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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Harsimrat Khandari Girija Shankar Sahoo Raja Praveen K N Ritu Shree Umamaheswari B Yashwant V Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of system mastering provides the potential to create an improved real-time records evaluation device this is more able to making experience of and knowledge the records. The development of an progressed actual-time statistics evaluation device utilizing system studying can hugely enhance statistics analysis techniques by means of integrating relevant data and structured analyses of the identical. this will include strategies inclusive of natural language processing and clustering algorithms to perceive trends, sentiment evaluation to understand customer feedback, and predictive analytics to better perceive areas for improvement. Moreover, the realtime statistics analysis system can be further enhanced by means of incorporating AI to make extra correct predictions, as well as reinforcement studying to evolve present models as new statistics is integrated. this would allow the system to learn more efficaciously and modify its algorithms to continually enhance its overall performance. Universal, by using machine studying to develop an advanced actual-time statistics evaluation system, extra correct and comprehensive insights can be generated from information, and groups can use this facts to better tell their choice making. moreover, temporal records evaluation can be advanced with faster response times as a result of the device being able to process records

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Expression of Concern for: Utilizing Machine Learning to Develop an Improved Real-Time Data Analysis System

Expression of Concern for: Utilizing Machine Learning to Dev...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

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Implementation of Agile Scrum Methodology in P4AI application Development

Implementation of Agile Scrum Methodology in P4AI Applicatio...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Sasmoko Yasinta Indrianti Sonya Rapinta Manalu Primary Teacher Education Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Podomoro University Jakarta Mobile Application & Technology Program School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665476652

The profiling for Aptitude Inventory (P4AI) application is an application developed to help students carry out self-assessments so they can understand the interests of the appropriate majors and careers. The application was developed using the agile scrum methodology, which is a software engineering method that applies the principles of the AGILE approach so that it relies on the strength of team collaboration, incremental product, and iterative processes to realize the product with the best final result. The Scrum. The purpose of this study was to evaluate whether the scrum methodology applies to the requirements of application development. The results of the research are several outputs from the Agile Scrum Methodology process which is implemented in the development of the artificial intelligence based P4AI application aptitude test website.

关键词： Human computer interaction Engineering profession Collaboration Iterative methods Scrum (Software development) Artificial intelligence Software engineering

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An Enhanced Explicit Port Forwarding Solution for Improving Video Delivery over Software-Defined Networks

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Journal of Internet technology 2023年第2期24卷 313-321页

作者： Ke, Chih-Heng Lo, Yu-Wen Chen, Yeong-Sheng Chen, Hung-Pai Department of Computer Science and Information Engineering National Quemoy University Taiwan Master of Information Technology and Application National Quemoy University Taiwan Department of Computer Science National Taipei University of Education Taiwan The Graduate School of Arts and Humanities Instruction National Taiwan University of Arts Taiwan

In order to improve video delivery, an Enhanced Explicit Port Forwarding (EEPF) solution is proposed. Data are divided into ordinary packets and video packets using a packet identification mechanism. Traditional mechanisms transmit packets through routing table lookup. As the size of the routing table often becomes excessively large, this lookup can be time-consuming. While the EEPF solution directly includes the transmission path in the video packet, which can reduce the transmission time. And by setting header flags/fields in video packets, the transmission can be further optimized. For example, to reduce the amount of duplicate data to be transmitted for broadcast packets are not replicated until they arrive at the destination switch;to avoid packet loss caused by disconnection, the transmission path can be monitored and rerouting can be initiated immediately in the event of disconnection;to increase the chance of successful delivery, packets can be replicated before they are transmitted via a path with high packet loss risk;and to prevent video packets from being discarded, the switch queue status can be monitored and managed. The testing results have shown that the EEPF solution can effectively shorten the time of video packet transmission and improve the quality of transmitted video. © 2023 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Packet loss

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Improvised Text Classification by Hybrid Machine Learning Techniques

Improvised Text Classification by Hybrid Machine Learning Te...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Cuddapah Anitha Akanksha Kapruwan Shilpa M Jyoti Dange Vipul Vekariya Ramya Maranan Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Application RNS First Grade College Channasandra Bangalore Karnataka India Department of Information Technology Atharva College of Engineering Malad West Mumbai Maharashtra India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

To improve the performance of English-text classification, text classification systems aim to design appropriate algorithms for extracting features and auto-classifying texts with automatic semantic feature selection. Motivated by the efficiency of Attention mechanism (AM), the ways to use AMs at different levels in CNN and specific RNN organization are explored in this work and BLSTM based models to eliminate misinformation, obtain more accurate contextual information, and ultimately improve performance. Finally, all of the features are concatenated and fed into the softmax classifier. The BLSTM is primarily used to hold the sentence interdependencies, then CNN is selected to extract features for continuous modeling operations, and this model is evaluated by applying it to the ETC. Furthermore, the proposed integrated CNN and BLSTM with Attention mechanism (A-C-BLSTM) model achieves significant results through our evaluation and performs better than a wide range of baseline models.

关键词： Technological innovation Attention mechanisms Computational modeling Text categorization Semantics Organizations Feature extraction

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Cell2Sentence: Teaching Large Language Models the Language of Biology 41

Cell2Sentence: Teaching Large Language Models the Language o...

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41st International Conference on Machine Learning, ICML 2024

作者： Levine, Daniel Rizvi, Syed Asad Lévy, Sacha Pallikkavaliyaveetil, Nazreen Zhang, David Chen, Xingyu Ghadermarzi, Sina Wu, Ruiming Zheng, Zihe Vrkic, Ivan Zhong, Anna Raskin, Daphne Han, Insu de Oliveira Fonseca, Antonio Henrique Caro, Josue Ortega Karbasi, Amin Dhodapkar, Rahul M. van Dijk, David Department of Computer Science Yale University New HavenCT United States School of Engineering Applied Science University of Pennsylvania PhiladelphiaPA United States School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Neuroscience Yale School of Medicine New HavenCT United States Wu Tsai Institute Yale University New HavenCT United States Google United States Yale Institute for Foundations of Data Science New HavenCT United States Yale School of Engineering and Applied Science New HavenCT United States Roski Eye Institute University of Southern California Los AngelesCA United States Yale School of Medicine New HavenCT United States Cardiovascular Research Center Yale School of Medicine New HavenCT United States Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT United States

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into"cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the finetuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S finetuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications. Copyright 2024 by the author(s)

关键词： Gene expression

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Deep Learning Techniques for Advanced Large-Scale Data Processing

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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International Conference on Computing and Networking technology (ICCNT)

作者： Mp Sunil Jyoti Shekhawat T. Vanaja Vazeer Ali Mohammed Shailesh J. Thaware Vaishali Singh Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer & Information System Lewis University Illinois USA Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information.

关键词： Deep learning Accuracy Scalability Information processing Predictive models Data processing Prediction algorithms Hardware Natural language processing Optimization

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Smart Home Security System Using Face Recognition Based on IoT- CNN

Smart Home Security System Using Face Recognition Based on I...

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Information technology Research and Innovation (ICITRI), International Conference on

作者： M. Aldiki Febriantono Alvin Zuhair Khaeruddin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Electrical Engineering Department University of Islam Blitar Blitar Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Malang Indonesia

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning computer Vision holds tremendous benefits for homeowners.

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Methodologies for Ensuring Robust Cybersecurity in Health Care Agencies for the Purpose of Securing Medical Internet of Things Devices

Methodologies for Ensuring Robust Cybersecurity in Health Ca...

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Disruptive Technologies (ICDT), International Conference on

作者： S.B. Prakalya Rajesh David Swapna Neerumalla J. Jegan S Sajithra B. Nancharaiah Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Application Architect KUKA U.S. Holdings Company LLC Austin TX USA Department of Computer Science and Engineering Vignana Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering School of Technology The Apollo University Chittoor Andhra Pradesh India Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Usha Rama College of Engineering and Technology Telaprolu Andra Pradesh India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Improvements in patient tracking, diagnosis, and treatment have resulted from the widespread use of MIoT devices in healthcare organizations. Patient safety and sensitive health data are at danger due to the increased number of linked devices, which has brought about major cybersecurity issues such as illegal access, data breaches, and service disruptions. This study delves into different approaches to guarantee strong cybersecurity in healthcare organizations, with a particular emphasis on safeguarding MIoT devices. Anomaly detection systems, secure communication protocols, device authentication, and end-to-end encryption are some of the key tactics that are investigated. In addition, the research looks into how machine learning and AI may be used for predictive analytics and real-time threat detection in MIoT networks, and how blockchain technology can guarantee data integrity and safe communication in these networks. Following legal standards like HIP AA and creating multi-layered security measures to reduce risks are also emphasized in the research. The goal of this study is to present a thorough framework for protecting MIoT devices in healthcare settings, guaranteeing patient safety and data confidentiality by integrating established cybersecurity practices with new technology. Additionally, issues like managing a diverse ecosystem of MIoT devices and device resource limits are discussed, along with suggestions for adaptable security solutions that are lightweight. The results provide healthcare organizations with practical advice on how to strengthen the security of their systems and prevent cyberattacks on their MIoT infrastructure.

关键词： Protocols Standards organizations Medical services Organizations Threat assessment Real-time systems Safety Encryption Internet of Things computer security

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Converging Paths in Divergent Systems: A Comparative Analysis of Data science Education Strategies in China and the United States

Converging Paths in Divergent Systems: A Comparative Analysi...

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Frontiers in Education (FIE) Conference

作者： Duo Li Elizabeth Milonas Qiping Zhang Department of Big Data Management and Application Shenyang Institute of Technology Fushun China Computer Systems Technology New York City College of Technology (CUNY) Brooklyn USA Palmer School of Library and Information Science Long Island University Brookville USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice full paper compares data science education strategies in China and the United States, exploring whether different approaches can achieve similar educational outcomes. In the U.S., data science programs are typically developed by individual schools to meet labor market demands, integrating data science as an interdisciplinary subject. In contrast, Chinese universities follow a uniform plan set by the education department, resulting in specialized fields like ‘Big Data Management and application’ and ‘Data science and Big Data technology.’ This research collected data on course offerings and university rankings in China and the U.S., analyzing curriculum content and program characteristics. Major findings indicate that in the U.S., Data science and Business Analytics programs focus on technical skills and are primarily found in top-ranking universities, while in China, Data science and Big Data Management programs emphasize management and are more widely distributed across different ranking groups. Despite these differences, both countries show similar geographic concentration patterns in data science programs. The study concludes that China and the U.S. adopt different educational strategies but achieve comparable effectiveness in data science education.

关键词： Economics Industries Technological innovation Analytical models Education Data science Big Data Market research Data models Business

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