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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Sivakavi, S Thillai Minu, R.I. Srm Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur603203 India

ISBN: (纸本)9798331521394

The sign language retrieval system, while similar to conventional video retrieval systems, faces distinct challenges that require a thorough understanding of the meanings conveyed through human actions depicted in the videos. Typically, meaningful information is extracted using RGB feature extraction techniques. However, this method can lead to redundancy, as it often fails to capture the global contextual information inherent in each video frame. Additionally, the reliance on RGB features can present significant memory challenges due to the large data requirements associated with processing and storing this information. Consequently, a more efficient approach is necessary to enhance both the accuracy and effectiveness of sign language retrieval. To address these issues, we have implemented a semantic masked transformer architecture specifically tailored for the sign language retrieval task. This innovative system not only incorporates RGB features but also integrates pose features, which are derived from the movements and positions of the signer. By fusing these two types of features based on their similarity, the system can provide a more refined and pertinent retrieval of textual information related to the video frames. Our extensive experiments conducted on a custom dataset designed for Tamil Sign Language demonstrate that this framework significantly outperforms current state-of-the-art methods. These results underscore the effectiveness of combining semantic understanding with advanced feature extraction techniques, ultimately leading to improved accuracy and efficiency in sign language retrieval. © 2025 IEEE.

关键词： Online searching

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Gun Detection: A Comparative Study of RetinaNet, EfficientDet and YOLOv8 on Custom Dataset 21

Gun Detection: A Comparative Study of RetinaNet, EfficientDe...

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21st IEEE International Conference on computer Applications, ICCA 2024

作者： Khin, Pyone Pyone Htaik, Nay Min Mandalay Technological University Department of Computer Engineering and Information Technology Mandalay Myanmar

ISBN: (纸本)9789998601673

The escalating threat of armed robberies has prompted the urgent need for advanced detection systems. Current solutions in the market lack the ability to autonomously identify and signal the presence of firearms during robbery incidents. To overcome this limitation, this research proposes a novel approach based on deep learning techniques, specifically employing the RetinaNet, EfficientDet, and YOLOv8 (You Only Look Once) models for gun detection. The objective of this study is to develop a sophisticated system that can automatically and accurately identify firearms in surveillance footage during robbery activities. The three selected models bring unique strengths to the system, contributing to its overall efficacy. The proposed system aims to revolutionize the way we address security challenges associated with armed robberies. By leveraging the capabilities of deep learning models, it promises to provide a more proactive and accurate means of gun detection, thus improving overall public safety. The implementation proposed in this study employs a custom robbery detection dataset specifically designed for robbery detection, encompassing classes such as gun, no-gun, knife, no robbery activity, and robbery activity. This study offers a comparison of gun detection algorithms, specifically focusing on different three models which are RetinaNet, EfficientDet and YOLOv8. Through a meticulous evaluation of the three models' performance on this custom dataset, the good results are obtained according to mAP. Among them, it becomes apparent that the YOLOv8 architecture surpasses others, achieving a remarkable accuracy of 91.4% Mean Average Precision (mAP) in robbery detection. © 2024 IEEE.

关键词： Deep learning

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Prevention of Alzheimer Disease by Predicting Oral Issues and Its Impact on Individuals at Early Stage and Indicating Using Grey Wolf Optimization Technique

Prevention of Alzheimer Disease by Predicting Oral Issues an...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Jayareka, K.S. Sathiyabhama, B. Sobiyaa, P. Fathima, S.K. Sona College of Technology Department of Computer Science and Engineering Tamil Nadu Salem India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam Tamil Nadu Erode India

ISBN: (纸本)9798331509828

Nearly 3.5 billion people are affected by oral diseases among 8,045,311,447 world population. Most of the disease is because of the bacteria present inside the mouth, which is untreated, and it becomes tooth decay in most of the young children, gum problems for those who are above 30 years old and above 65 years old are affected by tooth loss. Other than these issues, the dental practitioners recommend humans to take care of their teeth and oral hygiene by the best practice methods like brushing twice a day, using flaws in the bacteria affected area by finding out using plaque disclosing agents like liquid solutions or pills. Improper oral hygiene leads to heart attacks, stroke, diabetics, bone issues, Alzheimer's problems and several other issues without indicating any tooth pain. Human judgement varies from person to person, and it is difficult to predict. Here different AI algorithms such as grey wolf optimizer algorithm in deep learning methods infused with generative AI are for improved treatment outcomes through the analysis of dataset comprising dental records and clinical information. From the above process AI models learn to recognize patterns, identify abnormalities for predicting oral issues of young individuals of 30 plus aged people indicating to them their status of oral hygiene and alarming the effect of Alzheimer if the features set matches. Different oral features of 30 years and above are taken for study compared with Alzheimer feature. This research makes us understand the different issues that fall under improper oral hygiene and what are the tools techniques used to help both dentist and human for better health and accurate prediction and ensure the oral hygiene at our place and avoid Alzheimer disease in future. © 2025 IEEE.

关键词： Neurodegenerative diseases

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NADR-Net: A Deep Learning Framework for Predicting Neurological Adverse Drug Reactions Using 17 Molecular Descriptors

NADR-Net: A Deep Learning Framework for Predicting Neurologi...

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2024 International Symposium on Computing and Intelligent Systems, SCI 2024

作者： Chaurasia, Anushka Kumar, Deepak Yogita Department of Computer Science and Engineering National Institute of Technology Meghalaya India Department of Computer and Engineering National Institute of Technology Kurukshetra India

Neurological adverse drug reactions (NADRs) pose a significant clinical challenge as they can have a profound impact on patient health and treatment outcomes. While diverse drug descriptors have been employed for neurological ADR prediction, the potential of using 17 molecular descriptors for this purpose has not been explored. To address this, a multilabel NADR-Net and MLSMOTE-based framework have been proposed for neurological adverse drug reaction prediction. The data for 17 MD and ADRs were extracted from PubChem and ADRECs databases and then mapped based on drug ID. The resulting dataset contained information on 2160 drugs, including their molecular properties, and 1030 ADRs. The methodology was then applied to this dataset, and it showed promising results in terms of hamming loss, precision, true positive rate, f1 score, and ROC-AUC. This study highlights the potential of using molecular descriptors for predicting neurological ADRs, which could improve patient outcomes and drug safety. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep neural networks

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SciTexC: A Framework for Scientific Text Classification in Bengali using Transformer-Based Techniques 4

SciTexC: A Framework for Scientific Text Classification in B...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Nahiyan, Fahmida Hoque, Mohammed Moshiul Chittagong University of Engineering and Technology Department of Computer Science & Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9798331530938

Scientific text classification (STC) in NLP automatically categorizes texts into predefined scien-tific domains. Although there is enormous progress in STC regarding high-resource languages, it must be explored in low-resource languages, specifically Ben-gali. This study introduces a framework, SciTexC (Scientific Text Classifier), which uses a transformer-based technique that classifies Bengali texts into six scientific domains: Physics, Chemistry, Biology, information and Communication technology (ICT), Mathematics, and Others. A new corpus, STexD (scientific text dataset), containing 6000 Bengali texts, was also developed for classification tasks. Five transformer-based models were used to perform the task, including mBERT, dmBERT, Bangla BERT, Indic BERT, and XLM-RoBERTa. The evaluation results demonstrate that XLM-RoBERTa outperformed the other exploited models, achieving the highest F1 score (0.9158). © 2025 IEEE.

关键词： Classification (of information)

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Driving Efficiency: A Literature Review of Energy-Efficient Data Collection Strategies for Mobile Sink-Based Wireless Sensor Network 1

Driving Efficiency: A Literature Review of Energy-Efficient ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Upadhyay, Priyanka Meena, Uma Rana, Kamlesh SRM Institute of Science and Technology Department of Computer Science and Engineering Uttar Pradesh Modinagar India Bharat Institute of Technology and Science Department of Computer Science and Engineering Uttar Pradesh Meerut India

ISBN: (纸本)9798350353648

Portable sinks assume a vital part in information assortment within wireless sensor networks (WSNs), offering dynamic and flexible solutions to address the limitations of static sink-based architectures. Unlike their static counterparts, mobile sinks have the capability to traverse the network, adapt their trajectories, furthermore, gather information straightforwardly from sensor hubs. This versatility empowers a few critical benefits in information assortment proficiency, energy streamlining, and network execution upgrade. This survey paper presents an extensive outline of the writing on energy-effective information assortment plans for portable sink- based remote sensor organizations (WSNs). With the expansion of Web of Things (IoT) applications, WSNs have become integral for collecting data from the environment in various domains. However, traditional static sink-based approaches suffer from energy imbalances and limited network lifespans. The integration of mobile sinks offers a promising solution to these challenges by enabling dynamic data collection and energy-efficient routing. © 2024 IEEE.

关键词： Data collection

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A New HARQ Scheme for 5G Systems via Interleaved Superposition Retransmission

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China Communications 2023年第4期20卷 1-11页

作者： Qianfan Wang Li Chen Xiao Ma School of Computer Science and Engineering Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhou 510006China College of Computer Science Jiaying UniversityMeizhou 514015China School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510006China

Within the framework of the 5G new radio(NR),we propose a new hybrid automatic repeat request(HARQ)scheme to improve the throughput *** difference between the proposed scheme and the conventional one lies in the first retransmission,where the erroneous coded block group is interleaved and superimposed(XORed)onto a fresh coded block *** the receiver,an iterative message-passing decoding algorithm can be employed to recover the target erroneous code block group(CBG).Only when the superposed retransmission fails,the conventional incremental redundancy(IR)or repetition redundancy(RR)retransmission is *** any case,since the first retransmission is along with but has negligible effect on the fresh CBG,it costs neither transmitted power nor ***-Carlo simulation results reveal that the presented HARQ schemes can achieve throughput improvements up to 10%over block fading channels and up to 50%over fast fading channels in comparison with the original 5G CBG-level HARQ scheme but without excessively increasing the implementation complexity.

关键词： 5G NR HARQ LDPC codes superposition retransmission

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High performance of Dengue shock syndrome detection using extreme gradient boosting with ANOVA feature selection

Journal of Biotech Research

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Journal of Biotech Research 2024年 16卷 22-31页

作者： Muflikhah, Lailil Iskandar, Agustin Yudistira, Novanto Nadlori, Isbat Uzzin Dewanto, Bambang Nur Faculty of Computer Science Brawijaya University Malang India Faculty of Medicine Brawijaya University Malang India Department of Informatics Engineering and Computer Politeknik Elektronika Negeri Surabaya India Faculty of Technology Information Merdeka University Malang India

Dengue shock syndrome (DSS) is an infectious disease that affects millions of people every year all over the world. Early detection of DSS is essential for providing effective therapy and promoting patient recovery. In this work, we proposed a method to enhance DSS detection by combining the Extreme Gradient Boosting (XGBoost) algorithm with variance (ANOVA) feature selection analysis. We used a clinical dataset that contained important information gleaned from people with dengue virus infection. The dataset used for the research was collected from patients at Syaiful Anwar Hospital and consisted of 501 instances. Of these, 401 cases were related to DSS, while the other instances were unrelated to this specific medical condition. An analysis of variance (ANOVA) evaluated the most significant factors that distinguished persons with DSS from those with other dengue diseases. After that, the XGBoost model gave the characteristics of the selected features. For evaluation, we split the data into 80% and 20% for training and testing, respectively. The experimental result showed that the created model had a high degree of performance evaluation in detecting DSS. The highest performance achieved an accuracy of 0.839, precision of 0.875, recall of 0.92, and f1-score of 0.897. Most importantly, the model could detect DSS early on, enabling more proactive therapy and faster responses for individuals at risk of developing the condition. © (2024), (Bio Tech System). All Rights Reserved.

关键词： Patient treatment

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Energy-Efficient Encounter, Buffer and Contact Duration-Based Routing Protocol in Opportunistic Networks 2nd

Energy-Efficient Encounter, Buffer and Contact Duration-Bas...

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2nd NIELIT's International Conference on Communication, Electronics and Digital Technologies, NICEDT 2024

作者： Dutta, Amit Borah, Satya Jyoti Singh, Jagdeep Department of Computer Science and Engineering NERIST Nirjuli Itanagar India Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal India

ISBN: (纸本)9789819736003

Opportunistic network (OppNet) is a type of advanced wireless mobile network that has evolved from Mobile Ad-hoc Networks (MANETs) and belongs to the category of Delay Tolerant Network (DTN). The unique features that make OppNet different from other wireless networks are its intermittent connectivity, unstable topology, no dedicated path to the destination from the source, and high node mobility. This further makes the process of data transmission a challenging task. For this a method called "store-carry-forward" is used in which the battery-operated intermediate nodes store the message, discover a suitable relay node, and forward the message to that node. This further leads to the depletion of the battery power or energy of the node in due course of time. In this context, there is a necessity to develop routing models by taking the current energy status of a node into consideration. In this paper, the authors proposed an energy-efficient version of a popular routing mechanism EBC, termed E-EBC where the selection of the next suitable node depends upon the battery power (referred to as Energy) of that node. This model is then compared with existing benchmark protocols- Epidemic, Prophet, EDR, E-Prophet, E-EDR, and EBC. Simulation results (using ONE Simulator) depict that the proposed model performs much better than that of the benchmark models respectively by around 42.73, 41.46, 58.19, 32.03, 65.99, and 44.77% in terms of average latency. The proposed protocol E-EBC is also compared against messages dropped by varying the TTL, Buffer Size, and number of nodes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Delay tolerant networks

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Simulating Application Driven Mobile D2D underlay Networks using Gym API 17

Simulating Application Driven Mobile D2D underlay Networks u...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Batabyal, Suvadip National Institute of Technology Department of Computer Science & Engineering Durgapur India

ISBN: (纸本)9798331531195

Device-to-device (D2D) communication underlay cellular network improves spectral efficiency by reusing cellular resources over short and low-power links between the devices. For this, the D2D paradigm is considered to be an integral part of all kinds of future networks and is an active area of research at different levels. This requires a simple, configurable, scalable, and realistic evaluation of the protocols proposed for the D2D networking paradigm. Although there exists a few platform to evaluate a D2D underlay network, they have one or more of the following drawbacks: (i) long development time and complex environment, (ii) limited support for mobility, (iii) no support for application specific development and evaluation. In this work, we developed a framework for simulating an application-driven mobile D2D underlay network using the Gym API, where D2D connections are made spontaneously based on the application requirements. The proposed framework includes modules for generating application-specific traffic, buffer for transmission and caching of application data, and mobility management. This complements several earlier works on application (or utility) driven resource allocation in D2D underlay network. The framework is used to evaluate a D2D network using different mobility models and performance of cache based video streaming in a D2D underlay network. Finally, the framework is compared with ns-3 and OMNET++ simulators and is observed to have lower CPU utilization and run-time under the same network settings. © 2025 IEEE.

关键词： Resource allocation

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