration of ITIL with other governance frameworks, such as COBIT, further strengthens its impact on customer analysis. This integration allows organizations to align IT governance with customer-centric strategies, ensuring that IT services not only meet internal operational goals but also deliver value to customers. In conclusion, the principles of ITIL provide a robust framework for enhancing customer analysis within IT service management. By focusing on service quality, aligning IT operations with customer expectations, and fostering a culture of continuous improvement, organizations can significantly improve their service delivery and customer satisfaction. This thesis aims to delve deeper into these aspects, providing empirical evidence and case studies to illustrate the transformative impact of ITIL principles on customer analysis. Keywords: ITIL, Ai, Customer services, Software, Management
CRM yazılım sistemlerinde yeni trendler: Yapay zeka ve makine öğrenimi entegrasyonlarının müşteri analitiği üzerindeki etkisi
In this era of paced digitalization significant changes have surfaced. Nowadays companies are not just gathering customer information. Also concentrating on analyzing this data in an efficient manner to improve the overall customer satisfaction. The incorporation of machine learning and artificial intelligence, in CRM systems has revolutionized the way customer analysis is conducted making it a sophisticated and multifaceted procedure. Traditional CRM systems were originally created to store customer information and conduct analyses; however., AI and machine learning powered CRM systems offer sophisticated analytical features, like forecasting customer actions and creating customized marketing plans. These advanced technologies, fundamentally utilizing big data and data mining methods to analyze customer data, offer businesses strategic advantages in competitive markets. The contributions of machine learning and AI to CRM software play a crucial role, especially in customer segmentation, customer lifecycle analysis, and the implementation of upselling and cross-selling strategies. Moreover, these technologies allow for faster and more efficient analysis of customer complaints and feedback, enabling businesses to develop proactive solutions aimed at increasing customer satisfaction. Within the framework of machine learning and AI, the data analysis capacity of CRM software has been enhanced, providing deep insights into customer-business interactions and automating business processes (BPA), particularly in marketing. The primary objective of this study is to examine in detail the impact of AI and machine learning integrations on customer analysis in CRM software. Specifically, the study will explore the role of AI and machine learning in increasing customer loyalty, predicting customer behavior, and personalizing customer interactions. The contribution of these technologies to the development of customer-centric strategies by businesses will be assessed. By analyzing the effects of machine learning and AI on customer segmentation, customer lifecycle management, and data-driven marketing strategies, this study will investigate how these integrations have driven transformative changes in CRM software.
Design of advanced anomaly detection system based on hybrid machine learning technique with optimized feature selection in wireless sensor network
At present, the Wireless Sensor Network (WSN) plays a pivotal role in the wireless communication system that works based on a large number of sensor nodes. The development of the WSN has become more popular and the nature of versatility resulted in more security concerns and making it hard for the investigation to prevent the anomaly in it. One of the essential and challenging tasks in WSN is the security concern. Detecting the anomaly present in the network becomes the major challenge to ensure the security of WSN. In general, WSNs are affected by a different type of threats that tends the nodes to get damaged and form the wrong determination. Therefore, it is necessary to identify anomalous to minimize the false alarm. Moreover, the quality of the data gathered by the sensor nodes is mainly affected by the anomalies that are produced because of different reasons like reading errors, malicious attacks, failures, and unusual events. Hence it is significant to process the anomaly detection to ensure the quality of the sensor data before it is used to make decisions. In WSN, anomaly detection is the significant process to determine the anomaly or unusual event. However, timely anomaly identification is more complex to function to execute reliably in real-time. For the secure and reliable operation in the WSN, effective anomaly detection is more necessary. However, the standard anomaly detection techniques often fail to adequately secure the privacy of the data and identify the complex, particular, and unique breaches in the WSN. Also, the present anomaly detection models only process under the stationary environment and need to keep all the training data in the node. To address these limitations, a novel Hybrid Machine Learning Technique (HMLT) is introduced to effectively detect the presence of anomalies in WSN. The advanced hybrid technique is designed to enhance the detection performance and safeguard privacy. Initially, the required data from the WSN is collected from the available data resource. Further, the significant feature from the raw data is selected using the optimal feature selection process. Here the Secretary Bird Optimization Algorithm (SBOA) is used to achieve the optimal feature selection. Finally, the HMLT is implemented to perform the detection task, in which the hybrid classifier is the combination of the Deep Belief Network (DBN) along with the Bayesian Learning (BL). The model is specifically developed to identify the occurrence of anomalies in WSN using the HMLT for a given dataset. Extensive comparative analysis is performed to analyze the detection capability of the designed approach along with the conventional model. The resulting outcome defines that the proposed approach performs greater in detecting the anomaly than other standard modelsAt present, the Wireless Sensor Network (WSN) plays a pivotal role in the wireless communication system that works based on a large number of sensor nodes. The development of the WSN has become more popular and the nature of versatility resulted in more security concerns and making it hard for the investigation to prevent the anomaly in it. One of the essential and challenging tasks in WSN is the security concern. Detecting the anomaly present in the network becomes the major challenge to ensure the security of WSN. In general, WSNs are affected by a different type of threats that tends the nodes to get damaged and form the wrong determination. Therefore, it is necessary to identify anomalous to minimize the false alarm. Moreover, the quality of the data gathered by the sensor nodes is mainly affected by the anomalies that are produced because of different reasons like reading errors, malicious attacks, failures, and unusual events. Hence it is significant to process the anomaly detection to ensure the quality of the sensor data before it is used to make decisions. In WSN, anomaly detection is the significant process to determine the anomaly or unusual event. However, timely anomaly identification is more complex to function to execute reliably in real-time. For the secure and reliable operation in the WSN, effective anomaly detection is more necessary. However, the standard anomaly detection techniques often fail to adequately secure the privacy of the data and identify the complex, particular, and unique breaches in the WSN. Also, the present anomaly detection models only process under the stationary environment and need to keep all the training data in the node. To address these limitations, a novel Hybrid Machine Learning Technique (HMLT) is introduced to effectively detect the presence of anomalies in WSN. The advanced hybrid technique is designed to enhance the detection performance and safeguard privacy. Initially, the required data from the WSN is collected from the available data resource. Further, the significant feature from the raw data is selected using the optimal feature selection process. Here the Secretary Bird Optimization Algorithm (SBOA) is used to achieve the optimal feature selection. Finally, the HMLT is implemented to perform the detection task, in which the hybrid classifier is the combination of the Deep Belief Network (DBN) along with the Bayesian Learning (BL). The model is specifically developed to identify the occurrence of anomalies in WSN using the HMLT for a given dataset. Extensive comparative analysis is performed to analyze the detection capability of the designed approach along with the conventional model. The resulting outcome defines that the proposed approach performs greater in detecting the anomaly than other standard models
An improved image steganography scheme based on deep learningapproach and quadruple security layers
This thesis intends a fresh approach for the improvement of dataset secreting in images via the ACO algorithm. Digital image steganography requires a balance between two essential goals: this provides the maximum optimization of the concealment of data; it would also reduce the possibility of the image from existence noticed via optimizing the quality of the original image to be concealed. Many current steganographic methods tend to sacrifice the goals above and below at some point. The thesis introduces the "ACO-LSB" approach, which is designed to enhance embedding capacity using a gray-scale shelter image to hold secret dataset through adding an extra bit-pair in byte (b) to make a checksum of the integrity of the image or a check sum of the hidden message. The method encrypts secret information as the pairs of bits and embeds into the uncompressed images in grey scale. The algorithm used in the ACO is the adaptive scanning to find pixel locations and increase the data embedding capacity while decreasing the strong impact on the image quality. Otherwise, the specific pheromone values are changed in a cyclical fashion to avoid problems with stagnation in the context of the overall optimization process – the values should be ideal for proper selection of pixels. The performance results of the ACO-LSB method are outstanding and this research confirms that they enabled enhancement in the subsequent image embodiment, with up to a 30% increase in embedding capacity compared to traditional methods. Technology achieves an average maximum Peak Signal-to-Noise Ratio (PSNR) of (40.5) dB and Structural Similarity Index (SSIM) of (0.98). Furthermore, Methodology shows strong resistance to detection, reducing detection rates by 20%. The model was implemented using MATLAB R2023a and tested on a publicly available dataset of 1000 gray-scale pictures, providing strong evidence of its effectiveness.
Geliştirilmiş bir ağ girişim tespit sistemi için ağ trafik özelliklerinin ana bilgisayar trafik özellikleriyle birleştirilmesi
Network security is a key concern in today's linked world as cyber threats grow more sophisticated and ubiquitous. Traditional Network Intrusion Detection Systems (NIDS) generally fall short owing to their dependence on predetermined signatures and restricted detection scope, exposing substantial gaps in efficiently recognizing new and unanticipated intrusions. This research tackles these difficulties by merging network and host traffic data with sophisticated deep learning algorithms to boost NIDS performance. Utilizing the Network Intrusion Detection dataset, which comprises multiple intrusion scenarios replicated in a military network context, our technique involves painstaking data collection, preprocessing, and feature extraction. We employed a convolutional neural network (CNN) to assess these data, applying rigorous feature selection and dimensionality reduction to enhance model performance. The findings reveal that our deep learning-based NIDS achieves an amazing detection accuracy of 98.5%, exceeding current approaches and successfully resolving real-world cybersecurity problems. This complete approach not only develops NIDS technology but also provides a practical solution for boosting network security across many applications, therefore contributing to the development of intrusion detection systems.
A smart monitoring network for hybrid energy system by using iot
This thesis addresses the design, development, and implementation of a smart monitoring network for hybrid renewable energy systems using Internet of Things (IoT) technologies. The research aims to improve the efficiency, reliability, and performance of renewable energy integration through real-time monitoring and control. This thesis examines the design and implementation of a hybrid renewable energy system which integrates wind turbine and photovoltaic (PV) technologies to satisfy the growing demand for green energy. The approach is multidisciplinary, involving engineering, renewable energy systems, and information technology to design, simulate, and analyze performance of the hybrid system. Background, motivation for renewables integration and objectives of the study are presented in the introduction. To lay the ground for the next the research projects, the equally overview of the In order to build the foundation for the next research projects, a comprehensive overview of the literature is provided, including previous research on smart monitoring networks, hybrid energy systems, and Internet of Things-based energy management solutions. To prepare ground for the forthcoming research, a survey of the literature also incorporates reviewed works of smart monitoring networks, hybrid energy systems and IoT-based energy management solutions. Parameter extraction, Code Generation in MATLAB, and performance estimation Issue are widely described in the chapters of modeling and simulation of PV and WT systems. The hardware development chapter shows the design, and integration of sensors, converters, and control system constituents to effectively construct a network for the real-time monitoring of the hybrid energy system. The system's performance is thoroughly examined in the results chapters, which make use of visualizations from cloud platforms, smartphone application interfaces, and real-time monitoring data. The chapters offer insights into operational dynamics, system efficiency, and patterns of energy generation through in-depth analysis and visualization. In the practical implementation phase, hardware components are deployed to collect real-time data from the hybrid energy system. IoT-based communication protocols enable remote monitoring and control, facilitating optimization of energy generation, storage, and utilization. The research demonstrates the scalability and reliability of the monitoring network in improving the performance of hybrid renewable energy systems. In summary, by providing an extensive foundation to the design, implementation, and assessment of hybrid energy systems, this thesis adds to the rapidly expanding subject of renewable energy. The goal of the research is to promote an environmentally friendly and more resilient energy future by expediting the implementation of sustainable energy solutions by bridging the theory-practice divide.
Towards cleaner environments: A study of deep learning models for plastic bottle detection
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%.
Makı̇ne öğrenme teknı̇klerı̇ ı̇le ses tanıma: Lı̇teratür taraması
Bu çalışmada, ses tanıma alanında kullanılan makine öğrenme yöntemleriyle ilgili literatür taraması yapılarak, farklı makine öğrenme yöntemlerini karşılaştırmak ve en etkili yöntemi belirlemek amaçlanmıştır. Çalışma kapsamında, Ocak 2023-Mart 2025 tarihleri arasında yayınlanan güncel literatür taranarak 30 çalışma detaylı olarak incelenmiştir. Çalışmada, derin öğrenme tabanlı yaklaşımların, özellikle Transformer mimarileri ve uçtan uca öğrenme modellerinin, geleneksel yöntemlere kıyasla daha yüksek doğruluk ve sağlamlık sergilediği sonucuna varılmıştır. Bununla birlikte, ideal bir ses tanıma sistemi için tek bir "en iyi" yaklaşım yerine, uygulama senaryosuna, mevcut kaynaklara ve hedef kullanıcı grubuna bağlı olarak farklı yaklaşımların kombinasyonunun daha uygun olabileceği değerlendirilmiştir. Ses tanıma sistemlerinin gelişiminde kaydedilen önemli ilerlemelere rağmen, büyük miktarda etiketli veri ihtiyacı, gürültülü ortamlardaki performans düşüşleri gibi çeşitli problemler ve sınırlamalar hala mevcuttur. Gelecekteki araştırmalar için düşük kaynaklı diller için yarı-denetimli ve öz-denetimli öğrenme yaklaşımları, hibrit model mimarileri, çok görevli ve çok modlu öğrenme yaklaşımları, nöromorfolojik hesaplama yaklaşımları ve standartlaştırılmış değerlendirme metrikleri üzerine çalışmalar önerilmiştir.
Hızlandırılmış dağıtımlı enerji yönetim sistemi ((ADEMS) akıllı şebekeler için yeni bir mimari
Smart Grids have emerged as an effective solution for the integration of renewable energy sources and improving the efficiency and reliability of energy distribution. This paper presents the design and implementation of an Accelerated Distributed Energy Management System (ADEMS) model for Smart Grids. The ADEMS model, which integrates Advanced Metering Infrastructure (AMI), high-speed LiFi and fiber optic communication systems, an RNN-based load forecasting model, and an efficient Energy Management System (EMS), offers superior performance in terms of load forecasting accuracy, system response time, cost efficiency, and voltage stability. The integration of these advanced technologies and systems allows for real-time monitoring and control of the grid, thus enabling an optimal and responsive energy management. The model's effectiveness was validated through various scenarios, and it demonstrated promising results, outperforming other models in the literature. The paper concludes with potential areas for further research and improvements
NASA promise veri setlerinde derin ve makine öğrenme modellerinin yazılım hata tahmini performansının izlenmesi
In an era where software reliability and quality assurance have gained paramount importance, this study employs advanced machine learning models to predict software defects, thereby contributing to a refined understanding of their potential applications in enhancing software reliability. The focal point of the investigation is the PROMISE20 dataset, a collection of data from various NASA software projects. This dataset is segmented into three sub-datasets (CM1, JM1, KC1), with each instance marked by a binary dependent variable (indicating defect status) and independent variables based on Halstead and McCabe static code metrics. The study undertakes a comparative analysis between deep learning models, specifically LSTM and LSTM-GRU, and traditional machine learning models such as the XGBoost Classifier. Their proficiency in predicting software defects is gauged by assessing their accuracy and F1-scores. Upon examination, LSTM and LSTM-GRU deep learning models outperform with superior predictive performance, demonstrating accuracy rates of 88% and F1-scores of 0.89 and 0.90, respectively. In the realm of traditional machine learning, the XGBoost Classifier emerged as the top performer, boasting an accuracy rate of 0.88. However, the findings also underscore the need for further exploration. The study points to the necessity of examining additional datasets, exploring diverse models, optimizing model hyperparameters, and enhancing model interpretability to ascertain the optimal choice of model for software defect prediction. This research enriches the ongoing discourse in software reliability and defect prediction, offering a robust foundation for future investigations in the field of software defect prediction using machine learning.
Yenilenebilir enerji akıllı şebeke nesnelerin internetini tabanlı yönetim ve izleme sistemi tasarımı
Renewable energy sources are receiving more attention as a result of the population's quickening increase and growing worry over global warming. By lowering carbon emissions and generating cheap power, renewable energy sources significantly improve the environment. In comparison to other renewable energy sources, solar energy has lower operating and maintenance expenses and is thus the most widely used renewable energy source. Managing and regulating energy flow in the smart grid is one of the major issues that have to be resolved. Problems with power quality and stability may arise whenever there is a dynamic exchange of energy in a high-power system between different sources, loads, and energy storage devices. Energy flow through the system must be continuously managed in order to satisfy the load demand. The biggest problem with the smart grid's operation is the lack of technical details on the hardware and experimental setup of the energy storage system. This study assesses the power balance in a small-scale experimental SG under various conditions. To achieve Maximum PowerPoint Tracking (MPPT), the PV system efficiency in this work progressively uses artificial intelligence-based techniques. Additionally, this work involves wind turbine MPPT. By employing the Grey Wolf Optimizer GWO algorithm, which is based on the artificial intelligent approach used to achieve MPPT, to optimize the efficiency of the PV-wind turbine-battery system, the goal of this study is to enhance the power quality and energy management system of the PV-wind turbine-battery module. In order to strengthen system dependability, the IoT may also be coupled with the smart grid. In this scenario, the IoT can control load demand if the load increases more than the renewable energy sources over an extended length of time.