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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Priya, D. Divya Chinnasamy, P. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Jalil, Norazira Binti A. Sangodiah, Anbuselvan Mlr Institue of Technology Department of Computer Science Engineering Hyderabad India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Srivilliputtur India Faculty of Information and Communication Technology Perak Kampar Malaysia Quest International University Faculty of Computing and Engineering Perak Ipoh Malaysia

ISBN: (纸本)9798350379945

Traffic signs secure their safety on the roads and the protection of others around them;everyone must be aware of traffic signs and other aspects of road safety. Traffic sign detection is a highly relevant computer vision problem that forms the basis of numerous applications in sectors like the automotive industry. Between classes, traffic signs differ widely in terms of colour, shape, and the use of pictograms or text. The project's classification of traffic signs in an image into various categories is based on a deep neural network model. IoT devices that capture traffic signs and notify users of them are used to build a model. © 2024 IEEE.

关键词： Automotive industry

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Machine Learning-based Multi-path Reliable and Energy-efficient Routing Protocol for Underwater Wireless Sensor Networks 20

Machine Learning-based Multi-path Reliable and Energy-effici...

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20th International Conference on Frontiers of information technology, FIT 2023

作者： Khan, Zahid Ullah Aman, Muhammad Rahman, Wazir Ur Khan, Faran Jamil, Tooba Hashim, Rifhat Harbin Engineering University College of Underwater Acoustic Engineering Harbin150001 China University of Science and Technology Bannu Department of Software Engineering Pakistan Barani Institute of Information and Technology Department of Computer Science Rawalpindi Pakistan

ISBN: (纸本)9798350395785

The research on applications for Underwater Wireless Sensor Networks (UWSNs) including the investigation of submerged resources, gathering oceanographic data, conducting operational surveillance, and prevention from natural disasters is rapidly increasing. Traditional wireless sensor networks (TWSNs) are distinct from UWSNs. Routing protocols of TWSNs are also distinct from UWSNs routing protocols in terms of energy efficiency. Most of routing protocols have been developed to extract data from the ocean floor to the surface of waters. There are many challenges for indigenous designing of an efficient routing protocol for UWSNs, such as, design of reliable path for forwarding communicating data packets, managing the movement of nodes, configuration of sensor nodes, removing void communicating nodes and increasing the power efficiency of the system. This research focuses on designing a novel Machine learning-based multi-path reliable and energy-efficient routing protocol (M2RE2RP) for UWSNs. It will select an efficient path among the existing communication paths used by sensor nodes, which will ultimately increase the lifespan of the network. The simulation experiment results dictate that the proposed routing model M2RE2RP achieves better performance in terms of network throughput, end-to-end latency, total energy consumption and network lifetime. © 2023 IEEE.

关键词： Routing protocols

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Spectrum Serenade: OPNET Expedition Unveiling WLAN 802.11e Performance Evaluation 9th

Spectrum Serenade: OPNET Expedition Unveiling WLAN 802.11e P...

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9th Future Technologies Conference, FTC 2024

作者： Ali, Ali Mohd Owida, Hamza Abu Al-Qerem, Ahmad Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Medical Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13132 Jordan

ISBN: (纸本)9783031731242

Interest in Wireless LAN 802.11 is climbing owing to its cost-effectiveness and simplicity of installation, yet its restricted Quality of Service (QoS) capabilities present obstacles for real-time apps. This endeavour aims to evaluate QoS characteristics and boosts by devising scenarios within the OPNET simulator. Through contrasting Wireless LAN 802.11e with the standard 802.11, our goal is to measure how advancements in allocated channel access can elevate performance levels. Findings show that the improved standard provides a more effective service mechanism, giving priority to traffic that is highly important while keeping latency within acceptable bounds, hence enhancing QoS support for contemporary applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Quality of service

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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN computer science 2024年第5期5卷 546页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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Effect of Introducing Leucine Residues into 2-Aminoisobutyric Acid-Based Amphiphilic Helical Peptides on Intermolecular Interactions and Peptide Self-Assembly

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ACS Omega 2025年第21期10卷 22184-22190页

作者： Yamaguchi, Kasumi Kusumoto, Tomoichiro Araki, Takeshi Gomibuchi, Yuki Yasunaga, Takuo Osada, Satoshi Taira, Junichi Department of Chemistry and Applied Chemistry Faculty of Science and Engineering Saga University Saga 840-8502 Japan Department of Bioscience and Bioinformatics Fukuoka 820-8502 Japan Department of Physics and Information Technology Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka 820-8502 Japan

In recent years, self-assembling de novo-designed peptides have attracted increasing attention as materials for constructing submicrometer-scale structures. We have previously conducted structure-activity relationship studies on the BXBA-20 series of 20-residue amphiphilic peptides containing 2-aminoisobutyric acid (Aib) [Higashimoto, Y. et al. (1999) J. Biochem. 125, 705-712;Hara, T. et al. (2001) J. Biochem. 130, 749-755;Taira, J. et al. (2010) J. Pept. Sci. 16, 607-612]. These peptides share the common sequence Ac-(Aib-Xxx-Aib-Ala)₅-NH₂, where the introduction of a hydrophilic amino acid at the Xxx position results in the formation of a hydrophilic face on one side of the helix. Among them, some peptides exhibited weak intermolecular association and interactions with lipid membranes, though none demonstrated the ability to form submicrometer-scale structures through self-assembly. In this study, we present BKBL-20, a new member of this peptide series incorporating leucine on the hydrophobic face and lysine on the hydrophilic face. Circular dichroism spectroscopy indicated strong association in buffer, while hemolysis assays revealed significant membrane disruption. Transmission electron microscopy further confirmed the formation of ordered fibrous or array-like structures. These findings suggest that BKBL-20 successfully combines self-assembly and membrane-disruptive functions. © 2025 The Authors. Published by American Chemical Society.

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TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates 4

TRIPORA: Intelligent Machine Learning Solution for Sri Lanka...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Legrand, T.R. Bandara, K.M.R.A.I. Stefania Crishani, J.A.D. Uvindu, L.W.P. Amarasena, Nelum Kasthurirathna, Dharshana Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Sri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. There are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. The biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. There are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study. © 2022 IEEE.

关键词： Obsolescence

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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An upgraded variant of backtracking search algorithm and its application to COVID-19 CT images segmentation

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Multimedia Tools and Applications 2025年 1-57页

作者： Debnath, Sanjib Nama, Sukanta Chakraborty, Sanjoy Saha, Apu Kumar Mirjalili, Seyedali Department of Computer Science & Engineering National Institute of Technology Agartala Tripura Agartala799046 India Department of Science & Humanities Gomati District Polytechnic Fulkumari Tripura Udaipur799013 India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Ostrava Czech Republic University Research and Innovation Center Obuda University Budapest Hungary Department of Computer Science & Engineering The ICFAI University Tripura Tripura 799210 India

To improve the performance of the original backtracking search algorithm (BSA), this work combines BSA with a centralized population initialization and an elitism-based local escape operator and proposes a new, improved variant of BSA called CiBSA. BSA's performance is improved by centralized population initialization regarding search capability, convergence speed, and capacity to jump out of the local optimum. The suggested algorithm's elitism-based local escape operator method prevents BSA searching slowness and enhances convergence rates and local search efficiency. The performance of the proposed algorithm has been tested on a set of 54 test suites. An extensive comparison with other recent meta-heuristics algorithms (MA) demonstrates that both methodologies substantially assist BSA in boosting the quality of its solutions and speeding up the convergence rate. The proposed CiBSA is then applied to a real-world problem: image segmentation using Kapur's entropy function. The proposed CiBSA is successfully applied to the segmentation of COVID-19 CT images using Kapur's entropy for grey-scale images. Considering the performance matrices Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Intersection over Union (IoU), the suggested CiBSA outperformed conventional MA algorithms. In COVID-19 CT image segmentation trials, CiBSA has superior segmentation capability and adaptability at varied threshold levels than other approaches. As a result, CiBSA has a lot of potential for enhancing COVID-19 diagnosis accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-Telecom Services 2

Aspect-Based Sentiment Analysis on Amharic Text for Evaluati...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Tesfa, Tarikwa Belete, Befikadu Abera, Samuel Mohapatra, Sudhir Kumar Das, Tapan Kumar Addis Ababa Science and Technology University Department of Software Engineering Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Sri Sri University Faculty of Emerging Technologies Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN. © 2024 IEEE.

关键词： Sentiment analysis

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Aspect-Based Sentiment Analysis for Social Multimedia:A Hybrid Computational Framework

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computer Systems science & engineering 2023年第8期46卷 2415-2428页

作者： Muhammad Rizwan Rashid Rana Saif Ur Rehman Asif Nawaz Tariq Ali Azhar Imran Abdulkareem Alzahrani Abdullah Almuhaimeed University Institute of Information Technology–PMAS Arid Agriculture University Rawalpindi46000Pakistan Department of Creative Technologies Air UniversityIslamabad44000Pakistan Computer Engineering and Science Department Faculty of Computer Science and Information TechnologyAl Baha UniversityAl Baha65799Saudi Arabia Digital Health Institute King Abdulaziz City for Science and TechnologyRiyadh11442Saudi Arabia

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest *** share their thoughts and feelings about various topics,including products,news,blogs,*** user reviews and tweets,sentiment analysis is used to discover opinions and *** polarity is a term used to describe how sentiment is ***,neutral and negative are all examples of *** area is still in its infancy and needs several critical *** and hidden emotions can detract from the accuracy of traditional *** methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative *** existing strategies are *** proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion ***,classification was performed using *** proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and *** results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

关键词： Aspects deep learning lexicon sentiments reviews

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