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detection and Mitigation of Vampire Attacks in Wireless Sensor Networks using Weighted Hopfield Neural Network with Nature Inspired Optimal Path Selection

Detection and Mitigation of Vampire Attacks in Wireless Sens...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S. Famila Shanmugasundaram Hariharan N. Sujaudeen Gopu Sowjanya d. Annal Priyadarshini T. ramala Department of Electronics & Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chenni Tamil Nadu Dept of AIDS Vardhaman College of Engineering Hyderbad India Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of CSE(Data Science) CMR Technical Campus Hyderabad Telangana India Department of Computer Science and Engineering (AIML) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of science and technology Chennai Tamil Nadu

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Wireless sensor networks (WSNs) consist of several nodes whose primary objectives are to monitor and regulate surroundings. Furthermore, sensor nodes are distributed in accordance with network utilization. The energy consumption of sensor nodes is one of the most important problems in this kind of network. In fixed-sink networks, nodes in close proximity to the sink serve as intermediaries for transmitting data from other nodes to the sink. This results in a significant decrease in sensor energy usage. Consequently, the network’s lifespan decreases. due to their vulnerabilities, sensor nodes are prone to various attacks, including the vampire attack that poses a danger to WSN. This paper presents a novel approach for the detection and mitigation of vampire attacks using a Weighted Hopfield Neural Network (WHNN) combined with bio-inspired optimal path selection. The WHNN is employed to detect abnormal energy consumption patterns indicative of vampire attacks, leveraging its associative memory capabilities. To counteract the detected attacks, we incorporate a bio-inspired optimization algorithm, such as Aquila optimizer Algorithm (AOA), to dynamically select the most energy-efficient paths for data transmission. This dual-layered strategy not only enhances the detection accuracy of vampire attacks but also ensures optimal path selection. due to their vulnerabilities, sensor nodes are prone to various attacks, including the vampire attack that poses a danger to WSN. The superiority of the WHNN-AOA technique is proved by simulation results, when compared to current schemes, based on performance measures such as packet delivery ratio, average throughput, detection ratio, and network lifespan, while also reducing the average residual energy.

关键词： Wireless sensor networks Energy consumption Accuracy Prevention and mitigation Heuristic algorithms Simulation Scalability Hopfield neural networks Throughput Testing

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Wireless Power Transfer Technologies: State-Of-The-Art and Future Prospects

Wireless Power Transfer Technologies: State-Of-The-Art and F...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Vivek Justus S.Viveka M.Sumithra Purushothaman V V.Samuthira Pandi desidi Narsimha reddy Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Wireless Power Transfer (WPT) technologies represent a transformative approach to energy transmission, offering advantages over traditional wired systems such as convenience, flexibility, and reduced maintenance. This paper presents a comprehensive review of current advancements and future prospects in WPT. The review encompasses three primary technologies: electromagnetic induction, magnetic resonance, and microwave transmission, each offering unique benefits and challenges. recent developments have significantly enhanced WPT efficiency, extended transmission distances, and enabled integration with emerging technologies like Internet of Things (IoT) devices and electric vehicle (EV) charging infrastructure. Looking forward, the paper identifies several key research directions and challenges. These include improving efficiency through advanced materials and metamaterials, standardization efforts to promote interoperability and safety, and exploration of new applications such as wireless charging in smart cities and space-based applications. The potential impact of WPT on reducing reliance on fossil fuels and enabling sustainable energy practices underscores its importance in future energy landscapes. By synthesizing current knowledge and highlighting future opportunities, this study aims to guide researchers, engineers, and policymakers in accelerating the development and adoption of WPT technologies, paving the way for a moreefficient and interconnected energy ecosystem.

关键词： Costs reviews Smart cities Magnetic resonance Wireless power transfer Standardization Telecommunication computing Smart grids Internet of Things Tuning

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Smartphone-based Strep Throat detection using MTCNN: A deep Learning Approach for Non-Invasive diagnosis

Smartphone-based Strep Throat Detection using MTCNN: A Deep ...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： G. M. Sathyaseelan L. Sankara Maheswari S. Sophiya M. Malini S. P. Santhoshkumar d. Vikram Department of Information Technology JKK Natraja College of Engineering and Technology Kumarapalayam India Department of Information Technology Sri G.V.G Visalakshi College for Women Udumalpet India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This research proposes a novel approach for strep throat detection utilizing the Multi-Task Cascaded Convolutional Neural Network (MTCNN) algorithm and a smartphone. By incorporating advanced facial detection techniques, MTCNN enhances the accuracy of throat image analysis. The proposed method involves capturing throat images using a smartphone camera with an added device to reduce light reflection. MTCNN is then applied to extract relevant information from the images, enabling a classification model to distinguish between healthy and strep-infected throats. Experimental results demonstrate the effectiveness of this approach, providing a non-invasive and efficient method for strep throat diagnosis. The integration of MTCNN holds significant potential for smartphone-based healthcare applications due to its high accuracy and enhanced feature extraction capabilities.

关键词： deep learning Accuracy Sensitivity Image analysis Medical services Multitasking Feature extraction reflection Face detection Convolutional neural networks

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deep Learning-Based Ensemble Mounting for Comprehensible Cancer Identification

Deep Learning-Based Ensemble Mounting for Comprehensible Can...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： U. Hemavathi Vijaya Prabhu r Kalaiyarasi r K. Sivasankari r. Karthika M. Madiajagan Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai TamilNadu India Sri Manakula Vinayagar Engineering College Puducherry Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai Department of ECE Sri Sai Ram Intitute of Technology Chennai Department of EEE Theni Kammavar Sangam College of Technology Theni Department of Analytics VIT University Vellore

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

A lesion is a region of skin that has developed abnormally in comparison to other skin portions. There are seven categories in the 2018 injury dataset from the International Standards Institute. There is a reduced version of the dataset with just two classifications (malignant and benign). Benign tumors are not cancerous, but malignant ones are. Malignant tumors may spread throughout the body and multiply more quickly. In order to save patients' lives, skin cancer wounds must be identified quickly. deep understanding and synthetic learning models greatly simplify the process of evaluating skin lesions. Accuracy has previously been impacted by imbalanced datasets and image occlusion. This paper proposes a stack of interpretable artificial intelligence systems and deep learning techniques for the non-invasive diagnosis of melanoma skin cancer. The dataset used to create the classification model that differentiates between benign and malignant skin tags has an equal number of photos. The first machine learning model is trained using features that are manually produced. You learned how to use training set cross-validation to layer Level 1 models based on the expected results of these foundational frameworks. A deep learning system that has been pre-trained on image network data was used for transfer learning. We evaluated the classifier for each model. Subsequently, the models developed in an ensemble setting were evaluated using many model combinations. Furthermore, interpretability techniques have been developed that use heatmaps with well-formed adaptive descriptions to choose image regions that most accurately depict the condition. This enables dermatologists to understand the model’s results. The optimal model for classifying skin lesions was found by looking at the receiver operating curve (rOC curve), preciseness, the original Cohen, F1 score, doubt’s matrix, and other variables.

关键词： deep learning Training Adaptation models Computational modeling Transfer learning Melanoma Wounds Skin Telecommunication computing Lesions

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An improved energy management model for IoT devices using deep learning

An improved energy management model for IoT devices using de...

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International Conference on Computing and Networking technology (ICCNT)

作者： P. roy Sudha reetha S Joseph N. Pandeeswari r Anitha S. Jebarose Juliyana Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul Dt Tamil Nadu India Department of Electronics and Communication Christian College of Engineering and Technology Dindigul Dt Tamil Nadu India Department of Information Technology PSNA College of Engineering and Technology Dindigul Dt Tamil Nadu India Department of Artificial intelligence and Data Science Dr. Mahalingam college of engineering and Technology Pollachi Tamil Nadu India Department of Mechanical Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai Tamil Ndu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The Internet of Things (IoT) is booming, with research showing that billions of connected devices gather data, communicate, and automate. The widespread adoption of IoT devices has been hampered by one major challenge: limited energy resources that require recharging or replacing often. Energy management strategies on these devices are essential to improve battery life. This paper presents an enhanced version of the energy management model for IoT devices using deep learning techniques in technical abstracts. The aim is to reduce energy consumption by predicting and configuring real-time device settings based on user behavior trends. A deep neural network is used as the model to learn and tailor its response variably based on different user conditions like temperature, light, and usage patterns. The model can analyze this data to predict energy demand and programmatically adjust device settings.

关键词： deep learning Energy consumption Adaptation models runtime Computational modeling Predictive models Software real-time systems Internet of Things Energy management

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distributed Aperture Coherent Synthesis Method based on Two-stage Compensation

Distributed Aperture Coherent Synthesis Method based on Two-...

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Signal, Information and data Processing (ICSIdP), IEEE International Conference on

作者： Zhongwei Liu dongming Zhou rongrong Xiang Jie Chen Tianhang Wu Tianyu ding Xiaochuan Liu Hongwei Gao School of Electronic and Information Engineering Beihang University Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Department of Data Technology and Application Beijing Institute of Computer Technology and Application Beijing China Radar R&D Center Beijing Institute of Radio Measurement Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

To solve the problem that it is difficult to accurately estimate the coherent parameters of distributed aperture radar under the condition of low signal-to-noise ratio, a distributed coherent synthesis method based on two-stage compensation is proposed in this paper. This method utilizes prior information such as system layout, beam pointing, and inertial navigation station location to perform coarse alignment of echoes at the first stage in time, and then uses cross-correlation method to estimate coherent parameters of delay and phase within a small range, and perform precise compensation of time and phase at the second stage, so as to realize the coherent synthesis of multiple radar echoes. Experimental results show that the proposed method can effectively realize distributed coherent synthesis processing under low signal-to-noise ratio.

关键词： Accuracy Layout radar Inertial navigation Apertures data processing radar signal processing delays Signal to noise ratio

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Predicting and Analysing Lumpy Skin disease Using Ensemble of Machine Learning Models

Predicting and Analysing Lumpy Skin Disease Using Ensemble o...

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Information Technologies and Communications (GCITC), Global Conference on

作者： E. Chandralekha Jemin V.M T. Anitha V. deepa V. Usha ravikumar S Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of CSE R.M.K College of Engineering Chennai India Department of Artificial Intelligence and Data Science Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Artifical Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

The economic impact of the infectious bovine virus known as Lumpy Skin disease (LSd) is substantial. Because of the poxvirus that causes LSd, cattle suffer from decreased meat and milk output, higher veterinary bills, and trade restrictions on cattle-related products. The biggest difficulty in managing LSd epidemics is identifying them at an early stage. Because they are laborious and time-consuming, traditional procedures result in delayed responses. Our research paper provides a comprehensive evaluation of ensemble machine learning models for LSd prediction to help overcome these obstacles. Machine learning models have been shown to be valuable in the field of epidemiology due to their ability to successfully analyse complex data. In this research, a number of ensemble models that have been pre-trained on parameters including location, climate, and disease outbreak history. We also use data preparation methods to equalize data and standardize inputs, for a more reliable study. In order to better understand the patterns of LSd spread, data visualization tools like heatmaps and box plots are used. Our studies' overarching goal is to improve disease management measures by making use of ensemble machine learning models and data analysis strategies, which will have positive effects on cattle health and the economic security of the livestock industry.

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A Novel Approach to Identify dynamic deficiency in Cell using Gaussian NB Classifier 7

A Novel Approach to Identify Dynamic Deficiency in Cell usin...

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Pushpakumar, r. Prabu, r. Priscilla, M. renisha, P.S. Prabu, r. Thandaiah Muthuraman, U. Department of IT Veltech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering Department of It Chennai India S.A. Engineering College Department of Ece Chennai India Srinivas University College of Engineering and Technology Department of Cloud Technology and Data Science Karnataka Mukka Mangalore India Simats Saveetha School of Engineering Department of Ece Chennai India Francis Xavier Engineering College Dept of Eee Tirunelveli India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

For the identification of intra-cell faults that may arise in returned products, a novel learning-guided technique has been described in this study. Assumed static flaws described by stuck-at errors are used in the first half of the study. Many supervised learning methods are investigated, with varied degrees of efficiency. The second section of the study is built on the earlier research by addressing more complex dynamic flaws. As part of the diagnostic procedure, a Naive Bayesian classification algorithm is applied to identify and categorise the type of each new data fault. The suggested approach's reliability and clarity have been shown via testing on standard circuits and compared with a conventional cell-aware diagnostic tool. © 2022 IEEE.

关键词： Machine learning

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Weakly-supervised multi-granularity map learning for vision-and-language navigation 22

Weakly-supervised multi-granularity map learning for vision-...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Peihao Chen dongyu Ji Kunyang Lin runhao Zeng Thomas H. Li Mingkui Tan Chuang Gan South China University of Technology and Information Technology R&D Innovation Center of Peking University South China University of Technology South China University of Technology and Pazhou Laboratory Shenzhen University Information Technology R&D Innovation Center of Peking University South China University of Technology and Key Laboratory of Big Data and Intelligent Robot Ministry of Education MIT-IBM Watson AI Lab and UMass Amherst

ISBN: (纸本)9781713871088

We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. The instructions often contain descriptions of objects in the environment. To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location and the semantic information of the environment objects. However, enabling a robot to build a map that well represents the environment is extremely challenging as the environment often involves diverse objects with various attributes. In this paper, we propose a multi-granularity map, which contains both object fine-grained details (e.g., color, texture) and semantic classes, to represent objects more comprehensively. Moreover, we propose a weakly-supervised auxiliary task, which requires the agent to localize instruction-relevant objects on the map. Through this task, the agent not only learns to localize the instruction-relevant objects for navigation but also is encouraged to learn a better map representation that reveals object information. We then feed the learned map and instruction to a waypoint predictor to determine the next navigation goal. Experimental results show our method outperforms the state-of-the-art by 4.0% and 4.6% w.r.t. success rate both in seen and unseen environments, respectively on VLN-CE dataset.

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Advancements in Artificial Intelligence for Better Image Analysis, recognition, and Interpretation in the Field of Image Processing

Advancements in Artificial Intelligence for Better Image Ana...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： V. Saravanan A. Adaikkammai G. Sasikala Iyswariya A A. Babisha M. Latha Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science and Business Systems R.M.D. Engineering College Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Artificial Intelligence and Data Science Panimalar Engineering College Department of IT SRM Institute of Science and Technology

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Industries Image recognition Image analysis Transformers Satellite navigation systems Generative adversarial networks Satellite images Convolutional neural networks Security Artificial intelligence

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