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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Poonam Shrivastava Neeta Tripathi Bhupesh Kumar Dewangan Bikesh Kumar Singh Tanupriya Choudhury Ketan Kotecha Sanjana Dewangan Department of Electronics & Telecommunication SSTC Bhilai Chhattisgarh India Department of Computer Science and Engineering School of Engineering OP Jindal University Raigarh India Department of Biomedical Engineering National Institute of Technology Raipur Chhattisgarh India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India School of Science OP Jindal University Raigarh India

Respiratory disorders (RD) are common health problems, and they represent a huge health burden to people around the world. Nowadays, speech analysis is a very important research area in the biomedical field. Therefore, speech analysis can be a useful tool for pre-diagnosing of RD. This review paper aimed to select the features of speech affected by RD and to define autonomic computing in terms of self-care and self-management in disease assessment using the machine learning (ML) paradigm. Selected articles in this work have been published in the last decade and are written in the English language.722 articles in total were obtained, of which 15 were chosen based on the meta-analyses (PRISMA) approach, and for systematic literature reviews (SLR), the reporting items were preferred. Articles were compiled by searching different databases such as Google Scholar, Crossref, and PubMed. This SLR mainly contributed to identifying the role of autonomic computing in the application of self-management for the assessment of RD in ML classifiers. It was observed that most of the research work on respiratory disorders with self-management applying speech analysis was done during the year 2012. Then gradually less work was done in this area until 2018 and in 2019, it increased again and stayed finally stable until 2022. It was concluded that the important information provided by this SLR is efficient model to detect and characterize RDs by applying speech parameters using autonomic computing.

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artificial intelligence without Restriction Surpassing Human intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain

arXiv

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arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

artificial intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundament

关键词： Neurology

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A Review Analysis: Comparative Study On Various Machine Learning Techniques for Load Forecasting In Electric Power Distribution System with Multiprocessing

A Review Analysis: Comparative Study On Various Machine Lear...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Anjali Naudiyal Kapil Joshi Ajay Singh Gunjan Chhabra Harishchander Anandaram Anil Kumar School of Applied and Life Sciences Uttaranchal University Dehradun Premnagar India Computer Science and Engineerig Uttaranchal University Premnagar Dehradun India Deptt. of CSE Graphic Era Hill University Dehradun Centre for Computational Engineering and Networking Amrita School of Artificial Intelligence Coimbatore Amrita Vishwa Vidyapeetham India School of Computing DIT University Dehradun India

Contemporary electricity usage is rising every day, making it more challenging to predict it in advance. Grids have been replaced with smart meters to provide electricity estimation. Industrial, residential, and commercial loads are primarily calculated load in these three sectors. Electricity was simple to estimate as long as the consumption was minimal, but as consumption has increased, electricity has now evolved into a dataset. Using past performance and probabilistic data, load forecasting is used to forecast future outcomes. Three categories exist for load forecasting where first is long-term load forecasting which is also known as LTLF, predicted to last for three to five years; second is medium-term load forecasting which is also known as MTLF, predicted to last for three months to three years; and third is short-term load forecasting which is also known as STLF, predicted to one day to six weeks are covered under the predicting period. Several variables, like the weather, the rate of expansion, and new clients, have an impact on STLF. In this study, machine learning methods are used to analyze data from smart meters related to energy consumption. This study illustrates how accurate future forecasts of electricity consumption or production may be produced using machine learning techniques by contrasting the multiprocessing machine learning techniques used in STLF with data variables.

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A Intelligent Approach for Fault Detection in Solar Photovoltaic Systems based on BERT-BiGRU Network

A Intelligent Approach for Fault Detection in Solar Photovol...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Mohammed Hafeezuddin Bhagath Garapati T Vijayakumar Priyanka G Amit Chauhan A. Vidya Department of EEE Geethanjali College of Engineering and Technology Hyderabad India Department of EEE Geethanjali College of Engineering and Technology Cheeryal Telangana India Department of Artificial Intelligence and Data Science Dr.Mahalingam College of Engineering and Technology Coimbatore India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Life Sciences School of Sciences CHRIST (Deemed to be university) Bengaluru Karnataka India Department of Electronics and Communication Engineering K.Ramakrishnan College of Engineering Trichy India

Large-scale photovoltaic (PV) plant problem identification and diagnosis is expected to grow more difficult in the future as more and more plants of increasing capacity enter into existence. To keep large-scale PV installations safe, reliable, and productive, automatic identification and localization of any mal-operation among thousands of PV modules is necessary. In order to identify problems in PV plants, the suggested method compares the ‘residuals' (fault indicator signals) generated by each string to a predetermined threshold. The suggested method relies on three distinct processes: data preparation, feature extraction, and model training. Preprocessing employs the method of Transform Invariant Low-rank Textures (TILT). The most useful and efficient measurements from feature extraction are kept while less important ones are discarded in favor of the Reduced Kernel PCA technique. Let's move on to model training with BERT-BiGRU. The proposed method is clearly superior compared to the two leading options, BERT and GRU. The proposed method had a 97.36% success rate.

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Cybersecurity Threats in FinTech: A Systematic Review

arXiv

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arXiv 2023年

作者： Javaheri, Danial Fahmideh, Mahdi Chizari, Hassan Lalbakhsh, Pooia Hur, Junbeom Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of School of Business University of Southern Queensland SpringfieldQLD4300 Australia School of Business Computing and Social Sciences University of Gloucestershire The Park CampusCheltenham GloucesterGL50 2RH United Kingdom Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University ClaytonVIC3168 Australia

The rapid evolution of the Smart-everything movement and artificial intelligence (AI) advancements have given rise to sophisticated cyber threats that traditional methods cannot counteract. Cyber threats are extremely critical in financial technology (FinTech) as a data-centric sector expected to provide 24/7 services. This paper introduces a novel and refined taxonomy of security threats in FinTech and conducts a comprehensive systematic review of defensive strategies. Through PRISMA methodology applied to 74 selected studies and topic modeling, we identified 11 central cyber threats, with 43 papers detailing them, and pinpointed 9 corresponding defense strategies, as covered in 31 papers. This in-depth analysis offers invaluable insights for stakeholders ranging from banks and enterprises to global governmental bodies, highlighting both the current challenges in FinTech and effective countermeasures, as well as directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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Ethical Considerations in Explainable AI: Balancing Transparency and User Privacy in English Language-based Virtual Assistants

Ethical Considerations in Explainable AI: Balancing Transpar...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Franciskus Antonius Alijoyo S. Sandya Sneha Sri Purnachandra Rao Alapati Dilyorjon Yuldashev Ponni Valavan M Center for Risk Management and Sustainability Indonesia School of Business and Information Technology STMIK LIKMI Bandung Indonesia Krishna University Andhra Pradesh Prasad V Potluri Siddhartha Institute of Technology Kanuru Vijayawada Andhra Pradesh India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy Tamil Nadu India

English Language-Based Virtual Assistants (ELB-VAs) are AI-powered systems designed to comprehend and respond to user queries in the English language, exemplified by virtual assistants like Siri or Alexa. The need for balancing transparency and user privacy in ELB-VAs is paramount due to their pervasive integration into daily life. Ensuring transparency imbues user trust, while safeguarding privacy addresses ethical concerns associated with personal data. Existing methods involve clear privacy policies, user-controlled data sharing settings, and encryption. However, drawbacks include user confusion and potential biases. To address these limitations, this study proposes a novel approach. Methodologically, it integrates pre-processing techniques such as lowercasing and tokenization, coupled with a Natural Language Understanding model. This model undergoes intent and entity recognition training, enhancing accuracy, and incorporates privacy-aware response generation, ensuring informative yet privacy-conscious interactions. The implementation of the study's results is carried out using Python tools, showcasing improved metrics and response times. This approach contributes to a more transparent and privacy-respecting user experience, aligning with evolving ethical norms and setting the stage for advancements in ELB-VA technology. This comprehensive exploration bridges existing gaps, emphasizing the ethical imperative of user-centric and privacy-aware AI interactions in ELB- VAs. The proposed NLU model exhibits a substantial increase in accuracy compared to other methods, with an impressive accuracy value of 99.1 % • On average, it outperforms the Random Forest and Decision Tree models by 15.7 percentage points, highlighting its superior predictive capabilities in the evaluated task. This comprehensive exploration aligns with evolving ethical norms and establishes a foundation for future advancements in ELB-VA technology.

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Development of Elliptical Cryptography Technique to Watermark Embedded and Extrusion for Healthcare Records 2nd

Development of Elliptical Cryptography Technique to Watermar...

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2nd International Conference on Cyber Warfare, Security and Space Computing, SpacSec 2024

作者： Hegde, N. Prajwal Sivaraman, R. Dharmamoorthy, G. Dubey, Ankit Kumar Patro, Pramoda Mary, S. Suma Christal Department of Artificial Intelligence and Data Science NMAM Institute of Technology Nitte Deemed to be University Karkala India Department of Mathematics Dwaraka Doss Goverdhan Doss Vaishnav College Arumbakkam Tamil Nadu Chennai India Department of Pharmaceutical Analysis MB School of Pharmaceutical Sciences Mohan Babu University Tirupati Rangampeta India Department of Computer Science and Engineering Baderia Global Institute of Engineering and Management MP Jabalpur India Department of Mathematics Koneru Lakshmaiah Education Foundation Hyderabad India Department of Information Technology Panimalar Engineering College Poonamalle Chennai India

ISBN: (纸本)9783031734939

An application of E-healthcare watermarking method based on a hybridization of encryption as well as compression algorithms is proposed as the primary goal of this work. One step involves inserting a watermark into the image, and the other involves removing it. These are the two main components of the suggested system. Before beginning the embedding procedure, use a region-growing method to segment the tumour independently. Hash Secure Algorithm-256 encrypt the electronic health record (EHR) and Elliptical curve cryptography and its associated area of interest. Mathematical coding algorithms are then used to concatenate and compress the data. Lastly, the packed down bit is embedded into the original picture. When extracting a substance, the same steps are conducted again. Using the maximum signal ratio to noisy and normalized correlation, the experimental outcomes for various medicinal photos using EHR are shown, and the efficacy of the suggested method is evaluated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Predictive Analytics for Proactive Maintenance in Industrial IoT Applications

Predictive Analytics for Proactive Maintenance in Industrial...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： A. Jaya Mabel Rani B. Yasotha Sevanthi P K. Karthika Y.Alexander Jeevanantham V.Samuthira Pandi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology KattankulathurChennai Tamil Nadu India Department of Artificial Intelligence and Data Science S.A.Engineering College (Autonomous) Thiruverkadu Chennai Tamil Nadu India Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamilnadu India Department of Electrical and Electronics Engineering RMK Engineering College Kavarapettai Chennai Tamil Nadu India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology(CAWit) Chennai Tamil Nadu India

Industrial Internet of Things (IIoT) enabled predictive analytics have enabled the manufacturing sector to transition from reactive to proactive maintenance. This article discusses the use of predictive analytics in the IIoT field of preventive maintenance. The objectives consist of increased output, availability, and decreased malfunctions-research on preventive maintenance cast light on its evolution. The proposed work presents a framework for IIoT data acquisition, cleansing, feature selection, and analytical processing. Experiments and case studies validate the methodology. Utilizing predictive analytics for routine maintenance can improve the effectiveness and productivity of machinery. data integrity, model consistency, and scalability must be resolved before implementation. The proposed work's findings and recommendations have significant implications for future research into predictive analytics for proactive maintenance in IIoT systems and for industry practitioners.

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Remodeling Control with Filter in Collaboration among Isolated Robot Systems with Force Feedback

Remodeling Control with Filter in Collaboration among Isolat...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Mohanavel, V. Ravichandran, M. Paul, Chayan Diwakaran, S. Sajiv, G. Bharath Institute of Higher Education and Research Centre for Materials Engineering and Regenerative Medicine Tamil Nadu Chennai600073 India Chandigarh University Department of Mechanical Engineering Punjab Mohali140413 India K. Ramakrishnan College of Engineering Department of Mechanical Engineering Tamil Nadu Trichy621 112 India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science A.P. Vaddeswaram India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Srivilliputhur626128 India SIMATS Saveetha School of Engineering Department of ECE Chennai602105 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Autonomous robot systems incorporating control action have been the subject of much investigation. Developing autonomous robot systems incorporating motion sensors, particularly for collaboration across multiple frameworks, has a great deal of promise in the future. Autonomous robot systems with motion sensors allow a user to interact with an attached robot that has a pressure gauge through a computer by utilizing a tactile interaction equipment, while simultaneously watching the robot's movements with a recording device, as shown in Figure 1. In this research, we conduct an experiment to compare three different forms of stabilization management for teamwork amongst distant robot systems that use control action. A tactile interaction device is used in the method to allow a user to handle a distant automated machine equipped with a strain gauge while simultaneously seeing video. The three forms of stabilization management are response pressure management upon impact, thickness control during stabilization, and filtration control during stabilization. For the sake of our investigation, the user utilizes both technologies and interacts with tasks wherein the two robots must cooperate together to move an item. Within every system, we apply the three methods of stabilization control and determine which management is the most successful by comparing the results. © 2022 IEEE.

关键词： Stabilization

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Blockchain-Based Homomorphic Transaction Framework for Enhanced Consumer Security and Business Scalability

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IEEE Transactions on Consumer Electronics 2024年

作者： Guo, Tao Shang, Fengjun Dai, Xiangguang Liu, Qilie Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing400065 China Chongqing Three Gorges University Key Lab. of Intelligent Info. Proc. and Contr. of Chongqing Munic. Institutions of Higher Education Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology School of Three Gorges Artificial Intelligence Chongqing400044 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China

Energy trading in distributed microgrids represents an effective means of enhancing the utilization of renewable energy. However, the aggregation of large-scale consumption data may encounter business scalability issues and potential privacy exposure concerns, necessitating the development of novel secure and scalable frameworks for integrating consumption demands between prosumer and microgrid entities. we research focuses on providing a user-friendly transaction model for consumers and microgrid operators. Firstly, a privacy aggregation scheme for user resource requests is designed to ensure the security and decentralization of user data. In addition, the model employs a bidirectional heterogeneous resource allocation mechanism to support the optimal allocation of multiple resources, such as renewable energy, power assets, energy storage devices, and carbon credits, among different microgrids. Finally, a microgrid transaction prototype has been constructed on the Truffle development framework. In this context, smart contracts has been designed to ensure that the transactions are carried out automatically. Experimental results indicate that the scheme provides enhanced security for multi-resource trading in a cluster context relative to existing methodologies. The number of transactions increases linearly with the resource overhead, resulting in more efficient utilization of energy trading. Furthermore, the framework's scalability has been demonstrated through both temporal and spatial considerations. © 1975-2011 IEEE.

关键词： Decentralized finance

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