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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Sheeja, R. Akshaya, R. Akshaya, S. Jashwanthikaa, D.R. Easwari Engineering College Computer Science and Engineering India

ISBN: (纸本)9798350306613

In the modern era of transportation, improving the energy efficiency poses to be one of the major challenges faced by electric vehicles (EVs). When drivers apply brakes the braking systems convert kinetic energy into heat which leads to loss of power and energy. This loss of power hinders the performance and range optimization of EVs. In order to solve this problem, there is a need to find solutions that helps in increasing energy efficiency in EVs. This paper suggests a solution that can be used in optimizing the energy recovered during braking using Artificial Intelligence (AI). The application of AI algorithms helps in redirecting the kinetic energy to the battery resulting in an enhanced energy regeneration process. The proposed AI optimized aims to overcome the limitations of the traditional energy recovery systems. Through real time data-Analysis and adaptable control, the AI system maximizes the conversion of energy and ultimately improves both the energy efficiency and range of anxieties. This approach not only deals with the setbacks of existing braking systems, but also cope with the increasing demand for sustainable technologies EVs. The integration of AI into the process of energy recovery and optimization leads to a transformation in electric vehicle technology. © 2024 IEEE.

关键词： AI and ML algorithms braking force energy consumption energy optimization energy recovery EV

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Microgrids with day-ahead energy forecasting for efficient energy management in smart grids: hybrid CS-RERNN

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Australian Journal of Electrical and Electronics engineering 2024年第3期21卷 213-227页

作者： Shirley, C.P. Pattar, Jagannath Kavitha Rani, P. Saini, Sumit Ranga, Jarabala Elangovan, D. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Mechanical Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electrical Engineering School of Engineering & Technology Central University of Haryana Mahendergarh India Detartment of Electrical and Electronics Engineering Ramachandra College of Engineering Eluru India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi institute of technology Hyderabad India

By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO). ©, Engineers Australia.

关键词： Energy utilization

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A Refuge System Development for Underground Mines through IIOT 2

A Refuge System Development for Underground Mines through II...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Srinath Yadav, K. Vignesh, M. Sivaguru, S. Ranjeeth Kumar, G. Santhosh, P. VSB Engineering College Computer Science and Engineering India

ISBN: (纸本)9798350368109

Underground mines can use the Internet of Things to monitor the environment, safety, and productivity by installing sensors that detect temperature, moisture content, and gas levels in real time. These sensors collect data continually and relay it to a central monitoring system. By examining this data to discover any anomalous conditions such as high temperatures, excess moisture, or harmful gas concentrations, mine managers can take immediate corrective action to preserve worker safety, prevent environmental damage, and enhance production efficiency. Our real-time abstract monitoring system provides essential insights into underground mining operations, helping to decrease hazards and enhance operational effectiveness. © 2024 IEEE.

关键词： Mine safety

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Genetics Based Compact Fuzzy System for Visual Sensor Network

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computer Systems science & engineering 2023年第4期45卷 409-426页

作者： Usama Abdur Rahman C.Jayakumar Deepak Dahiya C.R.Rene Robin Department of Computer Science and Engineering Jerusalem College of EngineeringChennaiTamil Nadu600100India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringChennaiTamil Nadu602117India College of Computer and Information Sciences(CCIS) Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Science and Engineering Sri Sairam Engineering CollegeChennai600044India

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static *** from the camera is sent to energy efficient sink to extract key-information out of *** applications range from health care monitoring to military *** a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O *** this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as *** innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the ***,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into *** propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual *** architecture is designed based on Mamdani’s fuzzy *** parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH *** inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of *** proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive *** algorithmbased machine learning optimizes the interpretability aspect of fuzzy *** results are obtained using *** result shows that the classification acc

关键词： Visual sensor network fuzzy system genetic based machine learning mobile sink efficient energy life of network

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Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems

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Soft Computing 2024年第13-14期28卷 7983-7998页

作者： Umar, Shahla U. Rashid, Tarik A. Ahmed, Aram M. Hassan, Bryar A. Baker, Mohammed Rashad Department of Software College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Department of Computer Science College of Science Charmo University Sulaimani Chamchamal46023 Iraq

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Swarm intelligence

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Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

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computer Modeling in engineering & sciences 2024年第10期141卷 443-458页

作者： Prasanalakshmi Balaji Omar Alqahtani Sangita Babu Mousmi Ajay Chaurasia Shanmugapriya Prakasam Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61451Saudi Arabia College of Science and Arts in Rijal Alma King Khalid UniversityAbha61451Saudi Arabia Department of Information Technology Muffakham Jah College of Engineering and TechnologyHyderabad500034India Department of Computer Science and Business Systems Rajalakshmi Engineering CollegeThandalam602105India

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire *** recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining *** cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival *** analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection *** upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and ***,the histopathology biopsy images are taken from standard data ***,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are ***,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer *** efficacy of the model is evaluated using divergent *** compared with other methods,the proposed work reveals that it offers impressive results for detection.

关键词： Bidirectional long short-term memory breast cancer detection feature extraction histopathology biopsy images multi-scale dilated vision transformer

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Cloud-Enabled Wireless Gesture Control System for Humanoid Bionic Arm Using IOT 2

Cloud-Enabled Wireless Gesture Control System for Humanoid B...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Geetha, S. Vichitra, S. Nithiya Shree, M. Sandhiya, V. Yuvarani, V. VSB Engineering College Computer Science and Engineering India

ISBN: (纸本)9798350368109

The Internet of Things (IOT) connects Tangible assets via data fusion facilitating information interchange. Ranging from basic temperature sensors to sophisticated industrial robots, IOT covers a wide range of applications. Cloud computing offers immediate access to computing services, reducing infrastructure expenses. Inclinometer sensors and cameras combine for advanced hand gesture recognition, capturing initial movements through real time video analysis. An innovative IR - based recognition system, supported by Intelli-cloud and AES (Advanced Encryption Standard) security, enhances the framework. Technological progress narrows the human-machine divide, highlighting gestures in communication. Hand-arm robotic research emphasizes freedom levels, grip efficacy, and adaptability using sensor-focused methods, shaping the evolution of human-robot interaction. © 2024 IEEE.

关键词： Human robot interaction

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Hybrid Kalman Filter and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

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Informatica (Slovenia) 2024年第23期48卷 121-132页

作者： Marzook, Ali K. Alkenani, Jawad University of Basrah College of Engineering Department of Electrical Engineering Iraq Department of Computer Science Shatt Al-Arab University College Basra Iraq

Several significant research studies have been done in distributed applications, database management systems, and information collecting in computer science concerning data mining and processing for wireless sensor networks (WSNs). As a result of WSNs' limited computation, networking, and data mining capabilities, the primary objective of creating WSN-based applications has proven to be extremely challenging: making decisions in real time. As such, typical data mining techniques are difficult to apply to sensor data due to their nature, peculiarities, and the constraints of wireless sensor networks. This work introduces a novel method for data mining and gathering and noise removal in wireless sensor networks (WSNs), dubbed KF-BA. This methodology increases network lifetime and efficiency by combining the Kalman Filter (KF) with the Bat Algorithm (BA). The recommended methods and BA contrast the provided techniques for raising energy consumption and prolonging network lifetime. The BA algorithm is not as effective as the KF-BA approach. The suggested strategy yields network longevity that is almost (57%) longer than BA in this instance, and that is after 2,000 packets are sent to two sensors spread over the network. The results were simulated using the MATLAB environment. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Kalman filters

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Residual multimodal Transformer for expression-EEG fusion continuous emotion recognition

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1290-1304页

作者： Xiaofang Jin Jieyu Xiao Libiao Jin Xinruo Zhang College of Information and Communication Engineering Communication University of ChinaBeijingChina School of Computer Science and Electronic Engineering University of EssexColchesterUK

Continuous emotion recognition is to predict emotion states through affective information and more focus on the continuous variation of emotion. Fusion of electroencephalography (EEG) and facial expressions videos has been used in this field, while there are with some limitations in current researches, such as hand-engineered features, simple approaches to integration. Hence, a new continuous emotion recognition model is proposed based on the fusion of EEG and facial expressions videos named residual multimodal Transformer (RMMT). Firstly, the Resnet50 and temporal convolutional network (TCN) are utilised to extract spatiotemporal features from videos, and the TCN is also applied to process the computed EEG frequency power to acquire spatiotemporal features of EEG. Then, a multimodal Transformer is used to fuse the spatiotemporal features from the two modalities. Furthermore, a residual connection is introduced to fuse shallow features with deep features which is verified to be effective for continuous emotion recognition through experiments. Inspired by knowledge distillation, the authors incorporate feature-level loss into the loss function to further enhance the network performance. Experimental results show that the RMMT reaches a superior performance over other methods for the MAHNOB-HCI dataset. Ablation studies on the residual connection and loss function in the RMMT demonstrate that both of them is functional.

关键词： facial expression recognition human-machine interaction information fusion physiology regression analysis

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and Electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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