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An Effective Cognitive Radio Network Based Improved Differential Evolution Optimization in Energy Harvesting Scenarios

An Effective Cognitive Radio Network Based Improved Differen...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： B Sravankumar Sundararaj G Kharmega Muntather Almusawi Bhupathi Prashanthi K S Shashidhar CSE (AI and ML) Vaagdevi College of Engineering Warangal India CSE Department Dr. T. Thimmaiah Institute of Technology Oorgam KGF India The Islamic university Najaf Iraq Department of Computer Science and Engineering GRIET Hyderabad Telangana India Dept. of ECE Nitte Meenakshi Institute of Technology Bengaluru India

Energy harvesting-based Device to Device (D2D) communication, powered by renewable energy sources, contributes to sustainable and eco-friendly communication systems. Developing a model for cognitive radio-based D2D communication in energy harvesting scenarios addresses the need for energy-efficient, reliable, and adaptive communication systems capable of operating in resource-constrained environments. This research faces major problems such as sub-optimal power allocation and sub-optimal channel allocation. In this paper, propose the use of the Improved Differential Evolution optimization algorithm to enhance the energy efficiency of the model and improve throughput performance. Our model achieves better performance compared to other state-of-the-art methods. Evaluate the model on two different metrics, namely energy consumption and throughput. The numerical validation of energy consumption involves 50 D2D nodes with 10 Joules (J), while throughput evaluation includes 50 D2D nodes with 320 bits per second (bps). The model can be compared to similar existing models such as the Power Control-Fuzzy Clustering Method (PC-FCM) and the Hybrid Intelligent Clustering Strategy (HICS).

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Quantum Artificial Intelligence: Unlocking the Future of Technology

Quantum Artificial Intelligence: Unlocking the Future of Tec...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： G. Ignisha Rajathi L.R. Priya A. Vegi Fernando R. Vedhapriyavadhana S. Sasindharan J. Mohanalin Department of Information Technology Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Manipal India Department of Electronics and Communication Engineeing Farncis Xavier Engineering College Tirunelveli Tamil Nadu India Department of Computer Science and Engineering (AI & ML) Dayananda Sagar University Bengaluru Karnataka India School of Computing Engineering and Physical Sciences University of the west of Scotland London UK Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Electrical and Electronics Engineering College of Engineering Perumon Kerala India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Quantum Artificial Intelligence (Qai) stands for the combination of the exercise of ‘quantum computing’ and ‘artificial intelligence’ that drives breakthrough transformation across various industries. In this paper, it is discussed on how Qai is bringing change across industries including pharmaceuticals, finance, weather prediction, supply chain, security, smart cities, agriculture, telecommunication, and education. With quantum mechanics conceptions, Qai enhance computation ability to address more data, and enhance the algorithms. Quantum technologies have potential for attachment to ai systems and make the previously unachievable innovations with the help of overgrowing the classical models. As a conclusion, this paper also discusses its result on the methodological difficulties and ethical issues of Qai, the need to build strategic partnerships and the need for well-regulated legislation. The results extend our understanding of Qai to apprehend the future of technology and its application for solving some of the greatest problems that the world faces today.

关键词： Drugs Ethics Technological innovation Quantum computing Smart cities Finance Weather forecasting Security Artificial intelligence Optimization

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Improved Denoising for Multi-frame Image Restoration with Convolutional Neural Networks

Improved Denoising for Multi-frame Image Restoration with Co...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Hitesh Kalra Kiran Lokesh Maney Omkaresh S. Kulkarni K. Ranjith Singh Gunveen Ahluwalia T. Surulivel Rajan Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Mangement JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Multi-body photograph restoration is a key application of state-of-the-art image processing. With the improvement of cutting-edge convolutional neural networks (CNNs), it has come to be modern-day viable to repair photos with improved denoising performance. CNNs allow the present-day modern-day complicated non-linear features with high accuracy and fast inference. A CNN-based totally multi-body image healing (MFIR) technique is proposed that is able to seize the spatial correlations between noise and texture in multi-body snapshots. Experiments on benchmark datasets show that our technique achieves overall performance, demonstrating the effectiveness of modern-day CNNs in this challenge. Our approach is powerful to extraordinary multi-frame noise levels and works higher than CNNs with the best unmarried-body entry. Additionally, our proposed technique will be extended to different picture healing responsibilities with diverse today’s corpora.

关键词： Correlation Accuracy Noise reduction Noise Maintenance engineering Benchmark testing Image restoration Convolutional neural networks Noise measurement Noise level

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Federated Learning for Residential Load and PV Production Forecast

Federated Learning for Residential Load and PV Production Fo...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Gargi Naladkar Amit Yadav Jinendra Gugaliya Computer Science & Engineering Department Indian Institute Of Technology Kanpur Kanpur India Sr. Engineer Data Science CTO India Enphase Solar Energy Pvt.Ltd Bangalore India AI/ML Scientist – CTO Office Enphase Solar Energy Pvt.Ltd Bangalore India

ISBN: (数字)9798350373929

ISBN: (纸本)9798350373936

This paper explores the application of federated learning in predicting energy consumption and solar production for households equipped with Enphase smart home solar systems. Traditional centralized model training for energy prediction entails significant communication overhead, particularly when dealing with distributed data sources. To mitigate this challenge, federated learning is proposed as a privacy-preserving alternative, wherein each client trains a local model on its data and transmits model updates to a central server for aggregation. We propose a neural network-based energy demand predictor trained with federated learning, leveraging distributed data while maintaining prediction accuracies. Federated learning ensures data security and confidentiality by transmitting only model updates, preserving individual privacy. The datasets used are anonymized, and the actual data cannot be extracted from the shared gradients. Through experiments, we demonstrate the feasibility and effectiveness of federated learning in energy forecasting, highlighting its potential to enhance prediction capabilities without compromising privacy.

关键词： Training Energy consumption Accuracy Federated learning Computational modeling computer architecture Production Predictive models Data models Servers

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Augmenting Data Analysis Through Innovative Visualization Techniques

Augmenting Data Analysis Through Innovative Visualization Te...

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International Conference on Futuristic Technologies (INCOFT)

作者： Uma Rani K Omkaresh S. Kulkarn Ravi Kant Pareek Trapty Agrawal R Murugan N.V. Balaji Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Civil Engineering Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore

This paper examines new and progressive methods in information visualization for analysis and selectionmaking. It explains various traits of records that might be made less burdensome to apprehend via visuals, which include graphs, pictures, and other visible representations. It highlights the strength of facts visualization to assist people in interpreting, advantage insights, and conclude from information, making them more green and powerful in their evaluation. The paper covers the significance of records visualization modern and growing techniques and examines how facts analysis and choice-making may be augmented using these stylish visuals. Researchers can use statistics visualization techniques to reach broader audiences and help them understand important information more correctly.

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Data Integrity and Cost Efficiency in Smart Grids: Block-chain Integration Approach

Data Integrity and Cost Efficiency in Smart Grids: Block-cha...

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Advanced Computing and Emerging Technologies (ACET), International Conference on

作者： Chodagam Srinivas D M Vigna Priya M V S P Sagar Kakumanu V V Nagendra Babu A. Senthilkumar A. V. Pavan Kumar Department of EEE Madanapalle Institute of Technology & Science Andhra Pradesh India Department of CSE(AI&ML) Gayatri Vidya Parishad for Degree and PG Courses (E&T) Visakhapatnam Andhra Pradesh Department of EEE BITS Vizag(A) Visakhapatnam Andhra Pradesh Department of CSE-AIDS & AIM Bonam Venkata Chalamayya Institute of Technology Andhra Pradesh India Department of EEE M.A.M. School of Engineering Trichirappalli India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

This paper presents a novel approach to minimize power generation costs by harnessing the potential of an adaptive BAT algorithm synergistically integrated with block-chain technology, particularly Proof of Work (PoW). The focus is on optimizing power generation in complex electrical grids, exemplified through experimentation on the IEEE 14 bus system and the IEEE 30 bus system. By leveraging the adaptability of the BAT algorithm and the decentralized ledger capabilities of block-chain technology, the proposed methodology offers a robust solution to the power generation cost minimization conundrum. Through extensive testing and validation, the efficacy of the approach is demonstrated, showcasing significant improvements over existing optimization techniques. Furthermore, the comparative analysis reveals that the proposed approach consistently outperforms conventional methods in terms of efficiency and effectiveness. The results underscore the transformative potential of integrating advanced optimization algorithms with block-chain technology in addressing critical challenges within the energy sector.

关键词： Costs Proof of Work Power system reliability Consensus protocol Smart grids Reliability Optimization Power generation Standards Genetic algorithms

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HEECCNB: An Efficient IoT-Cloud Architecture for Secure Patient Data Transmission and Accurate Disease Prediction in Healthcare Systems

HEECCNB: An Efficient IoT-Cloud Architecture for Secure Pati...

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International Conference on Image Information Processing

作者： C. Veena Mulagundla Sridevi Kazi Kutubuddin Sayyad Liyakat Bishal Saha Sheri Ramchandra Reddy N Shirisha Artificial intelligence (AI) Dept G.Pullaiah College of engineering and technology (GPCET Autonomous) Venkayapallu(V) Nandikotkur road Kurnool (city) Andhra Pradesh India Department of CSE CVR College of Engineering Vasthunagar Mangalpally Ibrahimpatnam R.R District Telangana India Department of Electrical Engineering Brahmdevdada Mane Institute of Technology Solapur Maharashtra India Department of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of CSE (AI&ML) CMR Technical Campus Hyderabad Telangana India Department of CSE MLR Institute of Technology Dundigal Hyderabad India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

The rapid expansion of the Internet of Things (IoT) in various fields has brought about significant advancements in data processing capabilities, in particular with the advent of IoT-cloud-based devices. Among these advancements, the healthcare system stands as one of the most intriguing IoT-Cloud integration uses cases. However, ensuring the security and privacy of patient data has become a critical concern, necessitating the development of robust protective measures. This research addresses the challenges of data security and computation overhead in the context of an IoT-cloud-based health system. A novel strategy dubbed Homomorphic Encryption with Elliptical Curve Cryptography integrated with Naive Bayes (HEECCNB) is put forth to address these problems. This approach is intended to enable precise disease prediction based on the gathered IoT sensor data while simultaneously providing good patient data privacy inside the IoT Health Cloud system. By employing homomorphic encryption, sensitive patient information can be securely processed and analyzed within the cloud environment without the need for decryption, preserving the confidentiality of the data. Furthermore, the integration of Elliptical Curve Cryptography enhances the security of the system by providing efficient and robust cryptographic operations. The proposed HEECCNB approach demonstrates promising results in both data protection and disease prediction. Through experiments conducted on the IoT Health Cloud scheme, the research validates the effectiveness of the projected method in safeguarding patient data and accurately predicting diseases based on IoT sensor data. Hence, the HEECCNB approach presents a compelling solution to the data security and computation overhead challenges faced in IoT-cloud-based health systems. Its successful implementation could significantly enhance the overall functionality and trustworthiness of such systems, facilitating the integration of IoT technologies in healthcare fo

关键词： Cloud computing Data privacy Medical services computer architecture Predictive models Prediction algorithms Internet of Things

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Deep Neural Network-Based Fingerprint Reformation for Minimizing Displacement

Deep Neural Network-Based Fingerprint Reformation for Minimi...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Munish Kumar Sandeep Kumar Monali Gulhane Rajender Kumar Beniwal Nitin Shilpha Choudhary Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of CSE Symbiosis Institute of Technology (SIT) Nagpur Symbiosis International (Deemed University) (SIU) Pune India Electrical and Electronics Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Computer Engg. Govt. Polytechnic Hisar Haryana Department of Computer Science and Engineering(AI & ML) Neil Gogte Institute of Technology Hyderabad India

In the realm of biometric authentication and forensic analysis, the accurate matching of fingerprints is paramount. Fingerprint displacement, caused by factors such as skin elasticity and pressure during touch, has been a significant challenge in achieving precise fingerprint recognition both during the enrollment and the authentication process. This research presents a technique to address this issue by leveraging Support Vector Machines (SVM) for fingerprint transformation and displacement minimization. The proposed methodology involves the extraction of distinctive fingerprint features and the application of SVM-based algorithms to realign and correct fingerprint distortions resulting from displacement. We analyze the effectiveness of SVM in reducing displacement-induced errors and improving matching accuracy. Experimental results demonstrate the potential of SVM-based fingerprint transformation techniques to significantly enhance the robustness and reliability of fingerprint recognition systems. This research contributes to the ongoing efforts in biometrics and forensic science by providing a practical solution to the problem of fingerprint displacement, with potential applications in identity verification, criminal investigations, and security systems. The findings of this study offer a promising avenue for further research and development in the field of biometric authentication, ultimately leading to more accurate and secure identification methods.

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Deep Learning Technology Used High Dynamic Range Image Reconstruction And Detection

Deep Learning Technology Used High Dynamic Range Image Recon...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： M. Bheemalingaiah R. Srinivasan V. Vijey Nathan Raju Kolluri Amit Gupta Department of CSE J.B Institute of Engineering and Technology Hyderabad Telangana Department of CSE School of Computer Science and Engineering Galgotia University U.P India Department of CSE SRM TRP Engineering College Tiruchirappalli Tamilnadu India Department of ECE Narasaraopet Engineering College Narasaraopet Andhra Pradesh India Dep. of AI & ML J.B Institute of Engineering and Technology Hyderabad Telangana

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The suggested remedy for saturated pixels in digital camera photos introduces a brand-new learning-based method for reconstructing images with high dynamic range (HDR). While current techniques concentrate on increasing brightness range, they frequently fall short in producing realistic textures, leading to errors in places that are too saturated. Our method reconstructs an HDR image by using aesthetically beautiful saturated pixels from a low dynamic range (LDR) image. We provide a feature masking strategy to address problems discovered during training, when saturated and well-exposed pixels got comparable convolutional filters, resulting in uncertainty and checkerboard and halo aberrations. This method improves the overall quality of the reconstruction by reducing the contribution of features from saturated regions. Furthermore, in order to guarantee visually pleasing textures in the reconstructed HDR photos, we adjust the perceptual loss function based on *** use a two-step procedure in order to properly train our system. We first train the system for picture in painting on a sizable dataset, and then we modify it for HDR reconstruction. Our method performs exceptionally well even with a small number of HDR photos because it uses an advanced sampling strategy to choose hard training patches from a wide range of settings. Convolutional neural networks, feature masking, perceptual loss, and high dynamic range imagery are important components of our methodology.

关键词： Training Uncertainty Filters Image color analysis Supervised learning Digital cameras High dynamic range Convolutional neural networks Image reconstruction Painting

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Enhanced Sarcasm Detection using Grey Wolf Optimizer with Deep Learning on Social Media

Enhanced Sarcasm Detection using Grey Wolf Optimizer with De...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： M. Manimaraboopathy Rajeshwari D R. Rathiya C B Selva Lakshmi V. Lathajothi Sangeethaa SN Electronics and communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Dept. Of Information science & Engineering The National Institute of Engineering Mysuru Department of Information Technology Dr NGP Institute of Technology Coimbatore CSE Velammal College of engineering and technology Dept. of CSE (AI & ML) Velalar College of Engineering and Technology Thindal Erode Computer Science & Engineering Bannari Amman Institute of Technology

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

A linguistic expression, Sarcasm generally conveys the opposite meaning of what had been said in words, which makes it a compplex process for the machines to find out the real meaning. It is mainly depends on the context, making it hard for computation task and is largely defined by the inflexion with which it is spoken, with suggestion of irony. Furthermore, sarcasm express the negative sentiments using positive word, enabling it to confuse sentiment analysis (SA) model. Sarcasm detection is a crucial challenge in natural language processing (NLP), which is required for good understanding to serve as an interface for mutual interaction between humans and machines. This article develops an Enhanced Sarcasm Detection using Grey Wolf Optimizer with Deep Learning (ESD-GWODL) technique on social media. The major intention of the ESD-GWODL method is to detect and classify sarcasm on social media. To accomplish this, the ESD-GWODL technique applies data pre-processing to transform the input data into relevant format. Besides, TF-IDF model can be used to extract word embedding. To detect and categorize sarcastic texts, the ESD-GWODL technique applies long short-term memory (LSTM) model. The ESD-GWODL technique utilizes GWO algorithm for hyperparameter tuning purposes in order to boost the sarcastic text detection. The experimental evaluation of the ESD-GWODL technique is tested using sarcasm dataset. The obtained values infer that the ESD-GWODL technique has resulted in boosted detection results.

关键词： Deep learning Sentiment analysis Social networking (online) Text detection Transforms Linguistics Feature extraction Internet of Things Tuning Long short term memory

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