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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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Efficient Skin Lesion based Classification System for Monkeypox Detection using VGG16 and Ensemble Learning 10

Efficient Skin Lesion based Classification System for Monkey...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Pabbi, Varinder Khullar, Vikas Angurala, Mohit Ramgarhia Institute of Engineering and Technology Department of Computer Applications Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789380544472

Monkeypox is a recently emerged disease outbreak that has impacted the health of many individuals. This study focuses on developing a skin lesion-based classification system for monkeypox disease. Many recent works suggest that the direct implementation of machine learning (ML) and deep learning (DL) approaches for this task has not resulted in high accuracy. Therefore, this paper proposes an efficient technique that employs a hybrid of VGG16-based feature engineering, class balancing, and ensemble learning for the detection of monkeypox skin lesions. Specifically, DL-based feature extraction is applied, but the classification is carried out through ensemble learning techniques. The results demonstrate that the random forest, extra tree, and histogram gradient descent methods achieved better performance in terms of different performance metrics. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Feature extraction

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An Energy Efficient IPv6 Packet Delivery Outline for Industrial IoT Over G.9959 Protocol Based Wireless Sensor Network (WSN)

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SN computer science 2024年第7期5卷 813页

作者： Veena, R.S. Khambra, Geetanjli Sayal, Rishi Singh, Nagendra Dhandayuthapani, V. Bala Sar, Sumit Kumar Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Kanakapura Road Karnataka Bengaluru India Department of Computer Science and Applications The Bhopal School of Social Sciences MP Bhopal India Department of Computer Science and Engineering Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telangana Hyderabad 501506 India Department of Electrical and Electronics Engineering Trinity College of Engineering and Technology Telangana Karimnagar 505001 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas campus Shinas Oman Department of Computer Science BIT Durg Chattisgarh Durg India

The world is witnessing a significant paradigm shift in the delivery and management of industrial services, driven by the need for automated manufacturing in industries such as mining. This transformation is pivotal as it leverages the Internet to interconnect people and devices, giving rise to a new field known as the Industrial Internet of Things (IIoT). IIoT represents the fusion of IoT’s computational capabilities with the principles of the industrial revolution, facilitating extensive scalability through the transition from IPv4 to IPv6. A crucial challenge within IIoT is the battery life of sensor nodes, which is directly influenced by the efficiency of their operating modules and protocols. Various protocols have been tested within IIoT-based Wireless Sensor Networks (WSNs) to address this issue. However, these protocols often fall short in terms of data rate performance, which is essential for supporting the diverse range of IIoT applications and services. This manuscript investigates the IPv6 packet delivery rate, focusing on energy efficiency and delay. The research identifies the G.9959 protocol as superior to IEEE 802.15.4 for IIoT adoption. Additionally, it examines the impact of bandwidth variation on energy efficiency. Through comprehensive simulations, the proposed scheme demonstrates superior performance compared to existing protocols, highlighting its potential for enhancing IIoT applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： G.9959 IIoT IPv6 OPD WSN

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Webcam-Guided Gesture-Controlled Robotic Arm 3

Webcam-Guided Gesture-Controlled Robotic Arm

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Singh, Kanishk Joshi, Aditya Bhatt, Jaishankar Juyal, Amit Rawat, Manmohan Singh Graphic Era Deemed to Be University Department of Computer Applications Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University School of Computing Dehradun India Uttarakhand State Council for Science and Technology Dehradun India

ISBN: (纸本)9798350384598

This paper focuses on the design of a gesture-controlled robotic arm that is navigated with the help of a webcam and OpenCV-enabled real-time hand tracking. Embedded with OpenCV's hand tracking module, the system successfully identifies the hand landmarks from the live camera captured hand movement of the user. By the current state of each finger, the state is represented by a string format that includes the symbol and number (such as00010) that is transmitted to an Arduino Uno board, the robotic arm can move its fingers up or down. Control of the flows of the movement of the robotic arm is done using the Arduino Uno whereby the servo motors receive signals to move 0 or 180 degrees. This is mainly due to the efficiency, precision and short latency of the suggested system which makes it novel that gives the user a simple graphics interface to control robotic arms without necessity to know programming or have specific hardware. This technology provides an effective solution in robotic manipulation and presents the enormous potential to enhance HRI in different application areas. © 2024 IEEE.

关键词： Robotic arms

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Protein-Ligand Interaction Prediction with Effective Method of Graph Attention Networks and Long Short-Term Memory 2

Protein-Ligand Interaction Prediction with Effective Method ...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. AlFatlawy, Ramy Shashidhara, K.S. Revathi, R. Reka, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Sona College of Arts and Science Salem India

ISBN: (纸本)9798331504960

Nowadays, the drug discovery field of molecules in protein-ligand interactions developing effective therapeutics for identifying potential drug candidates with spatial features to predict binding affinity. However, the existing Deep Learning (DL) based prediction approaches failed to interact and predict the binding affinity of protein-ligand precisely for drug design with binding affinity. To overcome this problem, a Graph Attention Networks with Long Short-Term Memory (GATN-LSTM) method is proposed with spatiotemporal forecasting for interaction between protein and ligand in binding affinity prediction for drug design. The proposed GATN-LSTM method is employed on DUD-E dataset consists of enhanced the prediction by capturing relationship (interaction) between protein and ligand in molecules for drug discovery using PubChem for prediction. Then, the preprocessed data of protein-ligand interaction is extracted using (CNN-GNN) for better generalization of both local and global features for predicting drug discovery. Finally, GATN-LSTM is employed for interaction of molecules with binding affinity which enhanced the drug design and also improved the prediction results. Experimental results of the proposed GATN-LSTM method attained accuracy (0.9992), F1-score (0.9985) are higher than existing prediction approaches such as Convolutional Neural Network (CNN) and Mutual Information-based Feature Selection (MIFS). © 2024 IEEE.

关键词： Graph neural networks

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Physical Layer Security of 6G Vehicular Networks with UAV Systems:First Order Secrecy Metrics,Optimization,and Bounds

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computers, Materials & Continua 2024年第9期80卷 3685-3711页

作者： Sagar Kavaiya Hiren Mewada Sagarkumar Patel Dharmendra Chauhan Faris A.Almalki Hana Mohammed Mujlid Smt.Chandaben Mohanbhai Patel Institute of Computer Applications Charotar University of Science and TechnologyChangaAnand388421India Electrical Engineering Department Prince Mohammad Bin Fahad UniversityP.O.Box 1664Al Khobar31952Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Chandubhai S.Patel Institute of TechnologyCharotar University of Science and TechnologyChangaAnand388421India Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Kingdom of Saudi Arabia

The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research ***,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor ***,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography ***,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m ***,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper ***,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV *** findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and *** results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.

关键词： Physical layer security 3D positioning 6G communications UAV systems

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Wireless Body Area Network (WBAN) Centric Healthcare System for Continuous Monitoring 9

Wireless Body Area Network (WBAN) Centric Healthcare System ...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Kumar, M. Anand Valli Mayil, V. Jeyalakshmi, T. Ratha Ghanshala, Anshul Gupta, Anshika Graphic Era University Dept of Computer Applications Dehradun India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Jain University School of Sciences Department of Data Science Banglore India The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350397376

Stroke is one the most dangerous disease all across the world which would lead to serious disability or death among all aged people due to changes in environmental factors. Recent reports indicate that a mere 75 million people will suffer from stroke-related disabilities by the year 2030. It was also identified that a stroke occurs every 40 seconds on average across the globe. One out of every five stroke-related patients would die in a year. A Recent health survey indicates that most developed countries face a high number of stroke patients in very recent years. There is also a rapid growth in the number of stroke cases in developing countries. To prevent the high-risk factors related to stroke diseases, there is much need for continuous observation of physiological signals in real-time and to analyze the data. One of the main strategies is wearable technology that employs wireless sensor networks to link numerous medical devices and associated systems. They have to ability to function independently across several platforms. Wearable devices are used in most medical applications because of their noteworthy qualities, including adaptability, versatility, inexpensiveness, and efficiency in various platforms. Integration of the Internet of Things (IoT) with wearable devices and Fog Computing provides a more flexible mechanism to deploy a continuous healthcare monitoring system to monitor vital symptoms and to detect the early phase of stroke factors instantly. To predict stroke-related disorders, this research proposes a Wireless Body Area Network (WBAN)-centric healthcare system for continuous monitoring and examination of physiological signals. Initially, the proposed model predicts the nature of the symptoms based on the parameters collected by the IoT sensors. Deep learning techniques are then applied to predict the risk factors that are critical to the stroke patient during the emergency condition. The key contribution of this research work is to provide an emerg

关键词： Internet of things

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Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning 2

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Kavitha, S.J. Cibi, A. Sutherlin, Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Pallavaram Chennai India Virtusa Consulting Services Private Limited Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798331542573

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector. © 2024 IEEE.

关键词： Zero-shot learning

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Detection of Retinal Diseases in Retinal Optical Coherence Tomography Images 2

Detection of Retinal Diseases in Retinal Optical Coherence T...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Anand, M. Sheeba, T.M. Princella, A.S.Anshy SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Valliammai Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350397635

In biomedicine, disease image classification is a laborious, manual process. However, recent developments in computer vision have made applying artificial intelligence for the detection and categorization of medical images a more practical option. It is essential to analyse retinal illnesses utilising Optical Coherence Tomography (OCT) scans. For patients to survive, these disorders must be accurately detected and classified. The analysis of retinal illnesses is currently done by doctors on a regular basis by looking at the OCT pictures. The manual diagnosing process is laborious, though. In order to aid clinicians in their diagnosis, an automatic detection and classification technique for retinal illnesses has been developed in this work. Utilizing OCT scans, a deep multilayered Artificial Neural Network (ANN) has been employed to identify and categorise the retinal anomalies. On a 150 image open source retinal OCT dataset, the suggested technique has been used. © 2023 IEEE.

关键词： Optical tomography

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Intrusion Detection at the Edge Computing: A Deep Learning Approach Using the UNSW-NB15 Dataset

Intrusion Detection at the Edge Computing: A Deep Learning A...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Vipin Kumar Vivek Kumar Abhay Pratap Singh Bhadauria Jay Dixit Amit Kumar Department of Computer Science Gurukula Kangri (Deemed to be University) Haridwar India Department of Computer Engineering & Applications GLA University Mathura Department of Computer Engineering & Applications GLA University Mathura UP

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. The findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. This study investigates the application of edge computing, Internet of Things (IoT), and deep learning to improve the performance of Intrusion Detection Systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. The results demonstrate better performance, showing their suitability for real-time security applications. These findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments.

关键词： Deep learning Image edge detection Computational modeling Intrusion detection Real-time systems Internet of Things Security Servers Long short term memory Edge computing

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