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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sivagami, S. Gopinath, M. Yuktha Varshika, J. Srivarshinie, V. Monikaa, R. Senthil, K. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Computer Science And Engineering Tamil Nadu Chennai602105 India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Information Technology Tamil Nadu Chennai602105 India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Artificial Intelligence And Machine Learning Tamil Nadu Chennai602105 India Easwari Engineering College Department Of Computer Science And Engineering Tamil Nadu Chennai600089 India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

Retinal vessel segmentation plays a vital role in the analysis of ophthalmic imaging. Binary Segmentation is one type of segmentation that segments the retinal vessel and blood vessel in retinal images. Retinal vascular geometry reflects a patient's state of health and aids in the diagnosis of certain conditions, including hypertension and diabetes. Early detection of these diseases helps to prevent blindness of patients. Machine Learning and Deep Learning are two important sectors in Artificial Intelligence. Nowadays, Deep learning algorithms are very much used in medical Image processing. These Deep Learning Algorithms are more accurate and efficient than manual segmentation and other computer-aided diagnosis methods. Especially Convolutional Neural Networks (CNNs) in Deep Learning Architecture are used for the segmentation process. CNN architecture can handle the complex characteristics of retinal images. Traditional Data augmentation techniques are used to enlarge the training and testing samples. Matched filter methods are used to remove the noise and Morphological operations are implemented to detect the abnormal regions in the retinal image. Our proposed model achieves an accuracy of 97.87%, precision of 96.8%, Intersection over Union of 97.1%, and specificity of 0.5% in the segmentation process. The proposed CNN architecture not only expedites the segmentation process but it makes advancements in clinical decisions in the field of ophthalmology. © 2024 IEEE.

关键词： Matched filters

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Privacy‐preserving remote sensing images recognition based on limited visual cryptography

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1166-1177页

作者： Denghui Zhang Muhammad Shafiq Liguo Wang Gautam Srivastava Shoulin Yin Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhouChina Department of New Networks Peng Cheng LaboratoryShenzhenChina College of Information and Communications Engineering Dalian Minzu UniversityDalianChina Department of Mathematics and Computer Science Brandon UniversityBrandonManitobaCanada Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Math Lebanese American UniversityBeirutLebanon College of Information and Communications Engineering Harbin Engineering UniversityHarbinChina

With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing *** fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image ***,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing *** is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational *** Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted *** stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in *** this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is *** diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is *** fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing *** experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

关键词： activity recognition feature extraction image classification KNN privacy protection remote monitoring remote sensing

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Mechanism for Enhancement of Functionality in Deep Echo State Network by Optimizing Leaking Rate 3

Mechanism for Enhancement of Functionality in Deep Echo Stat...

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3rd International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

作者： Inoue, Shuichi Nobukawa, Sou Nishimura, Haruhiko Watanabe, Eiji Isokawa, Teijiro Graduate School of Information and Computer Science Chiba Institute of Technology Narashino Japan Yamato University Faculty of Informatics Osaka Japan National Institute for Basic Biology Okazaki Japan Graduate School of Engineering University of Hyogo Himeji Japan

ISBN: (纸本)9798350300604

Deep echo state network (Deep-ESN) model consists of multiple reservoir layers, that can respond to layer-specific different time-scales. This dynamical characteristic leads to enhance performance of ESN. However, neither the design guidelines for the hyperparameters of the network and individual neurons nor the mechanism to produce the diverse dynamical response have been clarified. In this study, we proposed an approach to generate the dynamical responses with different time-scales in each layer by adjusting the leaking rate of neurons. Through the evaluations time-series prediction task for different leaking rates, multiscale entropy analysis for each reservoir layer, and cross-correlation between adjacent layers, we found that when the leaking rate is set to low, the layer-specific dynamics with different time-scales are generated, as well as a mechanism whereby the signal propagates to subsequent layers with a delay, i.e., queue characteristic. These characteristics produce a high memory capacity. Consequently, diverse responses with delay leads to an enhancement of Deep-ESN functionality. © 2023 IEEE.

关键词： Deep learning

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DeepLymphoDetect: Leveraging Deep Learning Techniques for Acute Lymphoblastic Leukemia Detection in Blood Cells 1

DeepLymphoDetect: Leveraging Deep Learning Techniques for Ac...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Senthil, K. Monikaa, R. Gopinath, M. Vishwa, S. Yuktha Varshika, J. Sivagami, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Easwari Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600089 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Information Technology Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai602105 India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377002

Leukemia is a cancer that affects the bone marrow and blood, causing an excess of aberrant white blood cells (WBC). It can be affected by various factors such as Genetic mutations, Chemical exposure, and Immune system disorders. One kind of leukemia is acute lymphoblastic leukemia (ALL) that rapidly progressing and potentially life-threatening hematologic malignancy that predominantly affects children. This research employs a Convolutional Neural Network (CNN) architecture to propose an automated method for diagnosing and detecting acute lymphoblastic leukemia (ALL). For this research, Lymphocyte images are used as the input. The quantity of input samples is increased using a conventional data augmentation methodology, and correct findings are obtained by using a cross-validation method. This research achieves a 98.32% accuracy, a precision of 98.76%, and 97.38% recall for detection and classification tasks. This research exceeds the performance metrics of other cutting-edge methods. © 2024 IEEE.

关键词： Lung cancer

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Advanced Deep Reinforcement Learning Strategies for Enhanced Autonomous Vehicle Navigation Systems 2

Advanced Deep Reinforcement Learning Strategies for Enhanced...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Dhinakaran, M. Rajasekaran, R.Thalapathi Balaji, V. Aarthi, V. Ambika, S. Galgotias College of Engineering & Technology Knowledge Park Ii Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India Grt Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruttani India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India

ISBN: (纸本)9798350387933

The objective of this research is to investigate more sophisticated techniques in Deep Reinforcement Learning (DRL) for the purpose of improving navigation systems for autonomous vehicles. The results of our research indicate that the success rates of DRL algorithms are encouraging: DQN (75.2%), PPO (82.6%), and TRPO (78.1%). The success of navigation is highly influenced by optimisation factors such as parameter tuning (87.4%), exploration-exploitation balance (85.1%), and reward shaping (88.9%). The generalisation score for transfer learning is rather good (92.3%), while the safety modules are designed to make the system safer. These findings highlight the significance of algorithm selection, parameter optimisation, and advanced techniques in the process of optimising navigation for autonomous vehicles, hence establishing essential benchmarks for future developments. © 2024 IEEE.

关键词： Navigation systems

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Exploring Reinforcement Learning Algorithms for Autonomous Agents in Dynamic Environments

Exploring Reinforcement Learning Algorithms for Autonomous A...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Kumar, S. Magesh Swaminathan, Subbiah Sugumaran, D. Saranya, N. Naga Ashokkumar, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Information Technology Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Vel Nagar Tamil Nadu Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350388602

Reinforcement Learning (RL) algorithms are widely considered to be the enabling technology for deep learning and allow for agents in complex systems to learn in a dynamic environment. In order to present in this paper a break-through training methodology for learning RL agents in the dynamic environment, advised by the following adaptability, robustness, and scalability purposes. Our approach is assessed in an array of the model environments which are open to the users. This framework is labelled as OpenAI Gym environment. We apply different reinforcement learning algorithms in our experiments among which Covering Concurrent and Differentiable in Q-learning, Meta-learning (MAML), Virtual Imitation and Q-learning, and Dyna-Q are the most popular. Meta-learning's performance highlights its power and efficiency in grasping and optimizing decision-making logic in dynamic situations within 20 epochs and reaching the cumulative reward of 500. As a matter of fact, adaptive search strategies, for instance, UCB are another tool that is extremely useful reaching to 490 at 25 epochs after all. Transfer learning techniques like the Progressive Neural Networks shows extremely high power of learning in collecting a reward of 500 and in less than 20 epochs it also converges. Moreover, scalable parallel training methods such as CPDG which are normalized in distribution, have huge scalability and robustness can be applied for large scale dynamic environments. Consequently, it is concluded that the presented approach was very good in giving RL agents the ability to navigate environments and succeed in dynamic settings, thus contributing to the development of autonomous systems not only in the area of RL but also everywhere else. © 2024 IEEE.

关键词： Adversarial machine learning

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An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

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International Journal of information Technology (Singapore) 2024年第8期16卷 5033-5040页

作者： William, P. Patil, Jaikumar M. Panda, Sunita Venugopal, Anita Vidyullatha, Pellakuri Kumar, Nellore Manoj Jandwani, Aman Department of Information Technology Sanjivani College of Engineering SPPU Department of Computer Science and Engineering Shri Sant Gajanan Maharaj College of Engineering SGBAU Department of Electrical Electronics and Communication Engineering GITAM School of Technology IT Unit Dhofar University Department: Computer Science and Engineering Koneru Lakshmaiah Education Foundation Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Research Task

Nation’s rapid urbanization growth and economic development, waste generation has significantly increased. An important environmental concern on a global scale is waste management. In a majority of the world’s nations, including India’s metropolitan centers, organic waste management is a major problem. Therefore, it is necessary to create a productive system that will either eliminate or significantly decrease this issue. It will assist us in effectively maintaining a hygienic environment, and natural atmosphere. The implementation of smart cities requires an effective garbage collection system. In the given paper, we propose a smart waste collection and management (SWCM) system centered on the Internet of Things (IoT) architecture. A deep learning (DL) approach for classifying waste is being used. The proposed framework includes the use of smart bins for the collection of garbage, the categorization of that waste into several groups, and the management of smart waste. For this study, data from smart garbage cans were gathered. The data was normalized during preprocessing. The efficacy of the proposed system is assessed and compared with conventional as well as existing recent studies based on performance metrics such as classification accuracy (%) and power consumption (%). The results of the study reveal that the proposed approach is impactful in the smart collection and management of waste. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： And hygienic environment Deep learning Internet of things (IoT) Smart cities Smart waste

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Neural Networks-Based Predictive Models for Self-Healing in Cloud Computing Environments 2

Neural Networks-Based Predictive Models for Self-Healing in ...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sujatha, D. Michael Raj, T.F. Ramesh, G. Agoramoorthy, Moorthy Ahamed Ali, S. St.Peters College of Engineering and Technology Department of Computer Science and Engineering Avadi Chennai India Sclas Smats Department of Computer Applications TamilNadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Easwari Engineering College Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798350306415

Due to the widespread adoption of cloud computing, protecting the security and uptime of cloud services is of crucial importance. Unforeseen malfunctions and performance bottlenecks in cloud settings can lead to considerable downtime and financial losses. Systems that can automatically detect, diagnose, and recover from errors using self-healing mechanisms are a promising new approach. This study investigates how neural network-based prediction models can be used to improve cloud-based systems' inherent capacity for self-healing. This research introduces a novel method of using neural networks to forecast cloud system failures and performance irregularities. The proposed prediction models are able to spot warning signs by assessing past data and current metrics in real time. Neural networks may accurately anticipate outcomes by recognizing complicated patterns and learning from large, varied information. Various forms of neural networks, including deep learning designs, are evaluated to determine their efficacy in diverse contexts. The study also looks into proactively reducing faults by incorporating these predictive models into self-healing systems. Responses, such as resource reallocation, workload migration, and backup procedures, are triggered automatically when problems are predicted. This preventative method drastically lessens the amount of time users spend waiting for their cloud-based systems to restart. Extensive simulations and real-world case studies yielded experimental data that prove the prediction models based on neural networks proposed for self-healing in cloud computing are effective. The models' proficiency in failure prediction enables corrective measures to be taken promptly and precisely. Implementing these predictive models in massive cloud environments is also discussed, as are their scalability, flexibility, and real-world consequences. by tapping into the potential of neural networks for predictive analysis, this study adds to the develop

关键词： Cloud Computing LSTM Neural Network Self-Healing

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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IoT Based Alcohol Detection and Vehicle Control System 5

IoT Based Alcohol Detection and Vehicle Control System

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Sapthami, I. Raju, V. V. Narasimha Vaithianathan, V. Chinnasamy, P. Kumaran, G. Ayyasamy, Ramesh Kumar Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad500043 India Sri Sivasubramaniya Nadar College of Engineering Department of Electronics and Communication Engineering Kalavakkam Chennai603110 India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering TamilNadu Srivilliputtur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Faculty of Information and Communication Technology Kampar Malaysia

ISBN: (纸本)9798350389609

It is of the utmost importance to be aware of safety measures when operating a motor vehicle in the contemporary world, characterized by a fast-paced and hazardous environment. It has become necessary to develop innovative technology in order to improve road safety as a result of the alarmingly high number of incidents that can be linked to drivers who are under the influence of alcohol. As a solution to this significant problem, this study uses the capabilities offered by the Internet of Things (IoT) to develop an integrated system for detecting and controlling alcohol in commercial vehicles. This study aims to reduce the dangers connected with driving under the influence of alcohol by allowing for the seamless integration of cutting-edge alcohol detection technologies with real-time vehicle control mechanisms. The system makes use of specific sensors for the detection of alcohol. It interacts with the control mechanisms of the vehicle in order to restrict its functioning when alcohol is present. To make this possible, the hardware component of the system mounted in the vehicle uses the MQ3 sensor, also known as the alcohol sensor. This sensor is responsible for monitoring the alcohol molecules in the air around the driver to determine whether or not the driver is intoxicated. A buzzer, a DC motor, an LCD, a GPS, and a GSM are all components utilized by the system for data transfer. Additionally, the gear is powered by a 12-volt battery. As soon as the system determines that there is alcohol present in the vehicle, it immediately locks the accelerator. Using the GPS, the driver can obtain their location coordinates. © 2024 IEEE.

关键词： Highway accidents

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