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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Rathi, Snehal Khedkar, Vijayshri Jadhav, Rutuja Kamate, Mugdha Potdar, Apurva Kamble, Prachi Computer Department Vishwakarma Institute of Information Technology Pune India Department of Computer Science & Engineering Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798331522667

Agriculture is backbone of many economies but still farmers often face many challenges in accessing financial services or available resources for them, making informed crop choices and participating in government initiatives. Because of many farmers are not aware about all these things, to address these concerns, the proposed "Agricultural Financial Inclusion System" provides a comprehensive platform which integrates essential services with aim of improving farmers’ production and their financial awareness. Our system has a crop recommendation feature that helps farmers to choose the best suitable crop for their soil in the farm by taking climate and soil type into account. In order to make sure that farmers are aware about various available services/resources or any government schemes and able to take advantage of them, the platform also offers essential information on a variety of government initiatives, saving plans etc. By combining all these features, the Agricultural Financial Inclusion System encourage farmers to make smart choices which help them to enhance agricultural productivity and financial stability. In order to create more successful and sustainable agriculture sector, this integrated approach hopes to fill the gap between farmers and technology & resources they need. © 2025 IEEE.

关键词： Smart agriculture

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Disparity Computation With Low Intensity Quantization on Stereo Image Pairs

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2024年 10卷 318-328页

作者： Lin, Huei-Yung Hsu, Tsai-Yu National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan National Chung Cheng University Department of Electrical Engineering Minhsiung621301 Taiwan

From variate bit-rate stereo matching, it is observed that the image pair with a low intensity quantization level is still capable of providing good disparity maps. In this article, a mathematical model representing the level of disparity discontinuity is proposed to formulate the mismatching prediction based on the intensity quantization. It is used to derive the minimum quantization level for quality-assured stereo matching. Due to the high computational cost of stereo processing, the reduction of image data usage will benefit both network training and real-time inference. The formulation presented in this work is investigated extensively for various types of scenes for disparity computation. In the experiments, the feasibility of our approach is validated with Middlebury, KITTI 2015, and synthetic Scene Flow datasets. © 2015 IEEE.

关键词： Image coding

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Deep learning-based human activity recognition using CNN, ConvLSTM, and LRCN

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 259-268页

作者： Uddin, Md. Ashraf Talukder, Md. Alamin Uzzaman, Muhammad Sajib Debnath, Chandan Chanda, Moumita Paul, Souvik Islam, Md. Manowarul Khraisat, Ansam Alazab, Ammar Aryal, Sunil School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Centre for Artificial Intelligence and Optimization Torrens University Australia Department of Electrical and Computer Engineering University of Ottawa Canada

Human activity recognition (HAR) plays a crucial role in assisting the elderly and individuals with vascular dementia by providing support and monitoring for their daily activities. This paper presents a deep learning (DL)-based approach to HAR, leveraging convolutional neural network (CNN), convolutional long short-term memory (ConvLSTM), and long-term recurrent convolutional network (LRCN) architectures. These models are designed to extract spatial features and capture temporal dependencies in video data, enhancing the accuracy of activity classification. We conducted experiments on the UCF50 and HMDB51 video datasets, encompassing diverse human activities. Our evaluation demonstrates that the ConvLSTM model achieves an accuracy of 82% on UCF50 and 68% on HMDB51, while the LRCN model gives accuracies of 93.44% and 71.55%, respectively. Finally, the CNN model outperforms with an accuracy rate of 99.58% for the UCF50 and 92.70% for the HMDB51 datasets. These significant improvements showcase the effectiveness of integrating convolutional and recurrent neural networks for HAR tasks. Our research contributes to advancing HAR systems with potential applications in healthcare, assisted living, and surveillance. By accurately recognizing human activities, our models can assist in remote patient monitoring, fall detection, and public safety initiatives. These findings underscore the importance of DL in enhancing the quality of life and safety for individuals in various contexts. © 2024 The Authors

关键词： Long short-term memory

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Towards Blockchain-Based Secure BGP Routing,Challenges and Future Research Directions

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computers, Materials & Continua 2024年第5期79卷 2035-2062页

作者： Qiong Yang Li Ma Shanshan Tu Sami Ullah MuhammadWaqas Hisham Alasmary Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Border Gateway Protocol(BGP)is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different *** BGP protocol exhibits security design defects,such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes,easily triggering prefix hijacking,path forgery,route leakage,and other BGP security ***,the traditional BGP security mechanism,relying on a public key infrastructure,faces issues like a single point of failure and a single point of *** decentralization,anti-tampering,and traceability advantages of blockchain offer new solution ideas for constructing secure and trusted inter-domain routing *** this paper,we summarize the characteristics of BGP protocol in detail,sort out the BGP security threats and their ***,we analyze the shortcomings of the traditional BGP security mechanism and comprehensively evaluate existing blockchain-based solutions to address the above problems and validate the reliability and effectiveness of blockchain-based BGP security methods in mitigating BGP security ***,we discuss the challenges posed by BGP security problems and outline prospects for future research.

关键词： BGP security blockchain prefix hijacking trust model secure routing

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Efficient Real-Time Human Activity Recognition with IoT Sensor Data Using Conditionally Parametrized Convolutions 4

Efficient Real-Time Human Activity Recognition with IoT Sens...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Lovanshi, Mayank Tiwari, Vivek Singh, Anurag Ingle, Rajesh International Institute of Information Technology Department of Computer Science Engineering Naya Raipur India ABV-Indian Institute of Information Technology Department of Computer Science Engineering Gwalior India International Institute of Information Technology Department of Electronics Communication Engineering Naya Raipur India Symbiosis Skills of Professional University Department of Computer Science Engineering Pune India

ISBN: (纸本)9798331518981

Human Activity Recognition (HAR) is a trading area in computer vision and deep learning. However, boosting the performance of deep learning models often necessitates increasing their size or capacity, which raises computational demands. The high operational costs present challenges for real-time HAR on sensor-based devices. Shallow learning approaches, while computationally efficient, generally fail to deliver comparable performance. Consequently, there is a critical need for deep learning methods that can effectively balance accuracy and computational cost, a topic that remains largely underexplored. This paper introduces a computationally efficient CNN that utilizes conditionally parametrized convolution, designed specifically for real HAR on sensor-based devices. The model is tested on the OPPORTUNITY dataset, achieving existing accuracy without increasing computational load. Through extensive ablation studies, we show that this high-capacity network surpasses baseline models while maintaining a similar level of computational efficiency. The proposed method can directly replace existing deep HAR frameworks and be easily implemented on sensor-based platforms for real-time applications. © 2024 IEEE.

关键词： Convolution

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Motion Retargeting from Human in Video to 3D Characters with Different Skeleton Topology 4

Motion Retargeting from Human in Video to 3D Characters with...

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4th International Conference on Consumer Electronics and computer engineering, ICCECE 2024

作者： Guo, Hao Li, Shujie Jia, Wei Hefei University of Technology Department of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350384062

Motion retargeting from videos to 3D virtual character is a challenging task in computer vision and computer graphics. A solution is first to extract the 3D motion sequences from videos using human pose estimation algorithm and then retarget to 3D virtual character by skeleton motion retargeting algorithm. However, manual skeleton mapping is often necessary because of the difference in skeleton topology between pose estimation and 3D virtual character, introducing uncertainty and potentially compromising the semantics of the original motion. Additionally, the results of pose estimation contain noise while existing skeleton motion retargeting algorithms do not consider the influence of noise inputs in their design. In response to the above issues, we propose a novel motion retargeting framework and introduce a new motion retargeting network named CTBRnet (Combined Transformer Bi-LSTM Retargeting network). The framework leverages existing algorithm to generate paired training data, providing supervision to CTBRnet and enabling CTBRnet to learn potential skeleton mapping. The framework utilizes human motion videos as input and generates retargeting results for virtual characters, which are driven by joint rotations aligned with the virtual character's skeletal topology. Our proposed network combines transformer and Bi-LSTM, effectively capturing stable high-dimensional motion features from noisy results of pose estimation in both global and local perspectives. The experimental results show that our framework can achieve motion retargeting from videos to 3D virtual character with different skeleton topology, and compared with existing skeleton motion retargeting networks, CTBRnet reduces joint position errors while enhancing the smoothness. © 2024 IEEE.

关键词： Topology

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Intelligent Cost-Effective Electric Vehicle Charging Solutions for Cities and Highway Networks 17

Intelligent Cost-Effective Electric Vehicle Charging Solutio...

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

作者： Lohia, Akshat Pochhi, Aadesh Mishra, Shivansh Chakraborty, Nilotpal Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798331531195

In this work, we address the strategic placement and optimal sizing of electric vehicle charging stations for cities as well as highway traffic to minimize overall cost. We formulate the problem as a Mixed Integer Linear Programming (MILP) model and propose a set of greedy algorithms to obtain scalable solutions quickly. For city areas, we propose a systematic approach considering factors like population density and traffic patterns to maximize customer satisfaction and minimize costs. For highways and expressways, we account for deterministic inter-arrival times and maximum waiting times. Additionally, we propose an integrated approach that, when run for two cities and a connecting highway, demonstrates a total cost reduction compared to using separate algorithms for cities and highways. © 2025 IEEE.

关键词： Integer linear programming

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Extracting Facial Features to Detect Deepfake Videos Using Machine Learning

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International Journal of Advanced computer science and Applications 2025年第4期16卷 834-842页

作者： Aslam, Ayesha Mir, Jamaluddin Zaman, Gohar Rahman, Atta Salam, Asiya Abdus Ali, Farhan Alhiyafi, Jamal Bakry, Aghiad Gul, Mustafa Jamal Gollapalli, Mohammed Mahmud, Maqsood Department of Computer Science Abbottabad University of Science and Technology Havelian Pakistan Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia College of Electronics and Information Engineering Shenzhen University Shenzhen China Department of Computer Science Kettering University FlintMI United States Department of Business Administration University of York York YO10 5DD Heslington United Kingdom Department of Information Technology & Engineering Sydney Met SydneyNSW2000 Australia School of Computing Ulster University Northern Ireland Belfast United Kingdom

Generative adversarial networks (GANs) have gained popularity for their ability to synthesize images from random inputs in deep learning models. One of the notable applications of this technology is the creation of realistic videos known as deepfakes, which have been misused on social media platforms. The difficulty lies in distinguishing these fake videos from real ones with the naked eye, leading to significant concerns. This study proposes a supervised machine learning approach to effectively differentiate between real and counterfeit videos by detecting visual artifacts. To achieve this, two facial features are extracted: eye blinking and nose position, utilizing landmark detection techniques. Both features were trained on supervised machine learning classifiers and evaluated using the publicly available UADFV and Celeb-DF deepfake datasets. The experiments successfully demonstrate that the proposed method achieves a promising and superior performance, with an area under the curve (AUC) of 97% for deepfake detection in contrast to state-of-the-art methods investigating the same datasets. © (2025), (science and information Organization). All rights reserved.

关键词： Support vector machines

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A Novel 3D InceptionNet Model: Architecture and Applications in Hyperspectral Brain Image Classification 8

A Novel 3D InceptionNet Model: Architecture and Applications...

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8th IEEE International Conference on information and Communication technology, CICT 2024

作者： Singh, Raj Bahadur Datta, Aloke The Lnm Institute of Information Technology Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798331531539

Glioblastoma is an aggressive type of brain cancer with a high mortality rate. Early and accurate glioblastoma detection is crucial for timely and effective treatment. Hyperspectral Imaging (HSI) has emerged as a promising tool for brain tumor detection, as it can capture detailed spectral information that can be leveraged for classification. This paper proposes a novel modified deep learning architecture named 3D InceptionNet to classify hyperspectral brain images. 3D InceptionNet combines the advantages of 3D convolutional layers and the InceptionNet module to extract and learn discriminative features from the 3D hyperspectral data. To demonstrate the efficacy of our proposed approach, we evaluated its performance on an in-vivo hyperspectral brain image dataset using various performance measures. © 2024 IEEE.

关键词： Hyperspectral imaging

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EFS-XGBoost: A robust framework for precision classification of COVID-19 cases

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Intelligent Data Analysis 2025年第1期29卷 171-185页

作者： Abidi, Mustufa Haider Khare, Neelu D, Preethi Alkhalefah, Hisham Umer, Usama Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI

关键词： COVID-19

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