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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Kyaw, Nwe Ni Mitra, Pabitra Sinha, G.R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Indian Knowledge System Lab International Institute of Information Technology Bangalore Karnataka Bengaluru560100 India

ISBN: (纸本)9789819738588

The proposed system for recognizing Myanmar sign language between individuals who are deaf. The aim of this study is to develop deep learning models for the purpose of accurately identifying dynamic hand gesture images depicting 14 classes of Myanmar words and numbers. The present system utilizes a new dataset comprising 7834 images for 14 classes obtained through Webcam. The objective of this study is to enhance the robustness of the new model. Next, we proceed to evaluate the proposed model by comparing them to those of existing convolutional neural network models. These models are generally recognized as effective deep-learning approaches for recognition tasks. The proposed model exhibits a performance level of 96% for both validation and testing accuracies. Furthermore, the experimental finding demonstrates that the model, convolutional layers, achieved an accuracy of 100% in recognizing Myanmar Sign Language (MSL). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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OFDM-structure based waveform designs for integrated sensing and communication

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science China(information sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

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A Robust Security Detection Strategy for Next Generation IoT Networks

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computers, Materials & Continua 2025年第1期82卷 443-466页

作者： Hafida Assmi Azidine Guezzaz Said Benkirane Mourade Azrour Said Jabbour Nisreen Innab Abdulatif Alabdulatif Technology Higher School Essaouira Cadi Ayyad UniversityEssaouira44000Morocco IMIA Laboratory MSIA TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknesErrachidia50050Morocco CRIL-CNRS Artois UniversityLens62300France Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah52571Saudi Arabia

Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating *** connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented *** ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and *** these approaches,Intrusion Detection systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of *** study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification *** combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of *** analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT *** results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack *** the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of *** work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.

关键词： IoT security intrusion detection RF KNN SVM EL NSL-KDD Edge-IIoT

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OCR-CRNN (WBS): an optical character recognition system based on convolutional recurrent neural network embedded with word beam search decoder for extraction of text

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International Journal of information technology (Singapore) 2025年 1-8页

作者： Kumar, Mahesh Shalu Dureja, Ajay Narula, Rachna Shyla Arora, Ravi Department of Information Technology Bharati Vidyapeeth’s College of Engineering (BVCOE) New Delhi India Department of Computer Science and Technology Manav Rachna University Haryana Faridabad India Department of Computer Science & Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India

With the growing need for accurate digital text retrieval from images, OCR (optical character recognition) plays a critical role in fields such as document digitization, automated data processing, and information retrieval. The given paper introduces optical OCR system, comprising Convolutional Recurrent Neural Network (CRNN) embedded with a Word Beam Search (WBS) decoder for extraction of text. The framework performs preprocessing, feature extraction, and post-processing strategies that enhance the accuracy and robustness of text extraction. Preprocessing techniques and post-processing adjustments significantly improved the model's ability to handle diverse image formats, including scanned documents and screenshots. The proposed system demonstrates an overall accuracy of 92%, with macro and weighted averages across precision, recall, and F1-scores also at 92%. Individual class evaluations reveal notable performance consistency, with precision ranging from 0.79 to 0.98, recall between 0.79 and 0.96, and F1-scores from 0.79 to 0.94 across 26 alphabetic characters. This research highlights the system's potential for real-world applications, demonstrating its scalability, precision, and efficiency in extracting text data across various formats, ensuring high OCR accuracy in automated data processing. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： CRNN Document Digitization Optical character recognition (OCR) Text extraction WBS

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Lightweight Adaptive Learning Algorithm for Energy-Latency Tradeoff in IoMT-Enabled Edge Computing 17

Lightweight Adaptive Learning Algorithm for Energy-Latency T...

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17th International Conference on Machine Vision, ICMV 2024

作者： Yadav, Rahul Shafiq, Muhammad Kumar, Mohit Wei, Li Elaziz, Mohamed Abd College of Computer Science and Technology Harbin Engineering University Harbin China Cyberspace Institute of Advance Technology Guangzhou University Guangzhou China Department of Information Technology National Institute of Technology Jalandhar India Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt

ISBN: (纸本)9781510688278

Smart Healthcare is witnessing widespread implementation of Internet-of-Medical-Things (IoMT) devices. These devices play a crucial role in collecting vast amounts of data from various smart healthcare applications, which are then processed to facilitate informed decision-making. Edge computing has emerged as a valuable platform offering computational resources to handle this data collection efficiently. However, a critical challenge arises from the inappropriate and inefficient classical approaches to fair resource allocation in these energy-intensive, short battery life and delay-intolerant portable devices. To address this issue, this paper presents a Lightweight Adaptive Learning Offloading Algorithm designed to optimize energy and latency in an edge computing environment. The proposed algorithm formulates the problem as a combined minimization of latency and energy costs while satisfying constraints related to limited battery capacity and service latency deadlines. To achieve this, the algorithm employs the adaptation parameters (α, β, γ), and the tradeoff target (C′) is configured based on the specific task requirements. Experimental results demonstrate the advantages of the proposed scheme, including energy savings and minimized latency in an edge environment. To validate the effectiveness of the proposed scheme under a realistic scenario using the iFogSim2 simulator. © 2025 SPIE.

关键词： Electronic health record

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Performance Assessment of Sequential Models for Solar Radiation Forecasting Over Varying Forecast Horizon 11th

Performance Assessment of Sequential Models for Solar Radi...

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11th International Symposium on Applied Computing for Software and Smart systems, ACSS 2024

作者： Middya, Asif Iqbal Roy, Sarbani Paul, Agnish Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Information Technology Jadavpur University Kolkata India

ISBN: (纸本)9789819797615

A precise projection of solar system output is critical for efficient power grid operation and effective energy management. Now, forecasting solar radiance is a crucial part of this projection process. It may be done in a number of ways, such as by using AI-based techniques and remote sensing data with physical models. The recent literature explored the potential various sequential models such as LSTM and its variants for solar radiation forecasting. However, there is not much research done on how well the sequential models perform when forecasting solar radiation across different prediction horizons. In this context, this study performs the performance assessment of some state-of-the-art sequential models such as LSTM, BiLSTM, Hybrid LSTM (proposed model), Simple RNN and Stacked LSTM for solar radiation forecasting over varying forecast horizon (h = 5 h to h = 45 h). The experimental results highlight that although the stacked LSTM, Hybrid LSTM and BiLSTM generally demonstrated better performance for most horizons and performance metrics, the proposed hybrid model has been dominated most of the time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Solar irradiance

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AI-driven intrusion prevention system: leveraging mathematical foundations for advanced threats detection

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International Journal of information technology (Singapore) 2025年 1-6页

作者： Vashistha, Avneesh Alghamdi, Tami Abdulrahman Almalki, Sultan Ahmed Ead, Waleed M. Kaur, Gaganpreet Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Ghaziabad India Computer Science Department Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Computer Department Applied College Najran University Najran Saudi Arabia Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef Egypt Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

The given paper introduces a multi-staged AI-driven intrusion prevention system (IPS) that utilizes mathematical foundations like statistical analysis, probabilistic models, and optimization techniques to design strategic algorithms that recognize patterns as well as detect anomalies & potential attack vectors, which usually get missed out on by traditional security tools. The study focuses on the hybrid behavior that combines supervised and reinforcement learning (RL) methods to provide useful analysis of network traffic, which improves early detection of threats, whether they are known or unknown. This research demonstrates how the proposed AI-driven model can achieve a substantial decrease in the rate of false positives and improve threat detection accuracy. Thus, the given framework acts as a proactive component against today’s evolving cybersecurity threats and hence allows the creation of an efficient and responsive enterprise security framework. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Feedback loop Intrusion prevention Network traffic Reinforcement learning Threat detection

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Movable-Antenna-Assisted Covert Communications With Reconfigurable Intelligent Surfaces

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IEEE Internet of Things Journal 2025年第9期12卷 12369-12382页

作者： Xie, Wenwu Li, Zilong Yu, Chao Xu, Hongbo Wang, Ji Wu, Weimin Li, Xingwang Yang, Liang Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Central China Normal University College of Physical Science and Technology Department of Electronics and Information Engineering Wuhan430079 China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This article proposes a novel covert communication framework utilizing movable antennas (MAs) to enable covert communications in which the evading detection eavesdropper aided by a reconfigurable intelligent surface (RIS). The trajectories of the MAs over the entire time slot, transmit beamforming, and the phase shift of the RIS in each time slot are jointly optimized to improve the covert rate. Specifically, the movement trajectories of the MAs are modeled as a Markov decision process (MDP), optimized by developing a novel deep reinforcement learning (DRL) approach. Furthermore, an alternating optimization (AO) algorithm is designed to jointly optimize the beamforming and phase. In particular, penalty-based two-layer iterative algorithm is proposed to guarantee that the solution satisfies the rank-one constraints. Numerical results show that the proposed MA-assisted covert communications system significantly outperforms conventional fixed-position antenna (FPA) schemes in terms of covert rate. © 2014 IEEE.

关键词： Markov processes

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Enhanced English Sentence Pair Classification Through KIRINLog Technique 5

Enhanced English Sentence Pair Classification Through KIRINL...

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5th International Conference on Advances in Electrical, Electronics and Computing technology, EECT 2025

作者： Ounprasert, Patthanan Anekboon, Khantharat Department of Computer and Information Sciences Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798331541545

Natural language processing is a rapidly evolving technology in an era of quick technological growth. Conventional rule-based methods are insufficient to meet the demands of today's complex challenges. One of the significant tasks in natural language processing is recognizing textual entailment. Recognizing textual entailment is vital in natural language processing as it enables systems to understand the relationship between texts, which is crucial for applications like text summarization, sentiment analysis, information verification, question answering, text classification, and machine translation. One challenge is reducing the size of the input vectors while maintaining good prediction results, F1 scores. To overcome this challenge, this research presents the novel encoding technique by reducing the encoding size for the sentences involving compositional knowledge dataset. This dataset is sentence pairs in English to classify textual entailments into three categories: entailment, neutral, and contradiction. This research proposes the KIRINLog technique, which consists of six main methods: preprocessing, removing the same word, max-length padding and truncation for sentence alignment, adding a flag value, word embedding by Word2Vec, and dimensionality re-duction with principal component analysis to reduce the size of input vector. All of six main processes are proposed to create feature vectors for recognizing textual entailment (the sentences involving compositional knowledge dataset). This study used an attention-based bidirectional LSTM as a classification model. This model combines bidirectional LSTM with an attention to make analyzing sequences in both forward and backward directions and to help the model focus on important information in the sentence. This proposed technique achieved an accuracy of 92.4%, with precision at 88.8%, recall at 88.6%, and an F1-score of 88.6% on the 2014 sentences involving compositional knowledge dataset. © 2025 IEEE.

关键词： Sentiment analysis

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A q-learning approach for enhanced routing in dynamic LEO satellite networks

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Egyptian Journal of Remote Sensing and Space science 2025年第2期28卷 272-279页

作者： Riyad, Ahmed E. Mokhtar, Medhat Belal, Mohamed A. Bahloul, Mahmoud Mohamed Department of Information System El-Obour Institutes Egypt National Authority for Remote Sensing and Space Sciences Egypt Computer Science Faculty of Computing & Artificial Intelligence Helwan University 6 October Technology University Cairo Egypt Business Information Systems Business Information Systems Department Faculty of Commerce and Business Administration Helwan University Cairo Egypt

As global communication demand rises, Low Earth Orbit (LEO) satellite systems offer high-speed data transmission and extensive coverage options but face routing challenges due to dynamic topologies. This paper introduces a Q-Learning-based routing approach that converts dynamic networks into virtually static topologies at different snapshot intervals. Simulation results on a 66-satellite Starlink constellation demonstrate that Q-Learning outperforms Dijkstra's algorithm, achieving faster convergence and reduced latency. These findings highlight the potential for Q-Learning in enhancing efficient, cost-effective satellite communications. © 2025 The Authors

关键词： Satellite communication systems

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