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Multi-Agent Systems for Collaborative intelligence (ICMSCI), International Conference on

作者： Revathi K S. Priyanka Priyadharshini S Niranjan NN Visalachi N P. Sumathi Department of Information Technology SNS College of Engineering Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamil Nadu India Department of Information Technology & Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Mental health issues affected millions of peoples in worldwide, who often struggle with stigma, high costs, and low access to real life care. Conventional therapy methods faced lot of challenges to identify the issues. These challenges need to highlight for more accessible, affordable, and effective mental health solutions. Traditional models of care often fall short of providing adequate support to all those in need, leading to gaps in care and disparities in mental health outcomes. So, need for Innovation in Mental Health Support for effective mental health solutions to address these challenges. Introduction to “InnerPeaceAI” Aims to provide personalized, on-demand therapy using advanced AI technology, offering a confidential and user-friendly platform to improve mental health care accessibility. InnerPeaceAI aims to bridge the gap between the need for mental health care and its accessibility by leveraging advanced AI technology to deliver therapy sessions tailored to individual needs. The platform uses natural language processing and machine learning algorithms to create personalized treatment plans, identify mental health issues, and provide real-time support to users. This innovative approach allows for immediate access to therapy, without the constraints of time, location, or cost.

关键词： Technological innovation Costs Machine learning algorithms Medical treatment Mental health Depression Real-time systems Natural language processing Time factors artificial intelligence

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White Blood Cells Classification Model using Automatic Soft Computing

White Blood Cells Classification Model using Automatic Soft ...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Mahale, Pragati Prashant Deshmukh, Rakesh K. Pise, Priya Rangari, Sudhir Ramrao Kalinga University Raipur Department of Information Technology AISSMS's Institute of Information Technology Pune India Kalinga University Raipur India Indira College of Engineering and Management Artificial Intelligence and Data Science Pune India JSPM'S Jayawantrao Sawant College of Engineering Information Technology Department Pune411028 India

ISBN: (纸本)9798350361537

Accurate classification of blood cells, especially WBCs, is crucial for disease diagnosis and management. Traditional optical microscopic imaging provides limited information, but hyperspectral imaging (combining spatial and spectral data) offers richer insights. There are challenges and opportunities in white blood cell (WBC) classification using both Convolutional Neural Networks (CNN) including the following: (1) Important data, such as representative characteristics at various frequencies and orientations, may be lost due to the CNN model's usage of local receptive fields, shared weights, and subsamples. (2) To avoid over fitting, deep neural networks need a large pool of labeled training models. and (3) the higher computational overhead is yet another challenge. Therefore, these challenges motivate us the need of the proposed study. The scope of the proposed study is limited to the classification of WBC cells. Framework to classify and count the leukocytes in microscopic images and developed framework supports the pathologist and boosts the CAD system accuracy. The process of analyzing leukocytes in microscopic blood images is the major concern of this research. In this thesis, first we work on WBCs segmentation from microscopic blood images. © 2024 IEEE.

关键词： Convolutional neural networks

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Revolutionising Chrono Nutrition Using AI: A Review for Personalized Dietary Recommendation

Revolutionising Chrono Nutrition Using AI: A Review for Pers...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Vidhi Wankhede Meher Langote Prateek Verma Artificial Intelligence and Data Science Faculty of Engineering And Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi(M) Wardha India Artificial Intelligence and Machine Learning Faculty of Engineering And Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi(M) Wardha India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Chrono nutrition is the science of analysing foods' effects and consumption patterns on the outer-infra-clock organism's circadian rhythms. As such, it can provide valuable possibilities for tailored health promotion and disease prevention efforts. With artificial intelligence (AI), there is an opportunity to change dietary advice and make it accurate, dynamic and personalised. This review discusses how AI can improve chrononutrition, analyse large datasets, determine the optimal circadian feeding intervals, and deliver individualised interventions based on chronobiological and genetic data. Limitations include the absence of cross-sectional analysis and inadequate consideration of socio-cultural factors in AI models. AI can significantly contribute to optimising chronotherapeutic or chrono-dietary interventions in metabolic health and disease. Furthermore, a multisectoral approach in which nutritionists, data scientists, and other actors in the healthcare sector must work together is crucial within the present state and future practical application of these advances. In extending the discourse of this paper, it is also essential to establish that much more work needs to be done for AI to make a tangible difference in chronic nutrition and translate it into an optimal solution to the problem of practical dietary advice and disease prevention.

关键词： Analytical models Sentiment analysis Reviews Prevention and mitigation Collaboration Medical treatment Genetics Synchronization artificial intelligence Diseases

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End-to-End Congestion Avoidance Scheme for Underwater High Speed Communication Networks 3

End-to-End Congestion Avoidance Scheme for Underwater High S...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Shivank Jain, Romil Reena, R. Kadam, Prema S. Das, Sunanda Singh, Swati Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

The cease-to-give up Congestion Avoidance Scheme for Underwater high pace verbal exchange Networks, commonly known as E2E-UCAN, is a critical technique for lowering plausible throughput by using efficiently warding off congestion inside a community. E2E-UCAN is a disbursed congestion control mechanism which uses statistics obtained from cease-to-end feedbacks to regulate sending prices on the give up systems. By means of monitoring the differences among expected network overall performance and actual performance, E2E-UCAN is able to pick out congestion stages inside the device, and is therefore able to regulate to prevent congestion before it happens. This enables to improve the stableness of the community and to increase the throughput of a network. Moreover, E2E-UCAN is likewise capable of efficaciously coping with queuing, avoiding packet loss in non-uniform networks, and in preferred, permitting applications to maximize performance. © 2023 IEEE.

关键词： Community Congestion Lowering Mechanism Performance

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Introducing the Hyperdynamic Adaptive Learning Fusion (HALF) model for superior predictive analytics in E-learning

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Neural Computing and Applications 2025年 1-21页

作者： Islam, Umar Alali, Ibrahim Khalil Alotaibi, Shoayee Dlaim Alzaid, Zaid Shah, Babar Ali, Ijaz Moreira, Fernando Department of Computer Science IQRA National University Swat Campus Peshawar Pakistan Department of Instructional Technology Jouf University Sakaka Saudi Arabia Department of Artificial Intelligence and Data Science University of Hail Hail Saudi Arabia Faculty of Computer Science and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia College of Technological Innovation Zayed University Dubai United Arab Emirates REMIT Portucalense University & IEETA Aveiro University Aveiro Portugal

Today’s complex world is defined by digital changes in educational paradigms to which E-learning has contributed significantly, and as such, accurate prediction methods are needed for student performance modeling. In this paper a new and complex model is proposed, namely the Hyperdynamic Adaptive Learning Fusion (HALF) model that leverages adaptive computing paradigms and artificial intelligence to build a fusion of learning that adapts to the new learning patterns. Many conventional predictive models employ linear and simplistic relationships to predict an outcome from an input;hence, they fail to decompose complex and heterogenic data patterns of educational data and also suffer from scalability issues for dealing with large volumes of data. To overcome these issues, the HALF model employs the relevant ensemble learning algorithms that consist of bagging, boosting, and an innovative adaptive fusion strategy that integrates base and adaptive models to achieve higher accuracy and resilience in the latter. In doing so, and by adopting the scientific method of working on trials and errors and rigorous assessment employing a database derived from the Open University VLE, the investigation presented in this paper provides compelling evidence of the HALF model’s superior efficacy, which yields an accuracy of 87%. 2%, precision of 85. 4% It has been proved that 3% of all students have significant learning disabilities, while the recall value is 89. 1%, surpassing traditional methods. The model’s equation can be easily applied to any variety of courses and of students, which makes it highly beneficial to educators and administrators;at the same time, the model is highly interpretable. Therefore, HALF model proves to be a revolutionary addition to the current kind of statistical modeling in E-learning that depicts student engagement pattern into more precise and accurate form, reduce biases in all way possible and provides solution that might help to improve the course outc

关键词： Contrastive Learning

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An Empirical Study on: Time Series Forecasting of Amazon Stock Prices using Neural Networks LSTM and GAN models 23

An Empirical Study on: Time Series Forecasting of Amazon Sto...

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Proceedings of the 5th International Conference on Information Management & Machine intelligence

作者： Anjul Bhardwaj Uday Pratap Singh Artificial Intelligence & Data Science Poornima Institute of Engineering & Technology India Department of AI & DS Poornima Institute of Engineering and Technology India

ISBN: (纸本)9798400709418

The OHLCV (Open, High, Low, Close, Volume) data used in this study is used to forecast time series and anticipate stock price movement. We investigate a wide variety of models, including traditional statistical approaches and cutting-edge deep learning strategies combined with sentiment analysis, feature extraction, and hyperparameter tweaking. Instead of focusing on absolute stock prices, our main goal is to predict swings in stock prices, as this has been shown to produce more accurate outcomes. We start our research by obtaining historical Amazon stock data via the Yahoo API, and then we go on a thorough analytical journey. We generate features on the OHLCV data first, and then we design and test Fourier and Autoregressive Integrated Moving Average (ARIMA) models. We then switch to more sophisticated deep learning methods, using the pre-processed data to apply Long Short-Term Memory (LSTM) models. Interestingly, we add sentiment analysis to the LSTM study, which expands its scope and lets us consider market sentiment as a possible influencing factor. To guarantee the stability of our models, we use a careful train-test split technique and organize the data in a time series manner. The field of financial forecasting and trading methods will ultimately benefit from the insightful information our study's findings provide on the efficacy of different modeling techniques and their capacity to forecast stock price movements.

关键词： ARIMA (Autoregressive Integrated Moving Average)

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Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

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IEEE Transactions on Computational Social Systems 2023年第3期10卷 910-921页

作者： Deng, Ping Li, Tianrui Wang, Hongjun Wang, Dexian Horng, Shi-Jinn Liu, Rui Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Computing and Artificial Intelligence Chengdu611756 China National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan North Sichuan Medical College School of Preclinical Medicine Department of Computer and Mathematics Nanchong637100 China

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed;and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance. © 2014 IEEE.

关键词： Matrix algebra

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Integrating Deep Learning and data science: Challenges, Opportunities and Implications 15

Integrating Deep Learning and Data Science: Challenges, Oppo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Agarwal, Ankita Patil, Nagaraj Rajeswari, M. Yadav, Gitanjali B. Mishra, Rahul Sood, Gourav Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Deep gaining knowledge of and records science are the most important and surprisingly influential fields of technology. Deep studying can system extensive statistics to discover styles, create insights, and have their personal choices concerning further analysis. Facts and technological know-how are centered on information-pushed business selection-making, using predictive analytics, machine studying, and other more recent algorithms to perceive, refine, and increase intelligent choices based on information. Those fields are rapidly developing and overlapping, creating an ever-increasing demand for each technical professionals and people with an intensive knowledge of statistics science and deep mastering. Integrating deep gaining knowledge of and records technology can create some challenges and possibilities which have to be explored. © 2024 IEEE.

关键词： Contrastive Learning

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New extended sidon spaces with dimension k+1 8

New extended sidon spaces with dimension k+1

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2024 8th International Conference on Mechanics, Mathematics and Applied Physics, ICMMAP 2024

作者： Wu, Lingling Li, Yu Fang, Lin Niu, Yongfeng College of Electrical Engineering Changzhou Vocational Institute of Mechatronic Technology Changzhou213164 China School of Computer Science and Artificial Intelligence and Aliyun School of Big Data Changzhou University Changzhou213164 China China Telecom Anhui Branch Hefei230071 China

In recent years, Sidon spaces have gained great popularity and applications in various fields such as multi-antenna communication systems, distributed storage systems, multimedia transmission, and network coding. With our further research into this field, it becomes evident that Sidon spaces are crucial in the field of constructing cyclic subspace codes. Currently, lots of papers focuse on how to construct Sidon spaces. Researchers find that through using the roots of irreducible polynomials and primitive elements over finite fields, we can sucessfully construct them. Based on the significant research conducted by previous scholars, we obtain several Sidon spaces which have new cardinality. Moreover, these Sidon spaces is beneficial to help us obtain better-performance subspace codes. © Published under licence by IOP Publishing Ltd.

关键词： Network coding

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Identity-Based Access Control in IoT: Enhancing Security through Mutual Cryptographic Authentication and Context Awareness 3

Identity-Based Access Control in IoT: Enhancing Security thr...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： More, Priyanka Sakhare, Sachin Mahalle, Parikshit Smt. Kashibai Navale College of Engineering Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350317022

The proposed methodology strengthens security and privacy in IoT networks through mutual cryptographic authentication, employing Elliptic Curve Cryptography, Diffe Hellman for key exchange, and encryption methods for robust identity verification. Context awareness, considering factors like device type, communication type, successful transfers, and device behaviour, enhances access control decisions dynamically. This approach bolsters network security, defending against unauthorized access and potential threats. Identity-based access control streamlines access rights management, dynamically setting permissions for scalable network expansion. Combining cryptographic authentication and context sensitivity, this methodology emerges as a potent tool for securing IoT networks, ensuring data integrity, and preserving user privacy. Its versatility makes it applicable across diverse loT scenarios, offering a flexible and scalable access control solution. Overall, devices get authenticated with each other through identity based on clusters formed by context parameters and access rights are set to the cluster head. The cluster head is dynamically updated from time to time considering the context parameters. © 2023 IEEE.

关键词： context clustering decryption Elliptic Curve Cryptography encryption identity-based access control

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