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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Vaghela, Hardika Jadeja, Mahipal K.S. School of Business Management and Information Technology Ahmedabad India Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (纸本)9789819736898

The Covid-19 pandemic has substantially influenced human existence, impacting not just our social and economic aspects but also our professional lives. This effect is readily evident on social media platforms. Amidst the exchange of information and discussions concerning the Covid-19 pandemic, one can discern a growing wave of support for a hybrid work culture when closely examining social media activities. With this perspective in mind, we conducted an analysis of approximately 16,000 tweets to gauge public sentiment regarding the hybrid work culture. We chose Twitter as our platform for this study because it offers a wealth of data that can be effectively leveraged to gain a comprehensive insight into people’s sentiments. We have considered tweets containing the following keywords (hashtags): WFH, WorkFromHome, Remotework, and Hybridwork. By analyzing the Twitter dataset, we extract insights and ascertain the ratio of negative tweets to positive (or supportive) tweets concerning the hybrid work culture. Graphical visualizations of the polarity of the tweets support the study. We conclude that a significant percentage (around 50%) of people support hybrid work culture and show positive sentiments via tweets, whereas 30% and 20% of tweets are neutral and negative, respectively. This research aids in gaining insight into how public opinions align with the shifts in the "new normal." This study can greatly assist strategic decision-makers in planning for future organizational decisions. We combined the interpretation of the results with Wordmap (using WordCloud library of Python) in order to add more sense to the results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Performance Evaluation of Machine Learning Approaches in Detecting IoT-Botnet Attacks

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 136-146页

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Mughaid, Ala Taamneh, Salah Wedyan, Fadi I. Obiedat, Mamoon Department of Computer Science and Applications Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Information Technology Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Engineering Computing and Mathematical Sciences Lewis University RomeovilleIL United States

Botnets are today recognized as one of the most advanced vulnerability threats. Botnets control a huge percentage of network traffic and PCs. They have the ability to remotely control PCs (zombie machines) by their creator (BotMaster) via Command and Control (C&C) framework. They are the keys to a variety of Internet attacks such as spams, DDOS, and spreading malwares. This study proposes a number of machine learning techniques for detecting botnet assaults via IoT networks to help researchers in choosing the suitable ML algorithm for their applications. Using the BoT-IoT dataset, six different machine learning methods were evaluated: REPTree, RandomTree, RandomForest, J48, metaBagging, and Naive Bayes. Several measures, including accuracy, TPR, FPR, and many more, have been used to evaluate the algorithms’ performance. The six algorithms were evaluated using three different testing situations. Scenario-1 tested the algorithms utilizing all of the parameters presented in the BoT-IoT dataset, scenario-2 used the IG feature reduction approach, and scenario-3 used extracted features from the attacker’s received packets. The results revealed that the assessed algorithms performed well in all three cases with slight differences. © 2023 by the authors of this article. Published under CC-BY.

关键词： Internet of things

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Active Thermal Control for Power Semiconductor using Peltier-Device

Active Thermal Control for Power Semiconductor using Peltier...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Yamaguchi, Masamichi Watanabe, Hiroki Itoh, Jun-Ichi Lee, Kyo-Beum Nagaoka University of Technology Department of Science of Technology Innovation Nagaoka Japan Nagaoka University of Technology Department of Electrical Electronics and Information Engineering Nagaoka Japan Ajou University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350376067

The paper proposes an Active Thermal Control (ATC) approach employing a Peltier device as a heat exchanger for temperature regulation. The control strategy relies on Model Predictive Control (MPC) utilizing an equivalent thermal model. It elucidates the current control methodology for the Peltier device based on this model and predicted temperature. Moreover, the predicted temperature and additional thermal sensor estimate the loss amount at the heat source without any electrical sensor, such as a current or a voltage sensor. The simulation result affirms the effectiveness of the proposed control and loss estimation method. Finally, the experimental result demonstrates the validity of the proposed control, evidenced by a 2.6% maximum error in temperature control. © 2024 IEEE.

关键词： Predictive control systems

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Single-to-Multi Music Track Composition Using Interactive Chaotic Evolution 13th

Single-to-Multi Music Track Composition Using Interactive Ch...

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13th International Conference on Frontier Computing, FC 2023

作者： Hung, Ying Kai Pei, Yan Li, Jianqiang Graduate School of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu965-8580 Japan Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819993413

This research presents a new music generation model and a novel MIDI data format for MIDI music generation. This innovative data format allows us to process MIDI music in a manner analogous to video analysis. Initially, the model employs Convolutional Neural Networks (CNN) as an encoder to effectively capture local and global features within the musical data. Subsequently, we utilize a Transformer as a decoder, leveraging its self-attention mechanism to handle the long-term dependencies present in music data. In the training process, an interactive chaotic algorithm is introduced to update the model’s weights, assisting the model in avoiding entrapment in local optima. This enhances the learning efficiency of the model and improves the quality of the generated output, enabling the model to generate music, including accompaniment, that aligns with human aesthetics from any given melody. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Music

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Image Caption Generation using Vision Transformer and GPT Architecture 2

Image Caption Generation using Vision Transformer and GPT Ar...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Mishra, Swapneel Seth, Saumya Jain, Shrishti Pant, Vasudev Parikh, Jolly Jain, Rachna Islam, Sardar M.N. Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering New Delhi India Bhagwan Parshuram Institute of Technology Department of Information Technology New Delhi India Victoria University Australia

ISBN: (纸本)9798350371314

Transformer-based models have reshaped image captioning but grapple with issues like caption accuracy, particularly for complex visuals. Addressing these shortcomings is essential. Motivated by existing challenges, the Vision Transformer (ViT) as encoder and Generative Pretrained Transformer 2 (GPT-2) as decoder have been employed to enhance caption quality, utilizing the Seq2Seq framework and training on Flickr8k. This work introduced a novel ViT-GPT-2 image captioning model, evaluating it against benchmarks including Flickr8k. The model excels with BLEU-4 at 39.76 and METEOR at 52.30, bridging visual-textual gaps effectively. This research advances image captioning, offering practitioners an improved model for content indexing, accessibility, and human-computer interaction. ViT- GPT-2's success underscores bridging semantic gaps in image captioning, with future work exploring diverse datasets and fine-tuning techniques for enhanced performance. © 2024 IEEE.

关键词： Image enhancement

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Autonomous Exploration Algorithm for Mobile Robots in Unknown Confined Environment 16

Autonomous Exploration Algorithm for Mobile Robots in Unknow...

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16th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2024

作者： Lyu, Zhixiao Yin, Yue Liu, Qinyang Yang, Tianyao Nanjing Normal University School of Electrical and Automation Engineering China Nanjing University of Information Science & Technology School of Computer Science School of Cyber Science and Engineering China

ISBN: (纸本)9798331540241

For the efficiency and precision of autonomous exploration, this paper proposes an optimized algorithm for mobile robots in unknown confined environment. However, the traditional algorithms frequently lead to excessive navigation distance and waste of exploration time consumption. To address these limitations, frontier point selection is improved by utilizing RRT, accomplishing significant improvement in computational efficiency. The exploration path planning was accomplished through the utilization of an enhanced Dijkstra's algorithm, in conjunction with a topological map. Through experimental comparisons, the proposed algorithm achieves significant advantages over existing frontier-based and RRT-based algorithms when dealing with complex environments. In particular, our system is able to reduce the traveling distance by up to 55.16% in difficult scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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Quality of Service based Selfish Node Detection in Mobile Ad-Hoc Network 3

Quality of Service based Selfish Node Detection in Mobile Ad...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Kannan, B. Maruthu Vadivel, G. Viswanathan, C. Reddy, P. Kiran Kumar Sphoorthy Engineering College Department of Computer Science and Engineering Telangana Hyderabad India University of Technology and Applied Science Department of Information Technology Salalah Oman Alpha College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9781665462167

Mobile ad hoc networks (MANETs) are wireless networks containing a collection of wireless nodes interrelated with multi-hop communication paths. Mobile nodes can freely create and dynamically self-organize random and temporary wireless networks without pre-existing infrastructure. Hence, the transmission connection between two nodes breaks repeatedly due to node mobility. This causes an additional delay in the network's performance. All mobile nodes work together entirely regarding the distribution of their memory space. In actuality, though, several nodes may inconsiderately choose to cooperate partially;otherwise, not at all with other nodes. These selfish nodes minimize the whole data's accessibility. To solve these issues, quality-of-service-based self-node detection (QSND) in MANET is proposed. In this approach, the Quality of Service (QoS) parameters verifies the operation of a node. This approach uses the QoS parameters like packet loss ratio, packet delay ratio, and remaining energy ratio as the most significant components to evaluate the selfish nodes in the MANET. This approach minimized the delay and improved the network throughput. © 2023 IEEE.

关键词： Quality of service

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Joint Beamforming and Deployment Design for IRS-aided Energy Harvest IoT Network 3

Joint Beamforming and Deployment Design for IRS-aided Energy...

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3rd International Conference on Neural Networks, information and Communication engineering, NNICE 2023

作者： Liu, Zhenwei Zhang, Wenjie Cao, Qi Liang, Yunlong Huang, Wei He, Shiwen School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9798350335972

In this paper, we propose a transmission design scheme for the simultaneous wireless information and power transfer (SWIPT) in the intelligent reflecting surface (IRS) aided multi-user interference channel (IFC). Our goal is to maximize the harvested energy of all receivers by jointly optimizing the transmit and reflective beamforming vectors as well as the location of the IRSs while guaranteeing the rate of each user, deployable region of each IRS, and total transmit power constraints. The formulated optimization problem is hard to be solved due to the non-convex coupled of the variables related with location and beamforming vectors as well as the infinite number of constraints. To overcome the difficulty, we first design the location of IRSs by decoupling the larger-scale and small-scale fading of the reflective channels with the local region assumption. Then, based on this, we transform the transmit and reflective beamforming optimization problem into the second-order cone program (SOCP) and the relaxed semi-definite program (SDP) problems, which are convex and solved by utilizing the conventional interior point approach. Numerical results report that the gain is improved by optimizing the location of IRSs. © 2023 IEEE.

关键词： Optimization

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Efficient Cell Segmentation in Microscopy Images using Sub-Sampling Integrated SVM 2

Efficient Cell Segmentation in Microscopy Images using Sub-S...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Ramprasath, J. Kumar, M. Arun Krishnaraj, N. Vignesh, C. Chandru Saranya, N. Ramprakash, P. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of AI&DS Coimbatore India

ISBN: (纸本)9798331534950

Support Vector Machine (SVM) integrated with subsampling methods provides the latest approach to cell segmentation of microscopic images. This approach aims to increase computing efficiency and also maintain segmentation accuracy. To begin with, the processing load associated with large-scale microscopic images is reduced by employing subsampling techniques. On applying Support Vector Machine (SVM), classification of the picture patches into cell and non-cell portions based on the gathered attributes. The produced method is highly valuable for high-throughput analysis in medical and biological diagnostics as it provides competitive segmentation performance while drastically cutting down time for its process. Through a detailed analysis from rigorous testing and comparison with existing methods, efficiency and efficacy are demonstrated in accurately differentiating cells from microscopic images and extending solutions for vast biomedical applications. © 2024 IEEE.

关键词： Image segmentation

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Research and development of an information system for speech recognition based on neural network algorithms metrics 4

Research and development of an information system for speech...

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4th IEEE International Conference on Smart information Systems and Technologies, SIST 2024

作者： Kanashev, Maksat Mukasheva, Assel Yedilkhan, Didar Rakhmetulaeva, Sabina Gnatyuk, Sergiy Nazym, Mirambayeva Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The Department of Computer Engineering Astana IT University Astana Kazakhstan The Department of Information Systems International University of Information Technologies Almaty Kazakhstan The Faculty of Cybersecurity Computer and Software Engineering National Aviation University National Aviation University Kyiv Ukraine

ISBN: (纸本)9798350374865

This article examines the parameters of speech recognition in audio recordings containing a variety of phrases uttered by different people with different accents and intonations. The aim of the study is to evaluate the recognition speed, the accuracy of audio-to-text conversion and resource consumption when using SpeechRecognition and Levenshtein libraries. During the analysis, the above libraries were used to record and recognize audio files, as well as to calculate the distance between two lines and determine the recognition accuracy. The findings of this study can be useful for improving speech recognition systems and creating more adaptive and efficient solutions for audio data processing. The results of comparing the accuracy of speech recognition models are recorded in JSON format, after which, using the matplotlib library, they are visualized as graphs, comparing the speed and accuracy of recognition. Visualization of data in this way contributes to their improvement and more effective integration into educational systems. In conclusion, the authors emphasize the prospects for further development of speech recognition technologies in different fields. © 2024 IEEE.

关键词： Audio recordings

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