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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Sharma, Keshav Jain, Naman Upadhyay, Adarsh Mishra, Annu Chaturvedi, Ravi Prakash Pruthi, Jyoti Sharda School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Sharda School of Engineering & Technology Department of Computer Science & Applications Greater Noida India

ISBN: (纸本)9798350368109

This study summarises current advances in sign language recognition systems, emphasising trends, problems, and prospects. Twenty key research publications are analysed, spanning a wide range of sign language recognition topics, from low-power bio-inspired armband solutions to sophisticated neural network structures. These technologies' incorporation into human-robot interactions, as well as their potential to improve communication accessibility, are highlighted. The communication gap limits social integration, access to services, information, and education for individuals who rely on sign language. To address this issue, our research aims to develop an innovative method for real-time translation of sign language into spoken and written languages. We propose a comprehensive system called "SignText"that utilizes Large Language Models (LLMs) and Python programming to interpret and translate sign language into subtitles. Through this approach, we aim to bridge the communication gap and enhance the lives of individuals who use sign language. Initial results demonstrate promising accuracy, with an average recognition rate of over 90% for basic signs and gestures, and up to 85% and 70% for more complex and similar signs, respectively. These results show significant progress toward fostering greater inclusion and accessibility in social interactions and daily life. © 2024 IEEE.

关键词： Human robot interaction

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An Adaptive IDS Scheduling Framework for Host Based Mimicry Attack Detection in IoT Networks 18

An Adaptive IDS Scheduling Framework for Host Based Mimicry ...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sanyal, Surja Khatua, Manas Chattopadhyay, Pratik Indian Institute of Technology Guwahati Department of Computer Science and Engineering Guwahati India Varanasi Department of Computer Science and Engineering Varanasi India

ISBN: (纸本)9798350391725

With the expanding applicability of the Internet of Things (IoT), novel IoT network security challenges also appear more frequently. Host-based Mimicry Attacks (HMA) are one of them that is difficult to detect by traditional Intrusion Detection Systems (IDS) since they mimic legitimate network and host behaviors to evade detection. Additionally, IDSs consume host device resources when their implementations, partially or fully, are executed in the device, and thus it is needed to be scheduled properly so as to save device resources when possible. Current IDS solutions capable of detecting HMAs mostly use randombased scheduling or do not use scheduling at all and so are always executed. Random-based scheduling solutions have low HMA detection accuracy, while solutions without scheduling do not save device resources. This work proposes an adaptive IDS scheduling framework that suspends IDS execution based on device trust and resumes IDS execution when the device is under attack. Extensive experimentation using the Cooja simulator and Python script shows that the proposed framework achieves a device behavior classification accuracy of at least 97.15% when under attack while saving at least 81.98% of CPU energy when not under attack for a network of 40 devices, as compared to those when executing the IDS without any scheduling scheme. © 2024 IEEE.

关键词： Network intrusion

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An empirical evaluation of teaching–learning-based optimization, genetic algorithm and particle swarm optimization

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International Journal of computers and Applications 2023年第1期45卷 36-50页

作者： Shukla, Alok Kumar Pippal, Sanjeev Kumar Chauhan, Sansar Singh Department of Computer Science and Engineering G.L. Bajaj Institute of Technology & Management Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida India

Metaheuristic algorithms have probable to solve global optimization problems in various fields of engineering and industry. To find a global solution by exploring irregular or non-linear surfaces, classical optimization techniques are not able. To overcome the limitation of the classical approach, in the recent study, a large number of metaheuristic methods have been investigated to improve the solution quality concerning convergence and accuracy on the complex problems. Nowadays, a popular metaheuristic algorithm is introduced called teaching–learning-based optimization (TLBO). It is recently being used as an innovative, and robust method to solve the global optimization problem, inspired by the teaching–learning phenomenon. On the other hand, particle swarm optimization (PSO) algorithm is one of the most utilized algorithms in the current scenario, which has indicated acceptable efficiency. Genetic algorithm (GA) is a valuable component of metaheuristic which has been applied in various research applications. In this article, the performance of TLBO algorithm is estimated on 25 numerical test suites against other metaheuristic algorithms such as PSO and GA. The results of our experimental study show that TLBO outperforms the PSO and GA algorithm regarding convergence solution. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Genetic algorithms

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Reinforcement Learning based Adaptive Healthcare Decision Support Systems using Time Series Forecasting 5

Reinforcement Learning based Adaptive Healthcare Decision Su...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shirley, C.P. Berin Jeba Jingle, I. Abisha, M.B. Venkadesan, R. Absin, S.J. Karunya Institute of Technology and Sciences Department of Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Healthcare decision-making is a complex and crucial process that must take into account changing patient situations and medical trends. It is frequently difficult for conventional decision support systems to adjust to these shifting circumstances. In this work, we present a novel method for creating adaptive healthcare decision support systems by combining time series forecasting and reinforcement learning (RL). The main goal is to create a system that can use reinforcement learning (RL) to develop optimal policies for making decisions by utilizing time series data of patients from the past. Our system uses RL in conjunction with time series forecasting methods like LSTM (Long Short-Term Memory) networks and ARIMA (Auto Regressive Integrated Moving Average) to estimate future patient outcomes and healthcare resource requirements. There are several different components in the system architecture. The time series forecasting models are trained using historical healthcare data, which includes patient demographics, medical histories, and treatment outcomes. Forecasts for important healthcare parameters like patient recovery rates, disease progression, and resource consumption are produced by these models. According to the anticipated results, RL algorithms are used to learn decision policies. While interacting with a simulated environment that represents patient care scenarios, the RL agent aims to maximize long-term rewards, which may include reduced expenses, better patient outcomes, or more efficient use of available resources. Using actual healthcare datasets, the adaptability and efficacy of suggested approach will be assessed. The efficacy of RL-based decision policies, accuracy of projected results, and the system's adaptability to shifting healthcare dynamics are all examples of performance measures. © 2024 IEEE.

关键词： Decision support systems Adaptive systems Accuracy Time series analysis Systems architecture Medical services Reinforcement learning Predictive models Forecasting Long short term memory

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SISRR: Semantically Inclined Strategic Learning Model for Software Requirement Recommendation Using Artificial Intelligence 1

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Shyamalan, Raj Deepak, Gerard Santhanavijayan Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering BMS Institute of Technology and Management Yelahanka Bangalore India

ISBN: (数字)9783031647796

ISBN: (纸本)9783031647789

The following paper proposes the SISRR framework for knowledge centric, semantically inclined, framework for software requirement recommendations. This framework is intelligent driven by integrating semantically inclined techniques with knowledge model. The SISRR model is a metadata driven framework, where the initial requirements are preprocessed, and metadata is generated to intensify the auxiliary knowledge. This is handled by deep learning GRU classifier. Subsequently ontologies are generated from the SVN repository, which is applied along with Bag of Words to enrich the ontologies. The semantic similarity is computed using CoSimRank and Tang Index. Shannons entropy is also computed to measure the information content. The context map is generated using the DBSCAN algorithm in the model. Thus, the best precision, recall and lowest FDR has been achieved by the SISRR model compared to other existing models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Ontology

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RAPID-AV: Realtime Adaptive and Intelligent Detection For AV's Against DDoS Attacks 18

RAPID-AV: Realtime Adaptive and Intelligent Detection For AV...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Kotha, Aditya Venkanna, U. International Institute of Information Technology Department of Computer Science and Engineering Naya Raipur India National Institute of Technology Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350391725

Autonomous vehicles (AVs) increasingly rely on vehicle-to-everything (V2X) networks for communication. However, due to the devices' heterogeneity, they are more susceptible to attacks like distributed denial of service (DDoS), resulting in packet loss and transmission delays. Existing solutions to these issues often create bottlenecks and lack post-attack recovery mechanisms. To solve these problems, this paper proposes RAPID-AV, an ML-based approach that uses programmable P4 switches for real-time detection and mitigation of DDoS attacks in AV networks. The proposed system uses a threefold strategy: collect traffic statistics from AV, use P4-based programmable packet processing for anomaly detection, and use majority voting to mitigate attacks. In our experiments, we found that the decision tree algorithm achieves an accuracy of 94.33% in distinguishing traffic from DDoS traffic. In addition, RAPID-AV demonstrated a nearly 10% reduction in CPU load during attacks and maintained consistent communication between AV and RSU. This approach can help improve AV network security while addressing the limitations of existing solutions. © 2024 IEEE.

关键词： Network security

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Exploring the opportunities and challenges of ChatGPT in academia

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Discover Education 2024年第1期3卷 1-14页

作者： Islam, Iyolita Islam, Muhammad Nazrul Department of Computer Science and Engineering Bangladesh University of Professionals (BUP) Dhaka 1216 Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Dhaka 1216 Bangladesh

The Artificial Intelligence (AI) natural language model ChatGPT (Chat Generative Pre-trained Transformer), often referred to as ChatGPT-4, has a wide range of possible uses in the fields of research, business, academia, health, and similar fields. This language model can accomplish a number of academic jobs that were previously completed by people, taking a significant amount of time and effort. The purpose of the article is to investigate ChatGPT’s potential opportunities and challenges in academia. To attain this objective, a review of relevant literature and online resources (news, articles, etc.) was carried out. The noticing-collecting-thinking approach was adopted to explore and categorize all observed concerns. The outcome of this study reveals that research, education, personal skill development, and social aspects constitute the four broad perspectives that articulate the opportunities and constraints of ChatGPT in academia. For instance, from the education perspective, ChatGPT can help students have a personalized learning experience. On the other hand, it might provide false information as well as lack the ability to generate responses on its own because those responses depend on training datasets, which may contain errors. Similarly, from the point of view of the personal skill development, this model may impair a person’s capacity for critical thought and content production;while providing reading and writing practice sessions and relevant content, it can improve a person’s language proficiency. © The Author(s) 2024.

关键词： ChatGPT, GPT-3, GPT-4 academia, Challenges, Opportunities, Conceptual analysis, Artificial Intelligence, Education, Writing

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Gender-Based Analysis of User Reactions to Facebook Posts

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Big Data Mining and Analytics 2024年第1期7卷 75-86页

作者： Yassine El Moudene Jaafar Idrais Rida El Abassi Abderrahim Sabour Mathematical Engineering and Computer Science Laboratory Faculty of SciencesIbn Zohr UniversityAgadir 8706Morocco Department of Computer Science Mathematical−High School of Technology Ibn Zohr UniversityAgadir 8706Morocco

Online Social Networks(OSNs)are based on the sharing of different types of information and on various interactions(comments,reactions,and sharing).One of these important actions is the emotional reaction to the *** diversity of reaction types available on Facebook(namely FB)enables users to express their feelings,and its traceability creates and enriches the users’emotional identity in the virtual *** paper is based on the analysis of 119875012 FB reactions(Like,Love,Haha,Wow,Sad,Angry,Thankful,and Pride)made at multiple levels(publications,comments,and sub-comments)to study and classify the users’emotional behavior,visualize the distribution of different types of reactions,and analyze the gender impact on emotion *** of these can be achieved by addressing these research questions:who reacts the most?Which emotion is the most expressed?

关键词： profiling knowledge extraction data mining emotion mining social media data crawling Facebook reactions gender

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Pioneering Excellence in Semiconductor Manufacturing via XGBoost with BAT Optimization for Cutting-Edge Predictive Maintenance

Pioneering Excellence in Semiconductor Manufacturing via XGB...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Dwivedi, Abhishek Khan, Nikhat Raza IES University Department of Computer Science & Engineering Bhopal India IES College of Technology Department of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350374100

This study aims to revolutionize predictive maintenance in semiconductor manufacturing by using the SECOM dataset. The main objective is to create a precise and flexible predictive model by combining the XGBoost algorithm with BAT optimization techniques. The methodology involves intricate data preprocessing, incorporating data cleaning, handling multicollinearity, and using SMOTE-EEN to address the imbalanced SECOM dataset. The model is built using the robust XGBoost algorithm, and hyperparameter tuning is performed using BAT optimization techniques. The research results show great potential, with the model achieving a high accuracy of 0.99 and a strong F1 score of 0.95. The results confirm the model's effectiveness in predicting equipment failures in semiconductor manufacturing processes. The combination of XGBoost and BAT optimization techniques highlights the robustness and flexibility in dealing with the complex nature of the SECOM dataset. This study represents a significant advancement in predictive maintenance for semiconductor manufacturing. The integrated model provides a cutting-edge fault prediction solution, minimizing downtime and improving production efficiency. © 2024 IEEE.

关键词： Data handling

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