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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Kakkar, Madhur Daniel, John Sudha, D. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331538538

Sign language is the communication medium that is utilized by the hearing-impaired community. The medium is visual and it uses hand signs and symbols for communication. Over 300 different types of sign languages present in the world today. Sign language uses gestures and hand movements for the majority of the words and finger spellings for the rest. Nonverbal speakers face a lot of challenges while interacting with verbal speakers. Even non-verbal speakers who speak different sign languages face difficulties communicating with each other. This project proposes an enhanced real-time system which detects gestures and finger spellings of multiple sign languages, and translates them into multiple spoken languages. The proposed system aims to utilize CNN network for finger spelling recognition and Long Short-Term Memory (LSTM) network for gesture interpretation. The system will use computer vision for accurate gesture detection and Google Translator API for recognizing signs which will be translated into multiple spoken languages. Initial tests with isolated datasets indicate promising accuracy for both finger spelling and gesture recognition. The goal of the completed system is to help aid communication between verbal and non-verbal speakers and to provide an efficient mode of communication between the two communities. © 2025 IEEE.

关键词： Interactive computer systems

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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8th International Conference on Electronics, Materials Engineering and Nano-technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 IEEE.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

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Hate Speech Detection of Roman Urdu Using Transformer Models 19

Hate Speech Detection of Roman Urdu Using Transformer Models

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19th International Conference on Emerging Technologies, ICET 2024

作者： Ullah, Rehman Khan, Waseem Ullah Marwat, Safdar Nawaz Khan Ullah, Jasim Irshad, Fatima University of Engineering & Technology Dept. of Computer Systems Engg Peshawar Pakistan

ISBN: (纸本)9798350379587

Hate speech means that giving discrimination about someone, such as a person, a group or any community on the base of religion, personality of someone, race, sexual harassment, gender, or on the base of other identities. To prevent hated speeches different laws has been made by the governments of different countries. Which has worked a lot for the citizen's safety of these countries and their citizens are safe from such hated speeches up to some extent. Too many researchers and scientists has worked a lot in NLP to prevent hated speeches throughout the world, and they been successes up to some extent in their aim. As we all see nowadays on different platforms of social media that hated speeches are detected. We have used some of transformer methods and some of non-transformer methods, such as BERT Bidirectional Encoder Representations from Transformers (BERT) in transformer method and DISTEL BERT in transformer method and transformer model in transformer method. And we have LSTM, LSTM + attention layer, CNN and LSTM in nontransformer method. And we have trained it by one of the roman Urdu datasets which consists 30000 thousand of roman (Urdu) tweets, moreover we will check or look for accuracy precession, F1 score and recall in it. Then we compared those models in which BERT has the best result that's transformer model. © 2024 IEEE.

关键词： Natural language processing systems

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QoS-Constrained Service Migrations in MEC Using Dynamic Service Prioritisation Algorithm

QoS-Constrained Service Migrations in MEC Using Dynamic Serv...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Velrajan, Saravanan Jeyaprakash, Thangakumar Hindustan Institute of Technology and Science Dept. of Computer Science Chennai India

ISBN: (纸本)9798350379716

The rapid expansion of 5G networks and the increased focus on user experience has resulted in the growth of Multi-access Edge Computing (MEC) at the edge of wireless networks. The indeterministic workloads handled by MEC often lead to overloaded applications. Services migrations are performed in MEC to manage the application performance, potentially minimising the impact on the user's Quality of Experience (QoE). While prior research on application migrations in MEC has primarily focused on subscriber mobility, energy management, and resource optimisation, a gap exists in understanding how these migrations impact application performance in MEC environments with stringent Service Level Agreements (SLAs). This research proposes a new approach for service migrations in MEC based on dynamic service prioritisation. The proposed Dynamic Service Prioritisation Algorithm (DSPA). uses a variable application performance watermark to trigger migrations, enabling MEC infrastructure to dynamically adapt to indeterministic workloads. DSPA selects services for migration by optimally balancing service load and migration time parameters. Through extensive simulations, our study proves that DSPA is best suited for managing the performance of diverse applications hosted in MEC. DSPA exhibited superior performance in minimising SLA violations and mitigating user experience impact during service migrations compared to other contemporary algorithms. © 2024 IEEE.

关键词： 5G mobile communication systems

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Leveraging ML to Detect Fraudulent Customs Declaration: A Case of Logistic Regression and LightGBM Algorithms 4

Leveraging ML to Detect Fraudulent Customs Declaration: A Ca...

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4th International Conference on Next Generation Computing Applications, NextComp 2024

作者： Chilomo, Innocent Kankuzi, Bennett Malawi University of Science and Technology Dept. of Computer Science and IT Thyolo Malawi

ISBN: (纸本)9798331517403

Harmonised System (HS) code fraud, also known as Customs tariff classification fraud, is common in developed and developing countries. It occurs when traders intentionally mis-classify goods during the time of clearance. Classification fraud results in tax revenue loss, restricted and prohibited goods entry, and unfair market competition. Current manual verification techniques to detect classification fraud are time-consuming and error-prone;thus, an effective and precise strategy is required. In this study, we developed and evaluated the performance of Logistic Regression and LightGBM machine learning models in detecting HS Code fraud using a dataset obtained from a government Revenue Authority in a developing country. Results show that the LightGBM model outperformed Logistic Regression. The LightGBM model demonstrated superior accuracy (0.961), precision (0.890), recall (0.901), and F1 score (0.896) as compared to the Logistic Regression model, which had accuracy (0.932), precision (0.772), recall (0.900), and F1 score (0.831). Our contribution is threefold: Firstly, the dataset used is from a developing country, unlike other studies that use datasets from developed countries. Secondly, we worked with Customs import data collected at an 8-digit HS code level, whereas most similar studies typically used HS codes at 2, 4, or 6-digit levels. Lastly, unlike comparable studies, we have compared the performance of Logistic Regression and LightGBM machine learning algorithms. © 2024 IEEE.

关键词： Logistic regression

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Using Game Theory to Prioritize Goals in Requirements Engineering 4

Using Game Theory to Prioritize Goals in Requirements Engine...

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4th International Conference on Next Generation Computing Applications, NextComp 2024

作者： Munthali, Isaac Kankuzi, Bennett Malawi University of Science and Technology Dept. of Computer Science & IT Thyolo Malawi

ISBN: (纸本)9798331517403

Prioritizing competing goals among different stakeholders in requirements engineering is usually a daunting task for system architects as each of the stakeholders have their own interests. This study modeled this conflicting environment among stakeholders as a non-cooperative game in game theory with goals modeled as strategies and utility value of each goal as pay-offs in a pay-off matrix. In a user study that employed the NashPy algorithm to deduce the Nash equilibrium of the strategies in a two player game, the tool was able to automatically deduce goals which needed to be prioritized to achieve a win-win situation for two stakeholders. This was also confirmed by stakeholders. The paper has three contributions: modelling goals for stakeholders in requirements engineering as a non-cooperative game in game theory;automatically deducing goals that need prioritization among stakeholders in requirements engineering based on the Nash equilibrium;and performing a user study to validate the deduced prioritized goals. © 2024 IEEE.

关键词： Requirements engineering

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Effective Machine Learning Model for Disease Recognition and Categorization of Mango Leaves

Effective Machine Learning Model for Disease Recognition and...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Kumar, Sandeep Gupta, Bhupesh Grover, Richa Chhabra, Mohit Computer Science & Engineering Dept. Ambala Mullana India Panipat Institute Of Engineering & Technology Computer Science & Engineering Dept. India

ISBN: (纸本)9798350306446

Maintaining the health and production of mango farms depends on the early detection of illnesses in mango trees. Despite the vital role agriculture plays, it remains an industry receiving limited attention from the machine-learning community. A noteworthy challenge is the lack of standardized and openly accessible agricultural datasets, impeding researchers from fully leveraging the potential of advanced computational prediction tools. The study focuses on employing deep learning and image processing techniques for diagnosing prevalent mango leaf diseases in India. The goal is to enhance both the quality and quantity of agricultural production by implementing effective disease control measures upon identifying plant diseases. The automation of plant disease detection proves particularly beneficial, reducing the monitoring efforts required by large farms. Rapid identification of leaf diseases is crucial, given that leaves serve as a plant's primary source of nutrition. Notably, none of the seven different mango leaf illnesses that have been observed in India have ever been the subject of earlier research. In this study, we provide a lightweight Resnet50 model that can reliably classify healthy mango leaves and the seven discovered illnesses on mango leaves. The performance of our proposed lightweight Resnet-50 model is evaluated against many pre-trained models, such as Exception, Resnet101, and VGG16. The findings reveal that the lightweight Resnet-50 model achieves the highest testing accuracy, reaching an impressive 95%. This underscores the model's effectiveness in automating the identification of mango leaf diseases, offering a promising solution for improving agricultural practices in India. © 2024 IEEE.

关键词： Diseases

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A comprehensive step of XAI for Bengali and English text detection and recognition from natural scene images 2

A comprehensive step of XAI for Bengali and English text det...

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2nd International Conference on Networking, Embedded and Wireless systems, ICNEWS 2024

作者： Dutta, Mithun Das, Utpol Kanti Das, Jugal Krishna Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350367461

A critical first step in many applications, including augmented reality, document analysis, and scene comprehension, is text detection from images. Even though text identification for the English language has advanced significantly, there are particular difficulties when applying these methods to languages such as Bengali because of variations in script, morphology, and typographic characteristics. This article offers a wonderful method for leveraging the Faster R-CNN framework to identify and categorize text from images that contain Bengali and English sentences. The study utilizes a dataset named "Bengali and English text image dataset (BETID)"that consists of images with text in both Bengali and English scripts, taken from a variety of sources such as books, documents, natural scenes, internet images, and landscapes. Pre-processing methods are used to improve the resilience and generalization of the model, including image scaling, normalization, and augmentation. The Faster R-CNN architecture is tuned and trained to identify text regions in images and categorize Bengali or English text. The performance of the text Classification model is evaluated using evaluation measures such as accuracy on the BETID dataset. The proposed Faster R-CNN model achieved an average of 97.90% accuracy, which is better than other deep learning models. Empirical findings showcase the capacity of transfer learning to alleviate data scarcity concerns and enhance text detection precision for low-resource languages such as Bengali.) © 2024 IEEE.

关键词： Deep learning

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Smart Vehicle Procurement System using Blockchain technology 3

Smart Vehicle Procurement System using Blockchain Technology

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Boddu Manoj Rajeswari, G. Duraipandian, Kavitha David, Shiela Dept. of Computer Science and Engineering with specialization in Blockchain Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

Blockchain technology has become more widespread in our lives revolutionizing various industries through improved security, transparency, and operational efficiency. Its decentralized ledger system is particularly effective in areas like finance and supply chain management ensuring data integrity and reducing the need for middlemen. This study explores how blockchain technology is used in the process of buying vehicles highlighting its benefits compared to methods. Traditional vehicle purchases involve a network of dealers, banks and regulators leading to inefficiencies, higher costs and potential fraud. The usual process involves steps like financing checking vehicle history and changing ownership. Each requiring extensive paperwork and third-party intervention. In contrast using blockchain for vehicle purchases can simplify things through contracts that automate tasks like verifying history securely transferring ownership instantly settling payments. The unchangeable nature of records ensures that all transaction data remains secure against tampering fostering trust between buyers and sellers. Furthermore, blockchain can cut costs significantly by removing the need for middlemen such, as escrow services and title companies while reducing burdens. The advanced security elements built into technology help reduce the chances of fraudulent actions like altering odometer readings or selling stolen cars. This study showcases a comparison illustrating how blockchain can simplify the process of buying vehicles offering an economical and secure experience. © 2025 IEEE.

关键词： Decentralized finance

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Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第14期48卷 27-42页

作者： Alabbasi, Awab Aljabery, Maalim A. Bölük, Pınar Sarısaray Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Bahcesehir University Istanbul34353 Turkey Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah61004 Iraq Faculty of Engineering and Natural Sciences Software Engineering Dept. Bahçeşehir University Istanbul34353 Turkey

VGIS (Virtual Geographic Information System) Platform is a unified oilfield operations management platform based on MaaS (Management as a Service) that integrates advanced technologies such as AIoT (Artificial Intelligence & Internet of Things), GIS (Geographic Information System), Digital Twin, and AR (Augmented Reality)/ VR (Virtual Reality). The VGIS platform connects, collects, integrates, analyses, and acts on disparate data sources to provide the oilfield with a centralized view to help make informed decisions fast, bring operational visibility across all assets to improve safety and operational efficiency, reduce costs and improve margins, provide comprehensive analytics and decision support, increase business agility, and reduce management complexity. This is a case study of the application of the VGIS platform Majnoon oilfield, Basrah, Iraq. VGIS digital transformation, production process, and KPIs (Key performance indicators) are highly managed taking oilfield management to a higher level globally, in this study, the objective is to address and resolve the issue of high costs associated with using cloud services by utilising a local Data Center (a large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data). This approach involves directly uploading 3D maps to ensure faster service on the intranet, eliminating the need to pay for cloud *** aims providing a platform for researching and implementing monitoring and controlling processes in a more convenient and remote manner. The vGIS platform has emerged as a crucial instrument in the realm of disaster response and crisis management. This technology enables responders to rapidly evaluate, analyse, and respond to crucial information in real-time. This research examined the application of vGIS technology in disaster response and analyse the potential advantages and obstacles associated with its utilisation. © 2

关键词： Information management

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