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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Humaira Ashraf Fizza Khan Uswa Ihsan Fatima Al-Quayed N.Z. Jhanjhi Mamoona Humayun Department of Computer Science and Software Engineering International Islamic University Islamabad Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia School of Computer Science and Engineering (SCE) Taylor's University Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Systems College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia

With the advancement and availability of the internet in the present age, everything is being wireless. Be it our home appliances or high defined monitoring systems, Wireless sensor networks play an important role. But with advancement comes a high probability of getting attacked. One of these attacks is the Blackhole attack (BHA), in which a node drains all the traffic towards itself and discards the data traffic without sending it towards the receiver. There exist multiple approaches that help to protect a network against BHA, but existing approaches have some drawbacks. Many proposed schemes make sure that the given technique uses less energy but failed to reduce the packet drop ratio. In the detection of BHA, the proposed scheme should not only detect defected nodes but also manages the efficiency. The proposed methodology used mobile agents with authentication of nodes and trust values to detect black hole nodes. The methodology is tested using different evaluation measures such as energy consumption, latency, network life, and packet delivery ratio. The proposed method uses a detection algorithm that increases energy consumption and hence networks lifetime. Packet delivery rate is increased by 19.51%, the energy consumption is reduced by 53.3%, and network life is increased by 43.3% as compared to the previous technique.

关键词： Wireless communication Energy consumption Wireless sensor networks Mobile agents Energy measurement Clustering algorithms Transportation

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HASPER: An Image Repository for Hand Shadow Puppet Recognition

arXiv

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arXiv 2024年

作者： Raiyan, Syed Rifat Amio, Zibran Zarif Ahmed, Sabbir The Department of Computer Science and Engineering The Islamic University of Technology Boardbazar Gazipur Dhaka1704 Bangladesh Networking Research Group The Department of Computer Science and Engineering The Islamic University of Technology Boardbazar Gazipur Dhaka1704 Bangladesh AI-based software company ReplyMind AI Ltd. Mirpur Dhaka1216 Bangladesh The Department of Computer Science and Engineering The Islamic University of Technology Boardbazar Gazipur Dhaka1704 Bangladesh

Hand shadow puppetry, also known as shadowgraphy or ombromanie, is a form of theatrical art and storytelling where hand shadows are projected onto flat surfaces to create illusions of living creatures. The skilled performers create these silhouettes by hand positioning, finger movements, and dexterous gestures to resemble shadows of animals and objects. Due to the lack of practitioners and a seismic shift in people’s entertainment standards, this art form is on the verge of extinction. To facilitate its preservation and proliferate it to a wider audience, we introduce HASPER, a novel dataset consisting of 15,000 images of hand shadow puppets across 15 classes extracted from both professional and amateur hand shadow puppeteer clips. We provide a detailed statistical analysis of the dataset and employ a range of pretrained image classification models to establish baselines. Our findings show a substantial performance superiority of skip-connected convolutional models over attention-based transformer architectures. We also find that lightweight models, such as MOBILENETV2, suited for mobile applications and embedded devices, perform comparatively well. We surmise that such low-latency architectures can be useful in developing ombromanie teaching tools, and we create a prototype application to explore this surmission. Keeping the best-performing model RESNET34 under the limelight, we conduct comprehensive feature-spatial, explainability, and error analyses to gain insights into its decision-making process. To the best of our knowledge, this is the first documented dataset and research endeavor to preserve this dying art for future generations, with computer vision approaches. Our code and data will be publicly available. © 2024, CC BY-NC-SA.

关键词： Image classification

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Robust Feature engineering Techniques for Designing Motor Imagery-Based Bci Systems

SSRN

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SSRN 2025年

作者： Gardezi Ahmed, Syed Saim Khan, Mahnoor Bukhari, Syeda Muneeba Khan, Rizwan Ahmed Jawed, Soyiba Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science Faculty of Information Technology Salim Habib University Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naive Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provide an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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“Do you have Time for a Quick Call?”: Exploring Remote and Hybrid Requirements engineering Practices and Challenges in Industry

“Do you have Time for a Quick Call?”: Exploring Remote and...

引用

IEEE International Conference on Requirements engineering

作者： Ze Shi Li Delina Ly Lukas Nagel Nowshin Nawar Arony Daniela Damian Department of Computer Science University of Victoria Canada Application Lifecycle Management VX Company The Netherlands Department of Information and Computing Sciences Utrecht University The Netherlands Software Engineering Group Leibniz University Hannover Germany

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

With the onset of the COVID-19 pandemic and the ensuing shift away from co-located work arrangements towards working from home, practitioners encountered many collabora-tive and coordination challenges. While companies are slowly moving back towards in-person arrangements, many employers have permanently adopted fully remote and hybrid work modes. However, the work modes are more blurred, as hybrid work makes coordinating the requirements engineering practices that rely on rich interactions more challenging. Therefore, gaining more understanding of RE challenges and practices from prac-titioners transitioning to the new modes of work is imperative to identify insights that can be useful for organizations shifting to hybrid and remote work. In this paper, we use a mixed-methods approach to gain insights into remote and hybrid requirements engineering practices and challenges in the industry. Through interviews with 12 industry practitioners and a survey with 49 practitioners, we report on 7 adopted practices and 7 challenges encountered in these work arrangements. We found challenges such as organizing co-located tasks, lack of interpersonal con-nections, keeping everyone in the loop, and engagement barriers, which fall under coordination, communication and collaboration. To offset such challenges, we provide 20 recommendations based on our findings, such as proactive planning and using newer tools that support comprehensive tracking of important knowl-edge for requirements documentation. Our findings suggest that practitioners are facing challenges in remote and hybrid work arrangements, which they are mitigating with various strategies. Nonetheless, there remains a need for further research, as not all challenges are equally addressed across different work contexts.

关键词： Industries Surveys Prevention and mitigation Remote working Teamwork Planning Requirements engineering

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A Deep Learning Framework for Real-time Oil Spill Detection and Classification 2

A Deep Learning Framework for Real-time Oil Spill Detection ...

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2nd Workshop on Artificial Intelligence for Perception and Artificial Consciousness, AIxPAC 2024

作者： Patel, Pranshu Desai, Kedar Jain, Rinkal Bhatt, Chintan Vanlanduit, Steve Bruno, Alessandro Mazzeo, Pier Luigi Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India InViLab Research Group Universiteit Antwerpen Groenenborgerlaan Antwerpen171 2020 Belgium Department of Business Law Economics and Consumer Behaviour "Carlo A. Ricciardi" IULM AI Laboratory IULM University Milan20143 Italy ISASI Institute of Applied Sciences Intelligent Systems-CNR Lecce73100 Italy

Efficient detection of oil spills is critical for minimizing environmental damage. This study introduces a novel approach utilizing deep learning, specifically the YOLOv8 architecture, augmented with advanced computer vision techniques for oil spill detection. Through meticulous dataset curation and model training, the YOLOv8 model achieved an impressive overall accuracy (R-score) of 0.531 and a Mean Average Precision (mAP) of 0.549. Performance varied across different spill types, with the model demonstrating notable accuracy in distinguishing between oil spills and natural features, achieving precision and recall rates of up to 0.75 and 0.68, respectively, for sheen detection. Visualizations such as box loss, class loss, and confusion matrices provide insights into the model’s performance dynamics, revealing a steady decrease in losses and an improvement in accuracy over epochs. In this dataset, the measurements are drone measurements performed by Port of Antwerp Bruges. Furthermore, practical applications showcase the model’s versatility in detecting various oil spill types in both image and video data, affirming its potential for real-world deployment in environmental monitoring and disaster response scenarios. This research represents a significant stride towards more effective oil spill detection, contributing to environmental sustainability and resilience efforts. © 2024 Copyright for this paper by its authors.

关键词： Oil spills

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Random suppression of ECG artifacts using a NLSRLMMN-based cascaded noise canceller

Random suppression of ECG artifacts using a NLSRLMMN-based c...

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Asilomar Conference on Signals, Systems & computers

作者： Mohammed Mujahid Ulla Faiz Azzedine Zerguine Izzet Kale Department of Electrical and Computer Engineering Presidency University Bengaluru Karnataka India Electrical Engineering Department Interdisciplinary Research Center for Communication Systems and Sensing KFUPM Dhahran Saudi Arabia Department of Computer Science and Engineering Applied DSP and VLSI Research Group (ADVRG) University of Westminster London United Kingdom

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Normally, the electrocardiogram (ECG) signal artifacts are removed in a particular sequence starting with baseline wander removal at the beginning. In this work, a methodology has been proposed using a novel algorithm called the Normalized Leaky Sign Regressor Least Mean Mixed Norm (NLSRLMMN) algorithm to remove multiple ECG artifacts such as baseline wander, motion artifacts, muscle artifacts, and power line interference randomly without following a particular sequence. Through rigorous simulations we have shown that if we remove multiple ECG artifacts randomly using our proposed approach then an improvement in signal to noise ratio and mean square error is observed as opposed to a conventional method that employs the Least Mean Square (LMS) algorithm in all the cascaded adaptive noise canceller stages.

关键词： computers Adaptation models Adaptive systems Mean square error methods Interference Electrocardiography Muscles Noise cancellation Signal to noise ratio Motion artifacts

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Enhanced Word Embedding with CNN using Word Order Information for Sentiment Analysis 20

Enhanced Word Embedding with CNN using Word Order Informatio...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Atandoh, Peter Fengli, Zhang Hakeem, Paul Atandoh Acheampong, Edward Mensah Addo, Daniel Appiah-Twum, Michael Adu-Gyamfi, Daniel Blankson, Richard Safo Giza Wi, Tohye Tewodros School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Mercer University Department of Mathematics MaconGA31207 United States School of Resources and Environment University of Electronic Science and Technology of China Chengdu China C. K. Tedam University of Technology and Applied Sciences Department of Cyber Security and Computer Engineering Technology Navrongo UE/R Ghana Summit Economics LLC Colorado United States

ISBN: (纸本)9798350318982

On several e-commerce platforms, internet users share opinions. Understanding people's sentiments and opinions is necessary. GloVe, which uses word contexts and matrix vectorization, is an effective word vector learning method. Vector-learning systems have improved with this method. However, the GloVe model ignores word order in context. This paper presents Positional Embedding, which integrates word order into GloVe word embeddings. We found that our Word Order Vector (PEWOVe) word embeddings method outperforms GloVe in sentiment classification. Amazon data was used to test the proposed technique. The proposed model is 2.7%, 2.5%, and 2.1 % more accurate than baseline models on GE, GEC, and GE+PE. © 2023 IEEE.

关键词： Sentiment analysis

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ECG baseline wander removal using the LSRLMF-based fixed-point interference canceller

ECG baseline wander removal using the LSRLMF-based fixed-poi...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Mohammed Mujahid Ulla Faiz Saumya Kareem Reni Izzet Kale ECE Department Presidency University Bengaluru India Department of Computer Science and Engineering Applied DSP and VLSI Research Group University of Westminster London United Kingdom

The newly proposed Leaky Sign Regressor Least Mean Fourth (LSRLMF) algorithm is used in a fixed-point interference canceller for ElectroCardioGram (ECG) Baseline Wander (BW) removal application. An upper bound for the LSRLMF algorithm step-size is also presented. The LSRLMF-based interference canceller is quantized using different types of loss of precision methods. Through rigorous simulations, the quantization bit depth required for the different parameters of the LSRLMF-based interference canceller is found to be 9-bits for the most effective ECG baseline wander removal.

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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data

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Procedia computer science 2025年 256卷 1358-1366页

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao 56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand

Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems.

关键词： Activities of daily living low-resolution infrared sensors deep residual networks lightweight architecture healthcare monitoring

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A Deep Multi-Task Learning Network for Activity Recognition and User Identification Using Smartphone Sensors

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Procedia computer science 2025年 256卷 1350-1357页

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-than-ideal performance and restricted applicability. This research presents a novel deep multi-task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) layers to extract features from raw sensor data. The CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. These features are processed by fully connected layers for both activity recognition and user identification. A multi-task loss function is proposed to optimize both tasks based on their complexity and importance. The approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-the-art methods. This demonstrates the effectiveness of the multi-task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks.

关键词： human activity recognition user identification multi-task learning deep learning smartphone sensors

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