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2nd International Conference on Interdisciplinary Cyber Physical Systems, ICPS 2022

作者： Ansar, Syed Anas Jaiswal, Kriti Aggarwal, Shruti Shukla, Shruti Yadav, Jaya Soni, Nupur Babu Banarasi das University Department of Computer Application Lucknow India University of Lucknow Department of Computer Science Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (数字)9781665470223

ISBN: (纸本)9781665470223

With the ever-increasing cost of livelihood, technological intervention is necessary to keep costs under control. Hence, smart home technology can be considered as a well-established and entrenched region of interest. In the current scenario, research subsidizes present-time houses, enabling the user to design and manage a smart home to conserve energy while allowing for more autonomous applications. A voice-activated home automation system integrates the Internet of Things, Artificial Intelligence, Blockchain, and Natural Language Processing to deliver a cost-effective and efficient method of interacting with household equipment. The primary objective of this paper is to assess SPAs using blockchain technology and a natural language processor (NLP), as well as the design of a SPA based on the internet of things. Applicable cyber threats and countermeasures are also addressed in this article. Using Yule's coefficient of association, a case study was undertaken to evaluate the association between privacy and security threats posed by this modern technology. The findings show that privacy and security are positively related. © 2022 IEEE.

关键词： Blockchain

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Frontiers in Artificial Intelligence and applications 3

Frontiers in Artificial Intelligence and Applications

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3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Yadav, Jaya Shukla, Shruti Sharma, Kanika Soni, Nupur Agarwal, Shruti Pathak, Prabhash Chandra Babasaheb Bhimrao Ambedkar University Department of Information Technology Lucknow India Babu Banarasi das University Department of Computer Application Lucknow India Mangalmay Institute of Management and Technology Department of Computer Application Greater Noida India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Lucknow India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The industrial revolution has been the main cause ever since tremendous technological advancement was observed. The ubiquitous deployment of recent information and communication technologies (ICT), namely Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technology, is hastening the world's industrial and technological transformation. This technical aggrandizement enhances the working culture and has a favorable impact on the workplace, as per the progressivist perspective. The breakneck pace of technological advancement, as well as AI, has enabled humans to replace manual labor in various industries. As being a domain of science and technology, AI develops machines and programs for computers that are intelligent and can accomplish tasks that would normally require human intelligence abilities. This paper mainly explores the frontiers of artificial intelligence and its applications in various fields. The AI Frontiers promulgate methodical concepts that are peer-reviewed cutting-edge research on the disruptive technological revolution of Artificial Intelligence. Additionally, some key viewpoints in the field of AI have been listed along with the main frontiers, including Machine Learning (ML), Deep Learning (DL), Fuzzy Logic (FL), Natural Language Processor (NLP), and Genetic Algorithm (GA). Furthermore, this paper discussed some common AI applications and a briefing about the current scenario in the worldwide market for artificial intelligence. © 2022 IEEE.

关键词： Genetic algorithms

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Machine Learning and Data Mining for Predictive Modeling of Stroke Risk and Post-Stroke Care Management 3

Machine Learning and Data Mining for Predictive Modeling of ...

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

作者： Gobi, N. Singh, B.P. Yadav, Surendra Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350343274

The research investigates the efficacy of a comprehensive machine learning and data mining approach in predictive modeling of stroke risk and post-stroke care management. A holistic methodology is proposed, integrating diverse datasets and advanced algorithms, including Logistic Regression, Random Forest, and Long Short-Term Memory (LSTM) networks. Data preprocessing, feature extraction, and personalized care plan generation are key components. Performance evaluation using accuracy, sensitivity, specificity, precision, F1 Score, AUC, and MAE demonstrates the superior predictive capabilities and efficiency of the proposed method compared to traditional approaches. The study highlights the potential for revolutionizing stroke care, emphasizing personalized risk assessment and post-stroke management for improved patient outcomes and healthcare delivery. © 2023 IEEE.

关键词： Data mining

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Optimizing Drug Dosage and Medication Management for Geriatric Patients with Artificial Neural Networks 3

Optimizing Drug Dosage and Medication Management for Geriatr...

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

作者： Beemkumar, N. Singh, Daljeet Pal Ahmed, Zahid Faculty of Engineering and Technology Department of Mechanical Engineering Bagalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350343274

In this paper, we utilize ANNs to investigate how medicine dosing and management might be improved for the elderly population. The suggested strategy calls for the use of patient-specific data as inputs for the ANNs, such as age, renal and hepatic function, body mass index, and preexisting prescription profiles. The neural network model dynamically changes drug doses using the combination of the Multilayer Perceptron (MLP) algorithm, Backpropagation, and the Stochastic Gradient Descent (SGD) algorithm to maximize therapeutic benefits and reduce the likelihood of adverse drug events. The research highlights the improved accuracy, sensitivity, specificity, precision, and F1 score of the suggested technique compared to numerous conventional methods. As a result, the suggested system has great potential to revolutionize medication management for the elderly population since it is more personalized, flexible, patient-safe, effective, and efficient in its use of resources. © 2023 IEEE.

关键词： Optimization

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Bitcoin-Blockchain Technology: Security Perspective 3

Bitcoin-Blockchain Technology: Security Perspective

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Ansar, Syed Anas Arya, Swati Aggrawal, Shruti Yadav, Jaya Pathak, Prabhash Chandra Babu Banarasi Das University Department of Computer Application Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India University of Lucknow Department of Computer Science Lucknow India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Nowadays, Blockchain technology has also drawn mammoth attention as it is currently triggering multiple projects. This technology is the prevalent technology that allows Bitcoin to operate. Bitcoin has already cemented itself as a popular virtual currency, and it will soon become a pressing requirement for citizens. Furthermore, with digital currencies proliferating all over the world, security must not be overlooked, especially when there have been numerous attacks and security vulnerabilities on bitcoin's blockchain. However, the lucrative system has drawn opponents and attackers who are continually attempting to exploit flaws in the technology in order to get profit. This paper contemplates the bitcoin's transaction process, with a focus on obscuring the true identities of clients and their coins prior to transacting, and then elucidating on the following: SHA-256 function, the primary threats to Bitcoin's security, along with some countermeasures to be undertaken. © 2022 IEEE.

关键词： Blockchain

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A Multimodal Fusion and Ensemble Approach for Robust Fake News Detection through Deep Learning, Reinforcement Learning, and Using Blockchain to Enhance Security and Authorization

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2025年第2期18卷

作者： Kumar, Vivek Satveer Ahmad, Waseem Kumar, Satendra Department of Computer Application Quantum University U.K Roorkee India Department of Computer Science & Engineering Meerut Insitute of Engineering and Technology U.P Meerut India

Background: computer science significantly influences modern culture, especially with the rapid breakthroughs and technology in social media networking. Social media platforms have become significant channels for sharing and exchanging daily news and information on many issues in the current digital environment, which is known for its massive data collection and transmission capabilities. While there are many benefits to this environment, there are also many false reports and information that deceive readers and users into believing they are receiving correct information. Objective: Nowadays, all users use social media to obtain news content, but sometimes some malicious users tamper with real news and then spread fake news, which may reduce the reputation of social media. Therefore, many existing models have been introduced to detect fake news, but these models are based on traditional machine learning algorithms, such as decision tree (DT), multilayer perceptron (MLP), random forest (RF), etc. These models Lack of performance, security, and authorization. Our proposed model can solve existing model problems using reinforcement learning and blockchain technology. Methods: In this research paper, we explain a new way to identify fake news. This new approach, combined with policy-based heuristic reinforcement learning (PHRL), where the model dynamically adjusts through iterative learning, is the key innovation and gradually improves classification accuracy. The same as our smart contract authorization method, which enhances the authenticity of content posted safely by authorized users and improves the transparency and accountability of information. Results: Our model was tested on real-time information collected from various sources with 70% accuracy and valid authentication. Conclusion: Our proposed model produced better results with a Mean Absolute Error (MAE) of 0.0811 and Root Mean Squared Error (RMSE) of 0.2847, both significantly lower values. Our proposed mode

关键词： Deep reinforcement learning

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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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Algorithms for Detection of Anomalies in Data From Clinical Studies and Medical Registries

Algorithms for Detection of Anomalies in Data From Clinical ...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Patil, Harshal Swati Sudheendramouli, H.C. Pratyush, Kumar Parikh, Swapnil Vishwakarma, Rajesh Kumar Sri Balaji University Maharashtra India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand 248002 India Gsss Simha SubbaMahalakshmi First Grade College Department of Computer Science and Application Mysuru570016 India Shri Vile Parle Kelavani Mandal's Institute of Pharmacy Department of Pharmaceutical Chemistry Maharashtra 424001 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat India Jaypee University of Engineering and Technology Department of Electronics and Communication Engineering Madhya Pradesh 473226 India

ISBN: (纸本)9798350375237

Anomaly detection is a burgeoning trend in data quality, underpinned by the fundamental principles of honesty and truthfulness in scientific research. Adherence to these principles is critical for advancing science and ensuring public trust in scientific findings. Deviations from these principles can lead to scientific misconduct or fraud, with potentially severe consequences, particularly in clinical research where human lives may be at stake. This work introduces an implemented algorithm for anomaly detection, leveraging basic machine learning concepts such as training on original datasets and testing on new datasets. Two training methods, resubstitution, and external validation (hold-out), were employed. To assess classification accuracy, the input dataset was deliberately augmented with anomalous records, and the detection results were compared against a vector of artificially generated anomalies. Prior studies may have overlooked factors such as varying anomaly occurrence rates and the need for manual threshold adjustments, which our algorithm addresses. By addressing these limitations, the algorithm contributes to the advancement of anomaly detection methodologies in clinical research and beyond. © 2024 IEEE.

关键词： Clinical research

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Unlocking the Power of Natural Language Processing to Automate Knowledge Discovery 3

Unlocking the Power of Natural Language Processing to Automa...

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

作者： Ravikumar, V. Kodmelwar, Manohar K. Adlin Jebakumari, S. Shekhawat, Jyoti Sakhare, Nitin N. Vijayalakshmi, V.J. Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350319125

natural language processing (NLP) is an increasing number of powerful tools for knowledge discovery, permitting companies to automate techniques like textual content analysis and sentiment analysis that used to require big manual effort. NLP helps the automation of tasks inclusive of information person intent, comprehending textual content, extracting records, producing summaries, and classifying documents. With NLP, those duties can be achieved in a fraction of the time with some distance extra accuracy and without the want for manual processing. By way of leveraging present technology, companies can liberate the energy of NLP to automate knowledge discovery and substantially improve their aggressive advantage. © 2023 IEEE.

关键词： automation information substantially summaries techniques

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Rice quality Prediction using Convolution Neural Network 2

Rice quality Prediction using Convolution Neural Network

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Rekha, V. Shiny, R.M. Ramya, J. Sakthivel, B. Pratheep, V.G. Kumar, Ashok AgurchandManmull Jain College Department of Bachelors's in Computer Application Chennai India Velammal Institute of Technology Department of Artificial Intelligence and Data Science Thiruvallur India St. Joseph's College of Engineering Department of Computer Science Engineering Chennai India Vivekanandha College of Engineering for Women Department of Electronics and Communication Engineering Namakkal India Kongu Engineering College Department of Mechatronics Engineering Perundurai India Banasthali Vidyapith Department of Computer Science Banasthali India

ISBN: (纸本)9798350347456

Rice is the most widely grown crop in Asia. In Asia, 60% of the population relies on rice, China, Indonesia especially India consumes the majority of it. Food is the single most direct way for an individual to become infected with any ailment. Predicting the quality of rice is a crucial aspect as it is one of the most consumed foods in Asia. In this study, the quality of the rice is predicted using a Deep Learning (DL) model to predict the quality more precisely. The dataset required for this study is collected from the Kaggle website composed of a total of 5000 images of 5 different rice varieties. The collected raw dataset is then pre-processed using several methods to make them compatible with the DL model. The pre-proceed images are then subjected to the prediction of the quality of the rice using the Convolutional Neural Network (CNN), a DL model. Performance metrics such as accuracy, loss, and time are calculated to validate the precision of the model. The experimental result showed that the model has the highest accuracy of about 98.6% when compared to the other existing models. The model demonstrated a very minimal loss percentage of only about 1.24% and the model predicts the result in about 25 microseconds. The proposed DL model can be utilized for better and more objectively evaluate the quality of the rice at trading points inside the rice marketing chain due to its fairly high precision. © 2023 IEEE.

关键词： Convolutional neural networks

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