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International Conference on Computational systems and Communications (ICCsC)

作者： Anagha Acharya Ramya Ramesh Tasmiya Fathima Trisha Lakhani selva Kumar s Department of Computer science and Engineering B. M. S. College of Engineering Bengaluru India

The expectant mother experiences considerable anatomical and physiological changes during pregnancy that induce stress, and an acute response to stress induces a state of anxiety. When this stress is prolonged, depression may develop insidiously, impairing one's ability to cope with stress. Around the world, up to 15% of new mothers may experience postpartum depression, a mental condition. It has been linked to poor maternal and newborn bonding, reduced breastfeeding initiation, and poor mental health outcomes for mothers, children, and infants. Even though they are common, up to 50% of postpartum issues may go undiagnosed or untreated. Therefore, it is imperative to detect symptoms in the early stages for prevention and intervention. In this paper, we review the existing techniques for PPD detection and prevention and cite their merits and demerits. The secondary questionnaire-based screening tools can only act after the onset of depression but before the development of the disorder, while primary prevention approaches, which use powerful machine learning algorithms, can work before the emergence of symptoms themselves with a higher accuracy. The most accurate classifier has been proven to be support vector Machine in the vast majority of instances. Recently, Electroencephalogram (EEG) has received the most research attention because of its superior features in measuring cognitive abilities in real-time. Therefore, adopting EEG-based methodologies could help address inconsistencies caused by earlier approaches. Accordingly, the review suggests exploring the use of EEG for improving the detection of postpartum depression to account for early interventions.

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Decentralized and Distributed Control strategies for Microgrids: A Review of Key Techniques and Applications

Decentralized and Distributed Control Strategies for Microgr...

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IEEE students' Conference on Electrical, Electronics and computer science (sCEECs)

作者： sarathkumar D P. Maheswari Murugesan Manivel T. Jayakumar R. sivakumar b.K. Parrthipan Department of Electrical and Electronics Engineering Kongu Engineering College Tamil Nadu India Department of Electrical and Electronics Engineering Excel Engineering College Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronics Engineering Nandha Engineering College Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B Engineering College Tamil Nadu India Department of Mechanical Engineering Kamaraj College of Engineering and Technology Vellakulam Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Microgrids have emerged as a key solution for enhancing the flexibility, reliability, and sustainability of power systems. As the penetration of renewable energy sources and distributed generation increases, effective control strategies are essential for ensuring stable and efficient microgrid operation. This paper presents a comprehensive review of decentralized and distributed control strategies for microgrids, highlighting their significance in enabling scalability, robustness, and resilience. Key techniques, such as primary, secondary, and tertiary control layers, are discussed, with a focus on the transition from centralized to decentralized paradigms. The review explores the application of advanced algorithms like multi-agent systems (MAs), consensus-based control, and artificial intelligence (AI) for optimizing power sharing, voltage regulation, frequency stability, and energy management in islanded and grid-connected modes. Additionally, the paper analyzes the challenges and opportunities in implementing decentralized and distributed control, with real-world case studies and simulation-based insights to demonstrate their effectiveness in various microgrid configurations. Finally, potential future research directions are suggested to address emerging trends such as peer-to-peer energy trading, blockchain-based energy systems, and the integration of electric vehicles (Evs) into microgrid networks.

关键词： Electric potential Reviews Decentralized control Microgrids Power system stability Peer-to-peer computing Power system reliability sustainable development Resilience Multi-agent systems

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smart Energy Management system Using IoT for Real-Time Monitoring and Theft Detection

Smart Energy Management System Using IoT for Real-Time Monit...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Rajermani Thinakaran somasekar Jalari vikram Neerugatti Ravindra Raman Cholla K Nagendra Rao Faculty of Data Science and Information Technology INTI International University Malaysia Department of Computer Science and Engineering Faculty of Engineering and technology Jain (Deemed-to-be University) Bangalore India Department of Computer Science and Engineering S V College of Engineering Tirupati Andhra Pradesh India

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

The internet of things technology has developed almost all the sectors including energy management. In traditional energy management system meters are used to recording the number of units and electricity used but they are unable to provide the real time information or updates or any theft of electricity. by focusing this problem, we have developed a system which is able to overcome all the problems in traditional electric management system. It monitors the energy usage and real time theft detection of electricity as it cost lot of money. In our system we have used a current sensor which continuously checks the electricity flow. The microcontroller measures the actual energy usage as well as expected values. It compares both the values. If a big difference found, then it could mean the theft of electricity. so, the GsM module is used to send the alert to the user hence they can take required action. The Wi-Fi module sends the data over the cloud for data analytics. Also, user can see the real time data from the cloud platform. Hence user can track the energy usage and get timely alerts so that user can be aware of energy consumption or energy theft. The system is developed by setting up hardware parts as well as integration with software. The system can be operated or monitored from remote place, as Wi-Fi module connects the system with the internet. system can detect the unusual activities like theft of electricity by analyzing the data. Using the IoT technology for energy management, it can become cost effective as well as smart solution of energy management to avoid any theft. Hence it can overcome the loss.

关键词： Meters GsM Energy consumption Renewable energy sources Costs Electricity Real-time systems Recording Monitoring Wireless fidelity

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Probability-based Channel Pruning for Depthwise separable Convolutional Networks

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Journal of computer science & Technology 2022年第3期37卷 584-600页

作者： Han-Li Zhao Kai-Jie shi Xiao-Gang Jin Ming-Liang Xu Hui Huang Wang-Long Lu Ying Liu College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China State Key Laboratory of CAD&CG Zhejiang UniversityHangzhou 310058China School of Information Engineering Zhengzhou UniversityZhengzhou 450000China Department of Computer Science Memorial University of NewfoundlandSt.John's A1B 3X5Canada

Channel pruning can reduce memory consumption and running time with least performance damage,and is one of the most important techniques in network ***,existing channel pruning methods mainly focus on the pruning of standard convolutional networks,and they rely intensively on time-consuming fine-tuning to achieve the performance *** this end,we present a novel efficient probability-based channel pruning method for depthwise separable convolutional *** method leverages a new simple yet effective probability-based channel pruning criterion by taking the scaling and shifting factors of batch normalization layers into consideration.A novel shifting factor fusion technique is further developed to improve the performance of the pruned networks without requiring extra time-consuming *** apply the proposed method to five representative deep learning networks,namely MobileNetv1,MobileNetv2,shuffleNetv1,shuffleNetv2,and GhostNet,to demonstrate the efficiency of our pruning *** experimental results and comparisons on publicly available CIFAR10,CIFAR100,and ImageNet datasets validate the feasibility of the proposed method.

关键词： network compression channel pruning depthwise separable convolution batch normalization

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Automating ML Models Using MLOPs

Automating ML Models Using MLOPS

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Advancements in smart, secure and Intelligent Computing (AssIC), International Conference on

作者： N sirisha Ajmeera Kiran Mohammad Arshad Mounika M Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of IT Deccan college of Engineering and Technology Department of AI & DS B V Raju Institute of Technology

Machine learning engineering relies on MLOps, a collection of best practises for commercializing models. As fresh data is added, machine learning models can become less effective. ML models can't use all data. To do this, we must reimagine the model by changing the front and back ends. Our project regularly tracks and trains models to overcome this constraint. MLOps (Machine Learning Operations) can help. ML pipeline automation integrates the trained model into the cloud, which can take a dataset as input and output. To quickly update the cloud ML algorithm in response to fresh data, we don't need to change the model. This guarantees the chosen model is valid and can be used with fewer flaws, improving performance. Our main objective is to build a reliable automated pipeline and install, maintain, and optimise machine learning models and systems securely and efficiently.

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An Adaptive-Gain Recurrent Neural Network for solving the Joint-Angle Drift Issues of Redundant Manipulators

An Adaptive-Gain Recurrent Neural Network for Solving the Jo...

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2023 International Annual Conference on Complex systems and Intelligent science, CsIs-IAC 2023

作者： Zheng, boyu Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. School of Information Engineering Nanchang University Nanchang330031 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China College of Engineering Peking University Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

ISBN: (纸本)9798350309003

This paper proposes a new adaptive-gain recurrent neural network (AG-RNN) to effectively cope with the joint-angle drift issues in redundant manipulators. specifically, a joint-angle drift-free with the feedback control of the velocity layer motion equation (JADF-FC) is proposed via an optimization criterion for synchronously optimizing linear terms and quadratic. Then, the JADF-FC is reasonably formulated into a standard quadratic programming (QP) issue. Different from the previous recurrent neural networks (RNNs), the AG-RNN proposed in this paper constructs an error-based differential equation with a new adaptive-gain. It should be noted that the proposed adaptive-gain does not gradually approach infinity as time increases, which is more in line with actual hardware implementation requirements than the existing time-variant-gain. The adaptive-gain can reduce the joint-angle drift errors of the redundant manipulator. Therefore, the proposed AG-RNN can solve the QP problem of the manipulator more effectively and quickly. To validate the performance of the proposed AG-RNN, it is compared with representative RNNs. The experimental results indicate that smaller joint-angle drift errors can be get by the proposed AG-RNN solving JADF-FC scheme than the other solutions when solving the joint-angle drift issues. © 2023 IEEE.

关键词： Quadratic programming

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Malicious Traffic Classification in WsN using Deep Learning Approaches

Malicious Traffic Classification in WSN using Deep Learning ...

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Communication, security and Artificial Intelligence (ICCsAI), International Conference on

作者： Chhote Lal Prasad Gupta Dinesh Rajassekharan Dilip Kumar sharma Mohanraj Elangovan varatharaj Myilsamy Kamal Upreti Dept. of Computer Science and Engineering Bansal Institute of Engineering and Technology Lucknow India Faculty of Computer Science Peninsula College Peninsula Malaysia Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India Department of Artificial Intelligence and Data Science K. S. Rangasamy College of Technology Tiruchengode Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science CHRIST(Deemed to be University) Delhi-NCR Ghaziabad India

Classifying malicious traffic in Wireless sensor Networks (WsNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WsNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LsTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WsNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WsNs is essential. Classifying malicious communication in a WsN using a Long short-Term Memory (LsTM) is efficient. WsNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LsTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data.

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Detection of Early Fault in Power Electronic Converters through Machine Learning and Data Mining Techniques 3

Detection of Early Fault in Power Electronic Converters thro...

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3rd International Conference on Artificial Intelligence and smart Energy, ICAIs 2023

作者： Pradeep, K.v. Gowrishankar, K. sivanantham, E. Rao, Katta subba Kiran, Nagulapati vimal, A. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Amet University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Anil Neerukonda Institute of Technology and Sciences Department of Electrical and Electronics Engineering Andhra Pradesh Visakhapatnam India Sri Eshwar College of Engineering Department of Mechanical Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9781665462167

The Power electronic system plays a significant role in versatile applications. The power electronic converters are largely used in energy conversion mechanisms. A fault is defined as the abnormal condition of the system that results in various consequences. The important constraints in the modelling of power electronic systems involve losses, Electromagnetic Interference (EMI) and harmonics. This includes the fault detection in the power electronic converters that includes three phase rectifier, d-dc converter and single-phase inverter. These parameter affects the overall efficiency and quality of the system. To overcome the fault in the power electronic converters, the machine learning with data mining techniques is adopted. This helps to predict the early fault and helps to increase the efficiency of the system. © 2023 IEEE.

关键词： signal interference

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IoT and Image Processing based smart Door Locking system 1

IoT and Image Processing based Smart Door Locking System

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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRs 2022

作者： Jayaram, b. subhahan, D. Abdus sakthivel, b. Mohanaprakash, T.A. Joshi, sunita Kumar, M. Jogendra Department of Computer Science and Business Systems RMK Engineering College Tamil Nadu Kavaraipettai601206 India Department of Computer Science Engineering B V Raju Institute of Technology Telengana Narsapur502313 India Department of Electronics and Communication Engineering Vivekanandha College of Engineering for Women Elayampalayam Namakkal Tamil Nadu Tiruchengode637205 India Department of Computer Science and Engineering Panimalar Engineering College Tamilnadu Chennai600123 India Department of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad121004 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781665460842

An effective home security system improves the standard of living by making the home a safer place to live in general. In recent years, smart door locks have become increasingly popular as an integral component of home security systems. Locking and unlocking a door with a smart door lock system requires authentication. Unfortunately, several break-in attempts have been made when the invader tried to bypass the locks. In this work, Internet of Things (IoT) and image processing can be used to strengthen the safety of smart Door Lock systems. The camera is triggered after the motion detector has determined that a human has entered the area. The camera picks up a clear picture of the visitor standing by the entrance. Using image processing methods, the image is delivered to the microcontroller and compared with the registered users. A door will open automatically if the person's face is in the database. Otherwise, a notification and a photo of the person will be sent to the user's mobile app via IoT. In the designed mobile app, the user may view the individual's face and issue commands to the system, such as opening and closing doors. Using the developed methodology, the safety of the house will be increased, and unauthorized visitors will be prevented. © 2022 IEEE

关键词： Internet of things

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An Efficient Missing Data Imputation Model on Numerical Data 2

An Efficient Missing Data Imputation Model on Numerical Data

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2nd Global Conference for Advancement in Technology, GCAT 2021

作者： sowmya, v Kayarvizhy, N. B.M.S College of Engineering Department of Computer Science and Engineering Bangalore560019 India

ISBN: (纸本)9780738132150

With the rising spotlight on Data science and big data analytics in both private and public sectors, enormous amount of domain-specific information has been collected. Missing information is a typical issue and consequently has pulled in significant consideration. Incomplete data analysis is a practical problem, and an intriguing issue. This study presents a methodology and framework for imputing missing value and at different missing rate of variables. An effective missing data imputation model based using Long short-Term Memory (LsTM) on numerical dataset is proposed. For the hidden layers in a deep neural network, Rectified Linear Units (ReLU) is conventionally used as an activation function. However, there are situations where progressively more complicated functions are required to tackle vanishing gradient problem. The proposed LsTM with ReLU activation function is advantageous in terms of mapping features, weight distribution and loss function. We estimate LsTM-ReLU's hyper-parameter values and compared its average RMsE performance with another neural network LsTM model LsTM-softmax and machine learning models like Naïve bayes and KNN. Experiments were carried out on the various datasets. Results obtained using LsTM-ReLU model outperforms LsTM-softmax, Naïve bayes and KNN method. LsTM-ReLU achieves an average performance gain of 40% - 46% over LsTM-softmax imputation with varied missingness value and 40% - 42% on varied hyper-parameter size. Accuracy is much greater and mean square error less than 0.02% with Naïve bayes and KNN model. © 2021 IEEE.

关键词： Long short-term memory

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