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

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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Analyzing functional, technical and bibliometric trends of blockchain applications in education: A systematic review

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Multimedia Tools and Applications 2025年第8期84卷 4003-4048页

作者： Choudhary, Ankita Chawla, Meenu Tiwari, Namita Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

In an educational ecosystem, accurate and complete management of student’s records is a complex and error-prone process. Blockchain is a promising solution to add substantial value around enhanced efficiency, privacy protection, technological advancement, access control, and secure exchange of student course records. This work presents a systematic literature review to analyze the applications of blockchain technology in the educational domain addressing the gaps identified in the previous reviews. The technical papers published in English were searched by key terms related to blockchain and education on IEEE Xplore, science Direct, and six other databases. The risk of bias was assessed by considering all the crucial search terms preventing the accidental exclusion of any article. Many blockchain frameworks in the educational domain have been proposed;including some prototypes and implementations. The PRISMA framework was used for the evaluation of 150 distinct models, 124 of which were prototypes, pilots, or full implementation and the remaining 26 were only proposals. This work contributes a structured bibliometric, functional, and technical examination of these articles, assessing privacy, cost, performance, security, architecture, and standards to identify and bridge gaps. The study concludes an avalanche of proposals, with approximately half of them being prototypes, indicating that implementation and proof-of-concept pilots are expected. Blockchain is found to solve various record management issues at significant trade-offs between performance, storage, and cost. However, scalability issues, poor overall performance, and increased price are major limitations for implementing blockchain in education solutions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Records management

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Exploring Personalized Internet of Things(PIoT),social connectivity,and Artificial Social Intelligence(ASI):A survey

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High-Confidence Computing 2024年第3期4卷 136-162页

作者： Bisma Gulzar Shabir Ahmad Sofi Sahil Sholla Department of Information Technology National Institute of TechnologySrinagar 190001India Department of Computer Science and Engineering Islamic University of Science and TechnologyGazipur 1700India

Pervasive Computing has become more personal with the widespread adoption of the Internet of Things(IoT)in our day-to-day *** emerging domain that encompasses devices,sensors,storage,and computing of personal use and surroundings leads to Personal IoT(PIoT).PIoT offers users high levels of personalization,automation,and *** proliferation of PIoT technology has extended into society,social engagement,and the interconnectivity of PIoT objects,resulting in the emergence of the Social Internet of Things(SIoT).The combination of PIoT and SIoT has spurred the need for autonomous learning,comprehension,and understanding of both the physical and social *** research on PIoT is dedicated to enabling seamless communication among devices,striking a balance between observation,sensing,and perceiving the extended physical and social environment,and facilitating information ***,the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence(ASI)in PIoT ***,autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful *** paper provides a comprehensive review of the evolving domains of PIoT,SIoT,and ***,the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID *** study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions,paving the way for further advancements in this transformative field.

关键词： Personal Internet of Things(PIoT) Artificial Social Intelligence(ASI) Hyperpersonalization

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Secure-JobChain: Revolutionizing Job Recruitment with Blockchain technology

Secure-JobChain: Revolutionizing Job Recruitment with Blockc...

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2024 IEEE International Conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Pavithra, S. Geethanjali, S. Vidya, B. Suria, S. Rahul, B.S. Kishore, R. Computer Science and Engineering Rajiv Gandhi College of Engineering and Technology Pondicherry India SRM Institute of Science and Technology Data Science and Business Systems Kattankulathur India SRM Institute of Science and Technology Electronics and Communication Engineering Kattankulathur India

ISBN: (纸本)9798331539948

The traditional job recruitment process is time consuming and also undergoes certain difficulties such as data privacy, lack of transparency, and inefficient credential verification. These difficulties lead to mistrust and also delay the process of hiring. There is a need for improvement in this process that could benefit both the companies and the job applicants. In this paper, a decentralized recruitment platform based on blockchain technology is proposed. This proposed technique will overcome the above-mentioned difficulties by ensuring secure, transparent, and efficient connections between the recruitment companies and the prospective candidates. This solution uses the merits of blockchain i.e., immutability and automation to record job postings, applications, and hiring decisions on an immutable ledger, protecting personal data with cryptographic techniques and automating qualification verification with smart contracts. By implementing the decentralization concepts in Job recruitment, it is proven that the transparency in the interview process is improved by 9 0 and fraud risks are reduced considerably. Moreover, the efficiency of the entire job recruitment process is improved largely. Thus, the decentralized solution proposed in this paper demonstrates its potential in revolutionizing the job recruitment process by providing a secure, transparent and efficient hiring ecosystem. © 2024 IEEE.

关键词： Smart contract

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Soft magnetism enhancement and eddy current suppression in bioinspired Iron-based nanocrystalline soft magnetic composites with nacre-like structure

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材料科学技术（英文版） 2025年第3期206卷 202-210页

作者： Wangchang Li Wenbo Xiang Yue Kang Ting Zou Xiao Han Yao Ying Jing Yu Jingwu Zheng Liang Qiao Juan Li Shenglei Che College of Materials Science and Engineering Zhejiang University of TechnologyHangzhou 310014China Research Center of Magnetic and Electronic Materials Zhejiang University of TechnologyHangzhou 310014China Systems Engineering Institute Beijing100010China

Bioinspired nacre-like structured high-density soft magnetic composites(SMCs)have been successfully constructed using flaky-Fe73.8Si13.5B8.7Cu1Nb3 powders in the supercooled liquid region(SCLR).These densely arranged particles with a consistent planar orientation significantly enhance the soft magnetic properties of SMCs,including high permeability and low magnetic *** internal structures of the composites and microstructure evolution of the flaky nanocrystalline particles during the hot-pressing process have been thoroughly ***,systematic investigations into the effects of coatings and particle sizes on the maximum permeability and magnetic losses of the composites are *** SMC prepared using the coated particles with a size of 0-100 pm exhibits a high maximum perme-ability of 2170(at 1000 Hz)and low magnetic loss of 41.61 W kg-1(at 1000 Hz and 1.0 T).The losses and permeability analysis reveal that the superior performance of these soft magnetic materials is attributed to their laminated structure,insulation coating,and the reduced planar demagnetizing *** to the traditional silicon steel,this novel SMCs exhibits high magnetic permeability and reduced magnetic losses at frequencies above 1000 Hz,which possess immense application potential within high-frequency electric machines.

关键词： Nacre-like structure Nanocrystalline soft magnetic composite High permeability Low magnetic loss

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Advancing automotive high-performance computing: integrating direction API with LORA technology in unified machine vision for future smart cars

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Multimedia Tools and Applications 2025年第18期84卷 19315-19342页

作者： Chaudhary, Kapil Department of Computer Science & Engineering BipinTripathi Kumaon Institute of Technology Gaucher Dwarahat Road Uttarakhand263653 India

This manuscript addresses the challenges in developing Automotive High-Performance Computing (HPC) systems for Future Smart Cars and Unmanned Mobile Vehicles (UMVs). We introduce a novel Unified Machine Vision (UMV) narrative methodology for operators overseeing UMVs. The integration of diverse sensors, Direction API software, and LoRa communication technology presents significant technical hurdles. To tackle these, we focus on integrating Direction API with LoRa in UMVs, incorporating open-source natural interaction OpenNI, and haptic driver inputs into the Master Control System (MCS). The MCS, using the Comp-GAP algorithm, serves as the central interface between the driver and machinery sensors, effectively managing commands to achieve multifaceted objectives. Additionally, we present the CCHA, CROMM-MSV parallel algorithm to mitigate power consumption issues in autonomous vehicles via centralized HPC. This new centralized Advanced Driver Assistance System (ADAS) methodology optimizes UMV energy consumption in air and enhances operational efficiency through parallel HPC techniques. Simulation results demonstrate stable behavior in parallel processing, validating the proposed methodologies. In summary, this manuscript offers a robust framework for HPC systems in Future Smart Cars and UMVs, paving the way for improved navigation and vehicular control. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer vision

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Unique hash value based identical video sharing traffic flow optimization across social networks

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Multimedia Tools and Applications 2025年第13期84卷 11983-12011页

作者： Konyak, Chenlep Yakha Baydeti, Nagaraju Department of Computer Science and Engineering National Institute of Technology Nagaland Nagaland Chumukedima Dimapur797 103 India

Identical video sharing across social networks results in redundant video data traffic, consuming additional bandwidth, storage resources, and data usage. A novel method is proposed to optimise redundant video-sharing services across social media applications exclusively on the push model, which is based on creating a unique hash value identity (UHVID) to identify the video distinctly. The UHVID is generated based on the centroid of gradient orientation of the key frames. The key frames are extracted from the video based on colour histogram and structural differences. Using UHVID and video metadata, the server application validates and optimizes identical video content flow before actual video sharing. The end users’ privacy is not affected in the proposed framework, as the actual video is not used for comparison, only the hash values of the video. The statistical analysis has shown that in 14 test cases of 2700 traffic initiations and 39,180 transactions, 83.2% of redundant data of identical videos were optimised, resulting in 63.61% data savings, amounting to 19.75 GB. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Digital storage

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Hybrid approach for the detection of epileptic seizure using electroencephalography input

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International Journal of Information technology (Singapore) 2024年第1期16卷 569-575页

作者： Basha, Niha Kamal Surendiran, B. Benzikar, Amutha Joyal, S. School of Computer Science and Engineering Vellore Institute of Technology (VIT) Vellore India Department of Computer Science and Engineering National Institute of Technology (NIT) Puducherry Karaikal India School of Computer Science and Engineering Vellore Institute of Technology (VIT) Chennai India Saveetha Engineering College Chennai India

In the early days, it was difficult to study bio-electric signals, but now a days these problems have been solved by many hardware devices which are available at low cost. Even then there is a need for technical improvements to process bio-electric signals. Enormous effort has been taken for the detection of epileptic seizure from EEG signal. Considerable evidence available on Bonn university EEG dataset for epileptic seizure detection. However, deep learning algorithms have not applied often on Bonn university data like other machine learning algorithms for the detection of epileptic seizure due to the less availability of data. This work adopted machine learning and deep learning models for the detection of epileptic seizure. The data consists of a hundred subjects with sampling rate as 173.61 Hz of ‘S’ (ictal) and ‘Z’ (normal) dataset. The results show how the choice of healthy and ictic subjects through high order functional math decision like variance (STD), Power, Skewness, and Kurtosis values are analyzed to extract epileptic features. The performance of classifiers has been evaluated based on the evaluation metrics within which CGRU-SVM outperforms all other models with 97.54% accuracy. The features are extracted based on the statistical measures to detect epileptic seizure. Further, the proposed work provides more evidence on the epileptic seizure features, and it shows the new possibility for using deep learning models for epileptic seizure detection. In future, an analysis must be performed with dynamic sets of input and different analysis techniques. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Confusion matrix Deep learning model Electroencephalography Epilepsy Ictal subject Rhythmic frequency Statistical feature

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Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

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computer systems science & engineering 2023年第1期44卷 501-517页

作者： K.Nagalakshmi Dr.S.Suriya Department of Computer Science and Engineering Sethu Institute of TechnologyVirudhunagar626115Tamil NaduIndia Department of Computer Science and Engineering PSG College of TechnologyCoimbatore641004Tamil NaduIndia

Breast cancer is one of the deadly diseases prevailing in *** detection and diagnosis might prevent the death *** diagnosis of breast cancer remains a significant challenge,and early diagnosis is essential to avoid the most severe manifestations of the *** existing systems have computational complexity and classification accuracy problems over various breast cancer *** order to overcome the above-mentioned issues,this work introduces an efficient classification and segmentation ***,there is a requirement for developing a fully automatic methodology for screening the cancer *** paper develops a fully automated method for breast cancer detection and segmenta-tion utilizing Adaptive Neuro Fuzzy Inference System(ANFIS)classification *** proposed technique comprises preprocessing,feature extraction,classifications,and segmentation ***,the wavelet-based enhancement method has been employed as the preprocessing *** texture and statistical features have been extracted from the enhanced ***,the ANFIS classification algorithm is used to classify the mammogram image into normal,benign,and malignant ***,morphological processing is performed on malignant mam-mogram images to segment cancer *** analysis and comparisons are made with conventional *** experimental result proves that the pro-posed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.

关键词： Breast cancer detection segmentation classification malignant

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COVID-19 detection from Chest X-ray images using a novel lightweight hybrid CNN architecture

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Multimedia Tools and Applications 2025年第13期84卷 11295-11317页

作者： Pradeep Dalvi, Pooja Reddy Edla, Damodar Purushothama, B.R. Dharavath, Ramesh Department of Computer Science and Engineering National Institute of Technology Farmagudi Goa Ponda403401 India Dhanbad India

The pandemic of COVID-19 has affected worldwide population. Diagnosing this highly contagious disease at an initial stage is essential for controlling its spread. In this paper, we propose a novel lightweight hybrid convolutional neural network to identify COVID-19-positive patients from Chest X-ray images using building blocks of MobileNet and ResNet50. This model shows improvement over the base models when applied to the same dataset independently. The proposed model is trained and tested on same configuration and dataset as that of its base models, for better performance analysis and comparison. It also has fewer parameters as compared to MobileNet and ResNet50, which makes it lightweight, adding to its better performance. It achieves an accuracy of 98.47 % with very few false negative predicted samples, which is extremely beneficial in medical diagnosis. Concerning the parameters, this model is built with only 200,034 trainable parameters, which marks a parameter reduction percentage of 99.15 % concerning ResNet50 and 93.82% compared to MobileNet. The generalisation performance of our proposed model is evaluated using stratified 5-fold cross validation technique. Our model achieved very good accuracy with very less parameters as compared to various state-of-the-art models. The model also classified Pneumonia images from Normal ones with an accuracy of 98.9 %. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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