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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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Study of Thermal Power Plant’s Intelligent Fire Detection and Suppression system Via Wireless Sensor Network and Carbon Capture and Storage technology 1

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Chang, Kuo-Chi Amesimenu, Governor David Kwabena Sung, Tien-Wen Chu, Kai-Chun Chang, Fu-Hsiang Wang, Hsiao-Chuan Hsu, Tsui-Lien Wang, Ming-Tsung Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Information Science and Engineering Fujian University of Technology Fuzhou China Department of Business Management Fujian University of Technology Fuzhou China Department of Tourism Shih-Hsin University Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan Institute of Construction Engineering and Management National Central University Taoyuan Taiwan Institute of Communications Engineering National Yunlin University of Science & Technology Yunlin Taiwan College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

In terms of fire outbreak, the nature of some big industries and thermal plants seems extremely volatile. The existence of oxygen and fuel cannot be taken out in thermal power plant, and many actions may be originated from heat source. Many researchers have developed more interest in the research field of fire detection in some big industries and thermal plants, especially those who use hydrocarbon-based product as fuel. The fire that normally occurs at these places is normally class B type of fire, and carbon dioxide is used to put them off. This paper proposes a design of an automated fire alarm system which utilizes already existing technologies such as the wireless sensor network technology and carbon capture and storage system to help alleviate the problem of global warming and reduce the cost incurred in existing ineffective fire alarm system. During combustion process in the thermal plant, the carbon dioxide emitted is captured and stored to be used to put off or suppressing fire after early detection by the wireless sensor network with sensors such as optical flame detectors, heat detector, and hydrogen detector. The simulations done on the final designs proved that this system is feasible and can be successfully deployed on a larger scale. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Carbon capture

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Penetration of Deep Learning in Human Health Care and Pharmaceutical Industries;the Opportunities and Challenges

Penetration of Deep Learning in Human Health Care and Pharma...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： Ramakrishnan Raman Radha. H. R T M Inbamalar D. Abdus Subhahan Ashok Kumar S. Bathrinath Swagata Sarkar Symbiosis Institute of Business Management Pune & Symbiosis International Pune Maharashtra Department of Chemistry K. S. School of Engineering and Management Bangalore Karnataka Department of Electronics and Communication R.M.K. College of Engineering Thiruvallur Tamil Nadu Department of Computer Science B V Raju Institute of Technology Narsapur Telangana Department of Computer Science Banasthali Vidyapith Rajasthan Department of Mechanical Engineering Kalasalingam Academy of Research Krishnankoil Tamil Nadu Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Tamil Nadu India

Computational medicine has emerged as a result of the advancement of medical technology, which has led to the emergence of the big data era in the biomedical area, which is supported by artificial intelligence technology. To advance the development of precision medicine, people must be able to extract the valuable information from this vast biomedical data. In the past, professionals in the field of feature engineering and domain knowledge were typically utilised to extract the features from the biological data using machine learning techniques, which took a lot of time and resources. Modern machine learning techniques like deep learning (DL) have an advantage over them in that they can automatically find strong, complex features from fresh data without the necessity for succeeding engineering. The study of DL's applications in the fields of genomics, drug development, electronic health records, and medical imaging suggests that deep learning has clear advantages in maximising the use of biomedical data. Deep learning is becoming increasingly important in the field of medicine and health due to its large range of potential applications. The lack of data, interpretability, data privacy, and heterogeneity are some of the limitations of deep learning in computational medical health. A resource for improving the use of deep learning in medical health is provided by the analysis and discussion of these difficulties.

关键词： Deep learning Drugs Genomics Medical services Production Feature extraction Biology

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

arXiv

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arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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Sentiment analysis with recommendation systems-a survey

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AIP Conference Proceedings 2024年第1期2742卷

作者： Oswalt Manoj Catherine Chandralekha Dharshini Hemadharsini Department of Computer Science and Business System Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India

The Emotions are assessed, and the feelings transmitted by the individual are brought up. The goal of sentiment analysis is to determine the polarity of a person’s textual opinion. Most of us use social media to market ourselves and share all of our knowledge, thoughts, ideas, and experiences with the rest of the world. Sentiment analysis has been investigated by several internet businesses, which propose material based on human emotions expressed through social media. Many people use social media to publish and share their thoughts and emotions. The sentiment analysis standard assorts a sentence with all forms of emotions, each of which has intensity. This study includes a sentiment analysis of diverse perspectives on social topics that are trending on social media, along with a recommendation system. This method recommends related films to viewers based on their current statement or issue. The technology also makes tailored video recommendations. Users indicate diverse viewpoints on a certain social issue based on their social media activity, and they actively address their thoughts on social media.

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A survey on protocol based control system to enhance internet censorship

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AIP Conference Proceedings 2024年第1期2742卷

作者： Balakrishnan Subramanian Yogeshwaran Kabilan Mohamed Nousath Yusuf Department of Computer Science and Business System Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India

VPN (Virtual Private Network) is a mechanism that creates a private network inside a public network (such as the internet) to transfer data. The VPN creates a virtual tunnel within a public network and uses various encryption techniques to keep the transmitted data safe. Though the VPN was created to increase security on a public network, this also masks the user’s true identity on the internet making them virtually anonymous. Thus, VPN introduces some new cyber security issues. In this paper, we will look at some of the existing methods to prevent the usage of VPN, and their weaknesses. We will also understand the proposed system of Content Restriction Protection Protocol (CRPP), how it works and how it overcomes the weakness of the existing methods.

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Correction to: A systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications (Archives of Computational Methods in engineering, (2024), 31, 1, (291-311), 10.1007/s11831-023-09978-x)

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Archives of Computational Methods in engineering 2024年 1-1页

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Punjab Phagwara144411 India Department of Computer Science Government Bikram College of Commerce Punjab Patiala147001 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan Mohali140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajaplayam India

Typesetter overlooked corrections regarding affiliations of author Rakesh Kumar and needs to be correctly read as only affiliated to: department of Mathematics, Lovely Professional University, Phagwara, Punjab 144411, India. The original article has been corrected. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

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A New Optimization Based on Parallelizing Hybrid PSOGSA Algorithm 16th

A New Optimization Based on Parallelizing Hybrid PSOGSA Algo...

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作者： Yu, Jie Wang, Hong-Jiang Pan, Jeng-Shyang Chang, Kuo-Chi Ngo, Truong-Giang Nguyen, Trong-The College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Computer Science and Engineering Shandong University of Science and Technology Shandong China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Haiphong University of Management and Technology Haiphong Viet Nam

ISBN: (纸本)9789813367562

This study suggests a new metaheuristic algorithm for global optimization, based on parallel hybridizing the swarm optimization (PSO) and Gravitational search algorithm (GSA). Subgroups of the population are formed by dividing the swarm’s community. Communication between the subsets can be developed by adding strategies for the mutation. Twenty-three benchmark functions are used to test its performance to verify the feasibility of the proposed algorithm. Compared with the PSO, GSA, and parallel PSO (PPSO), the findings of the proposed algorithm reveal that the proposed PPSOGSA achieves higher precision than other competitor algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Global optimization

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An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm

An Efficient Algorithm to Classify the Mitotic Cell using An...

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Computer, Power and Communications (ICCPC), International Conference on

作者： R.G. Vidhya J. Seetha Sudhir Ramadass S. Dilipkumar Ajith Sundaram G. Saritha Department of ECE HKBK College of Engineering Bangalore India Department of Computer science and Business systems Panimalar Engineering College Chennai India Data analyst Sterck systems Chennai Department of Analytics Scope VIT Vellore Selection grade School of Business Kochi Department of ECE Sri Sai Ram institute of technology Chennai Tamilnadu India

Cancer that originates in the breast tissue then spreads to the chest wall is called breast cancer. Doctors routinely examine mammograms for signs of cancer; however, aberrant macrocalcifications and microcalcifications might appear on mammograms when the picture quality is subpar. Always get checked out if you see anything out of the ordinary, especially if it involves your breasts, such abnormal calcium deposits. For this mammographic deposit to be properly interpreted, top-notch picture quality is necessary. Many different breast cancer screening methods and the many breast cancer phases are still the subject of active study. In order to construct effective medical image processing systems, experts use methods including the Ant Colony Algorithm (ACA), the Improved Adaptive Fuzzy C-Means (IAFCM), and TNM (the size of the breast tumor (T), the lymph nodes around the tumor, and metastasized). Classes were determined using an MPIG, or a modified Poisson inverse gradient classifier. More than five hundred picture modalities are used across all methods. Medical professionals that rely on images to establish diagnoses or treatments might find the results of this research useful.

关键词： Image segmentation Sensitivity Breast tissue Medical services Feature extraction Breast cancer Mammography

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