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

作者： T. Kalpana R. Thamilselvan K. Chitra S. Priyanka R.Reshmi Jahaan T. Thenmalar Department of Computer Application Kongu Engineering College Erode Department of Computer Science and Engineering Kongu Engineering College Erode

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Parkinson disorder is a neurological disease that progresses gradually which is typified by the brain's dopamine-producing neurons becoming exhausted. This neuronal loss results by symptoms including tremors, muscle stiffness, bradykinesia, and balance difficulties. Early detection and accurate prediction of PD progression are especially crucial for improving management and intervention strategies. This study builds a prediction model with the use of MRI datasets. To detect Parkinson's disease from MRI data, this study uses deep learning algorithms, such as Inception, Xception, and VGG19, and evaluates how well they perform in terms of accuracy.

关键词： Deep learning Neurological diseases Accuracy Parkinson's disease Magnetic resonance imaging Neurons Predictive models Muscles Prediction algorithms Predictive analytics

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An Effective Prediction of Rainfall Using Machine Learning Technique 5

An Effective Prediction of Rainfall Using Machine Learning T...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Rana, Nisha Guru Prasad, M. Sharma, Ashutosh Pratap, M.S. Anand Kumar, M. Aditya Pai, H. Dept. of Computer Science and Engineering Rishikesh India Graphic Era Deemed University Dept. Computer Science and Engineering Dehradun India Rishikesh India School of Computing and Information Technology Reva University Bengaluru India Dept. of Computer Application Dehradun India Dept. of Computer Science and Engg. Dehradun India

ISBN: (纸本)9781665493604

Climatic information such as temperature, humidity, and precipitation are useful for agriculturists, businesses, researchers, and the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. In this study, factual approaches and machine learning methods are used to estimate and forecast meteorological parameters for the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. Approaches are used to forecast and estimate precipitation. Daily observations were used as part of the experiment. Validation of results using real-world data is used to check the accuracy of forecasting model experimentation. The experimental results show that distribution, correlation, line-plots, and neural networks perform well for forecasting meteorological characteristics and have the best classification accuracy when compared to other types of machine learning methods for precipitation forecasting. © 2023 IEEE.

关键词： Machine learning

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Security Attacks and Key Challenges in Blockchain Technology: A Survey 4th

Security Attacks and Key Challenges in Blockchain Technology...

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4th International Conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Patel, Rima Patel, Dharmendra Department of Computer Science & Engineering Faculty of Technology & Engineering Devang Patel Institute of Advance Technology and Research CHARUSAT Changa India Department of Computer Application Smt. Chandaben Mohanbhai Patel Institute of Computer Applications CHARUSAT Changa India

ISBN: (纸本)9789811998874

A blockchain is decentralized, distributed and public ledger, which is used to record the transaction across the number of clusters of computers. The record cannot be altered without an alternation in subsequent blocks. In the world of IT, the term Blockchain is not obscure. Despite tremendous applications in the field of information technology, finance and other industries, there are some challenges with Blockchain technology that need to be taken into consideration. In this paper, we have discussed the key challenges and issues with Blockchain technology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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A Comparative Study on Reinforcement Learning in Disease Prediction on Medical Data

A Comparative Study on Reinforcement Learning in Disease Pre...

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Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Nijana. V P. Selvi Rajendran Computer Application Department Hindustan Institute of Technology and Science Department of Computer Science and Engineering Hindustan Institute of Technology and Science

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Medical imaging has been used extensively in healthcare in recent years for a variety of purposes, including disease diagnosis, treatment planning, and tracking the course of an illness. These applications entail taking pictures of the afflicted organ using a variety of modalities. Image segmentation and classification is two important process performed in disease diagnostic application. Deep reinforcement learning approach applied in many gaming application. DRL provides the accurate result in many real world applications, so researchers pay attention in DRL in medical data that helps the physicians in treating the patient. This study focus on surveying the various applications such as Image registration, lesion localization, image segmentation, image classification and Landmark detection using deep reinforcement learning in healthcare. In addition, this study investigates on the various DRL algorithms (Qlearning, deep deterministic policy gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TDDDPG), Deep Q Network (DQN) and Soft Actor-Critic (SAC) in Alzheimer's disease prediction. Among the DRL methods the DDPG achieved the highest accuracy in detecting the Alzheimer's disease with 97%.

关键词： Location awareness Surveys Image segmentation Image registration Deep reinforcement learning Prediction algorithms Lesions

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Real-Time Data Streams: The Future of Predictive Maintenance in Manufacturing 3

Real-Time Data Streams: The Future of Predictive Maintenance...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Goyal, Manish Kumar Haripriya Singh, Akhilendra Pratap Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350343274

Dynamic Predictive Modeling is a method that has brought about a revolutionary change in the field of preventive maintenance in the industrial sector. Using the benefits of real-time data streams and flexible algorithms, this cutting-edge technology enhances predictive maintenance processes. In this investigation, we evaluate the efficacy of Dynamic Predictive Modeling to that of numerous more conventional methods, including vibration analysis, acoustic emission analysis, thermography, oil analysis, ultrasonic testing, and data analytics. Accuracy, resource optimization, adaptability, downtime reduction, cost-effectiveness, and preventative maintenance are just a few of the criteria we use to evaluate the methods. The findings indicate that Dynamic Predictive Modeling outperforms its predecessors across a range of performance metrics. It can better adapt to the ever-changing industrial environment, allowing for more precise and timely breakdown forecasts. The allocation of resources and the economy of operation are both enhanced by the fact that maintenance procedures may be adjusted according to the actual condition of the equipment. Predictive maintenance is the most effective method for minimizing unscheduled downtime and increasing output in heavy industry. In a comparison of several approaches to predictive maintenance, Dynamic Predictive Modeling comes out on top with the highest aggregate score. A study reveals how an unconventional approach might improve the effectiveness and reliability of industrial operations. Increased equipment reliability, decreased maintenance costs, and a potential advantage in the manufacturing industry might arise from the use of dynamic predictive modeling. © 2023 IEEE.

关键词： Cost effectiveness

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Loss Functions in Link Prediction Based on Graph Neural Networks 6

Loss Functions in Link Prediction Based on Graph Neural Netw...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sood, Vivek Rani, Sangeeta Rustagi, Tanvi Ghakkar, Garima Rastogi, Mitanshi World College of Technology and Management Department of Computer Science & Engineering Gurgaon India Dr. Akhilesh das Gupta Institute of Technology & Management Department of Computer Science & Engineering New Delhi India Vivekanand Institute of Professional Studies Department of Computer & Application New Delhi India

ISBN: (纸本)9798350304480

GNNs are neural networks that can be used to analyse graphs directly. We're going to discuss utilising GNNs for link prediction even though they can also be used for node-level tasks (like giving each website on the internet a score) and graph-level tasks (like predicting characteristics given a molecular structure). It is feasible to predict if a link will most likely connect two network nodes. Given the pervasiveness of networks, it has a wide range of uses, including reconstructing metabolic networks and recommending friends, movies, and knowledge graphs. To train these link prediction networks. We mostly use the binary categorical cross-entropy (CE) loss with adaptive moment estimation to train these link prediction networks. A loss function, cost function, or error function is a calculation formula that returns an event or the value of one or more variable to its true number, which in turn represents some 'costs'associated with this event. In this study, we evaluate the effects of various loss functions on the effectiveness of GNNs for link prediction with specified labels and various sample sizes. Our findings for GNNs demonstrate that in both of the aforementioned circumstances, there is no discernible difference between models trained using CE and other conventional loss functions. © 2023 IEEE.

关键词： Graph neural networks loss functions semi-supervised node classification

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A Data Commercialization Evaluation Model Based on Fuzzy Multi-attribute Group Decision Making 4

A Data Commercialization Evaluation Model Based on Fuzzy Mul...

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4th International Conference on Big Data Economy and Information Management, BDEIM 2023

作者： Zhao, Bin Cao, Li Gao, Yilong Zhai, Peng School of Information Science and Engineering Linyi University Shandong Linyi China School of Mathematics and Computer Application Technology Jining University Shandong Jining China

ISBN: (纸本)9798400716669

The core purpose of data commoditization transformation is to lower the threshold of data utilization and facilitate data analysis and mining at the user end. In this paper, we propose a data commoditization degree evaluation model for data trading websites. First, the evaluation index system of data commercialization is constructed, combining the abstract indexes reflecting data commonality and industry-specific data commercialization standards to assess the degree of data commercialization;second, the fuzzy multi-attribute group decision-making method is utilized to conduct fuzzy evaluation of the indexes in response to the issue of user evaluation preference;and the fuzzy function corresponding to the fuzzy evaluation is calculated based on the fuzzy structured element afterwards. The degree of superiority of the program is calculated by fuzzy compromise group decision-making method. Finally, the simplicity, convenience, and effectiveness of the data commoditization assessment model are demonstrated through a speech data case study. © 2023 ACM.

关键词： Data assimilation

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Decoding the Brain Neuro-Inspired Deep Learning Architectures of Cognitive Computing

Decoding the Brain Neuro-Inspired Deep Learning Architecture...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Latha, J. Murugan, S. Karthick Jayachandran, S. Maheshwari, G. Naveen, G. Navaneethan, S. R. M. K. Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvallur India R. M. D. Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvallur India Srm Institute of Science and Technology Department of Computer Science and Application Chennai Ramapuram India Rmk College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruvallur India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Tami Nadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359688

In response to the growing need for advanced artificial intelligence (AI) systems, the study thoroughly examines neuro-inspired deep learning architectures within the domain of cognitive computing. The need for such innovation originates from the constraints of traditional systems, characterized by their inefficiency in duplicating complex cognitive activities. Traditional techniques often need help with complex tasks and more biological neural network flexibility. The proposed system uses neuroscience principles to solve these limitations and create advanced models such as spiking neural networks and hierarchical attention processes, increasing machine learning capabilities. The results of extensive testing and analysis show that incorporating neuro-inspired designs considerably improves the system's performance across several dimensions. The results show increased efficiency, flexibility, and an expanded understanding of cognitive processes, indicating an evolution in AI development. As the results prove, adopting neuro-inspired deep learning architectures is essential for progressing toward artificial general intelligence and realizing the full potential of cognitive computing. © 2024 IEEE.

关键词： Adversarial machine learning

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A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning

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SN computer science 2024年第4期5卷 353页

作者： Nramban Kannan, Senthil Kumar Aseervatham, Joshi Moholkar, Kavita Palanimuthu, Mithun Marappan, Saranya Narendran, Muthusamy Sathar, Banu Sengan, Sudhakar Department of Computer Science & amp Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Computer Science and Business Systems Engineering JSPM’S Rajarshi Shahu College of Engineering Savitribai Phule Pune University Pune 411033 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai 600119 India Department of Information Technology Paavai Engineering College Tamil Nadu Namakkal 637018 India Department Information Technology KCG College of Technology Tamil Nadu Chennai 600097 India Department of Master of Computer Application T. J. Institute of Technology Chennai 600100 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627451 India

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL

关键词： Alerting systems and diagnostic assistance Chronic kidney disease Machine learning Medical history RL-ANN

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Sentiment Analysis of Speeches using VADER, Sentiment Intensity Analyzer, and TextBlob

Sentiment Analysis of Speeches using VADER, Sentiment Intens...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ankush Joshi Deepika Joshi Vikash Kumar Anuj Kumar Ritik Kumar Singh Naman Chauhan Computer Science and Application COER University Roorkee India Computer Science and Engineering Amarpali University Haldwani India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

In today’s world the words of a leader are the most valuable entity for his/her followers, and when the leader is a prime minister of one of the largest democratic countries then the value of his/her words is more valuable and important. In this research paper researchers have analyzed the data in the form of sentiments of the two most popular prime ministers of India Dr. Manmohan Singh and Mr. Narendra Modi. By this analysis, researchers can check the impact of their words on the people of India. For analyzing the sentiments researchers have used tools such as TextBlob, VADER, and Sentiment Intensity Analyzer, and for checking the occurrence frequency of words word cloud is used.

关键词： Sentiment analysis Speech analysis Education Tag clouds

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