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StrideSense: Enriching Lower Extremity and Kinetics in ACLR Patients via Sonic Insights

IEEE Internet of Things Journal 2025年第12期12卷 19549-19560页

作者： Ahmed, Awais Yang, Panlong Butt, Abdul Haleem Rizwan, Muhammad Angin, Pelin Khan, Taha Nanjing University of Information Science and Technology University of Science and Technology of China China University of Bologna Department of Biomedical and Neuromotor Sciences Italy Air University Islamabad Department of Creative Technologies Faculty of Computing and Artificial Intelligence Pakistan University of Science and Technology of China School of Computer Science and Technology China Middle East Technical University Department of Computer Engineering Turkey Detectivio AB Sweden

Tearing the anterior cruciate ligament requires repair and rehabilitation to restore lower limb functionality fully. This study outlines a method for monitoring rehabilitation after knee surgery by analyzing footstep sounds and applying deep learning techniques. The process involves examining gait sounds during the initial four weeks of rehabilitation. The suggested system, StrideSense, recognizes walking sounds, enabling seamless and ongoing monitoring of patients' gait rehabilitation. The research proposes a novel method for event detection by analyzing walking patterns using dynamic time-warping and sequential footstep duration. It leverages physiological data like gait sound and energy descriptors to develop a deep-learning model for gait improvement assessment, evaluated by a physiotherapist through lower extremity functional scores. The suggested model, AtdNet, which utilizes Densenet169 and attention mechanisms, achieves 96% accuracy in classifying walking on various post-surgical days. It also predicts lower extremity functional scores with a mean absolute error of 4.63%. A deeper analysis of bone-conducted footstep sounds enriched the acoustic sensing method. We assessed the suggested acoustic model alongside existing methods, showing that rehabilitation monitoring driven by acoustics outperforms traditional clinic-based approach assessments. Future efforts will focus on validating the model with a larger dataset and integrating it into smart homes. © 2014 IEEE.

关键词： Physiological models

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Performance Challenges and Solutions in Big data Platform Hadoop

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Recent Advances in computer science and Communications 2023年第9期16卷 27-41页

作者： Singh, Balraj Verma, Harsh K. Madaan, Vishu Department of Computer Science and Engineering Dr. B.R. Ambedkar NIT Jalandhar India School of Computer Science and Engineering Lovely Professional University Phagwara India

Background: The present era demands continuous support to bring improvements in executing complex analytics on large-scale data and to work beyond traditional systems. Objective: The need for processing diverse data types and solutions for different domains of the industry is rising. Such needs increase the requirement for sophisticated techniques and methods to enhance the existing platforms and mechanisms further. It provides an opportunity for the research community to investigate further into the existing systems, find potential issues, and propose new ways to improve the current systems. Hadoop is a popular choice to manage and process Big data. It is an open-source platform and a front-runner in the batch processing of large-scale jobs. The economy associated with the cluster in scaling is low as compared to other platforms. However, this popularity by no means guarantees high performance in all scenarios. With the continuous evolution in data development and industrial requirements, it is imperative to investigate and look into new methods and techniques to bring advancements to the existing system. Method: A systematic review is represented in this paper to have an insight into the current progress in this field. Research publications from various sources are taken and analyzed. The performance of a cluster largely depends upon the different job processing mechanisms and policies associated with it. Conclusion: While extensive studies and solutions are proposed, the performance bottlenecks in terms of load balancing, resource utilization, content management, and efficient processing prevail. Not many of the solutions are there on scheduling about the trade-off between different parameters, the process of content splitting and merging is not explored to a large extent and the skew mitigation solutions are more focused on Reduce side of the MapReduce while the Map side is not utilized much for load balancing. © 2023 Bentham science Publishers.

关键词： Batch data processing

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Sentiment Analysis System for Image and Text Based Social Media data 1

Sentiment Analysis System for Image and Text Based Social Me...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Baoyue, Hu Ashraf, Humaira Jhanjhi, Nz Verma, Sahil Taylor's University School of Computer Science and Engineering Malaysia Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350373042

The paper provides an explicit outlook on the development of a comprehensive sentiment analysis systems for online social media by targeting user-generated text and images on platforms such as Twitter, Facebook, and Instagram. The system is developed to provide a complete sentiment analysis of users' emotions and expressions on social media. The project is composed of three interconnected sections, a Text-based sentiment analysis model, an Image-based sentiment analysis model and a web application prototype that integrates these models to provide real time results. The text-based model includes, data preprocessing, analysis using Word Cloud, analysis using the techniques of Word2Vec and implementation of multiple machine learning and deep learning models. Similarly, image-based model includes data preprocessing, data augmentation, deep learning methods for sentiment assessment. Final module is focused on representing application with real time demonstration, consisting with the practical implementation of these models. The prototype displays a real-time sentiment analysis and visualizes data. The paper is focused on a wide range of the field where the importance of the sentiment analysis is immense. For instance, companies can analyze how products are being analyzed in the market, politicians can determine how people are thinking of their speeches, teachers can know the interest of the lecture for the students. Therefore, the research signifies the importance of the Sentiment Analysis to extract the meaning or insight from the overwhelming unstructured, dynamic and always evolving nature of the social media content. © 2024 IEEE.

关键词： Image analysis

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A survey for light field super-resolution

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High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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The Politics of Fear and the Experience of Bangladeshi Religious Minority Communities Using Social Media Platforms

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW2期8卷 1-32页

作者： Rifat, Mohammad Rashidujjaman Das, Dipto Podder, Arpon Jannat, Mahiratul Soden, Robert Semaan, Bryan Ahmed, Syed Ishtiaque Department of Computer Science University of Toronto TorontoON Canada Khulna University of Engineering & Technology Khulna Bangladesh Department of Women and Gender Studies University of Dhaka Dhaka Bangladesh Computer Science and School of the Environment University of Toronto TorontoON Canada Department of Information Science University of Colorado Boulder BoulderCO United States

Despite significant research on online harm, polarization, public deliberation, and justice, CSCW still lacks a comprehensive understanding of the experiences of religious minorities, particularly in relation to fear, as prominently evident in our study. Gaining faith-sensitive insights into the expression, participation, and inter-religious interactions on social media can contribute to CSCW’s literature on online safety and interfaith communication. In pursuit of this goal, we conducted a six-month-long, interview-based study with the Hindu, Buddhist, and Indigenous communities in Bangladesh. Our study draws on an extensive body of research encompassing the spiral of silence, the cultural politics of fear, and communication accommodation to examine how social media use by religious minorities is influenced by fear, which is associated with social conformity, misinformation, stigma, stereotypes, and South Asian postcolonial memory. Moreover, we engage with scholarly perspectives from religious studies, justice, and South Asian violence and offer important critical insights and design lessons for the CSCW literature on public deliberation, justice, and interfaith communication. © 2024 Copyright held by the owner/author(s).

关键词： Social psychology

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A survey on graph neural network-based next POI recommendation for smart cities

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Journal of Reliable Intelligent Environments 2024年第3期10卷 299-318页

作者： Yu, Jian Guo, Lucas Zhang, Jiayu Wang, Guiling Department of Computer Science and Soft Engineering Auckland University of Technology Auckland New Zealand School of Information Science and Technology Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China

Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there’s an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially pivotal in smart cities, these systems aim to enhance user experiences by offering location recommendations tailored to past check-ins and visited POIs. Distinguishing itself from traditional POI recommendations, the next POI approach emphasizes predicting the immediate subsequent location, factoring in both geographical attributes and temporal patterns. This approach, while promising, faces with challenges like capturing evolving user preferences and navigating data biases. The introduction of Graph Neural Networks (GNNs) brings forth a transformative solution, particularly in their ability to capture high-order dependencies between POIs, understanding deeper relationships and patterns beyond immediate connections. This survey presents a comprehensive exploration of GNN-based next POI recommendation approaches, delving into their unique characteristics, inherent challenges, and potential avenues for future research. © The Author(s) 2024.

关键词： Location based services

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Towards property-preserving JPEG encryption with structured permutation and adaptive group differentiation

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Visual computer 2024年第9期40卷 6421-6447页

作者： Su, Guo-Dong Chang, Ching-Chun Lin, Chia-Chen Chang, Chin-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Fujian Province University Fuzhou China Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

The security and privacy of digital images are a major concern in cyberspace. JPEG is the most widely used image compression standard and yet there are problems with format compatibility and file size preservation in most of the modern encryption schemes for JPEG images. To address these problems, we propose a novel properties-preserving encryption scheme that obfuscates JPEG images by scrambling AC and DC coefficients. The key observation is that an innovative structured permutation has a remarkable effect on enciphering the coefficients and simultaneously prevents value overflow as well as size increment. On this basis, we first leverage the row-wise reordering and reversing technique to disorganize the DC coefficients. Afterward, a series of groups of consecutive DC differential values are produced using adaptive group differentiation and each of them is independently permuted inside. Finally, we perform block-wise ACCs permutation and intra-block ACCs shuffling to further confuse the outline of the original image. Moreover, invariant features regarding the coefficients are incorporated in the key generation process to resist various cryptanalytic attacks. Experimental results demonstrate a significant improvement in terms of format compatibility and file size preservation, and verify the security against various attacks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Cryptography

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A Streamlined Method for Detecting Strawberry Leaf Diseases Utilizing Deep Learning Techniques 3

A Streamlined Method for Detecting Strawberry Leaf Diseases ...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Singh, Jashandeep Singh, Gursharan School of Computer Science and Engineering Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331527549

Strawberry leaf scorch presents major risks to fruit yields through its harmful effects on plant health so early detection methods are vital for growers. This research analyzes how different Convolutional Neural Networks classify strawberry leaf diseases while fixing problems found in earlier studies about speed and diagnosis precision. The proposed research evaluated five CNN models namely AlexNet, DenseNet, ResNet, MobileNet, and EfficientNet through implementation on a total of 6146 images that showcased both healthy and scorch-affected strawberry leaves. The proposed research optimized feature extraction techniques and added model comparison to find the most effective configuration. EfficientNet achieved higher accuracy and precision than all test models when detecting diseases in samples. The strong disease identification abilities of DenseNet and MobileNet show promise for developing better ways to manage agricultural health problems. This study expands precision agriculture methods while helping farmers sustain their operations through modern machine learning tools. © 2025 IEEE.

关键词： Fruits

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Multimodal Labor Exploitation Detections for Taiwan Distant Water Fishing Industry

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SN computer science 2024年第1期5卷 46页

作者： Karthikeyan, P. Hsiung, Pao-Ann School of Computer Science and Engineering RV University Bengaluru India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi 62102 Taiwan

Taiwan plays a significant role in global seafood supply chains, accounting for approximately 10% of global tuna catches. The country is a substantial producer of tuna, shrimp, and squid, and its seafood industry is worth an estimated $1.3 billion annually. The Taiwan Distant Water Fishing (DWF) industry faces several challenges, including concerns about labor practices and illegal fishing. However, the government is working to address these issues and improve the industry’s sustainability. This research addresses labor exploitation, specifically within Taiwan’s DWF industry. The proposed research introduces a Mobile face verification system (MFVS) to overcome human error in work-hour recording. A multimodal labor exploitation detection system is also developed, utilizing Retrained-You Only Look Once-v7 and the Global Fishing Watch (GFW) model. The multimodal performance is evaluated using DWF data, demonstrating superior performance to the GFW model. The overall accuracy of the multimodal labor exploitation detection system for the Taiwan DWF Industry is 0.87, while the GFW model achieves an accuracy of 0.83 explicitly. The research facilitates combat labor exploitation and improves industry sustainability. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Distant water fishing Global fishing watch Labor exploitations Multimodal Retrained YOLOv7

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Optimizing abnormality detection in fundus images with Triplet-OS and orchard search optimization model

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Neural Computing and Applications 2024年第36期36卷 23181-23194页

作者： Venkatraman, K. Hemalatha, R. Radhika, S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha Nagar Thandalam Chennai India Department of Computer Science and Engineering St. Joseph’s College of Engineering Chennai India Department of EEE Sathyabama Institute of Science and Technology Chennai India

The accurate and early detection of abnormalities in fundus images is crucial for the timely diagnosis and treatment of various eye diseases, such as glaucoma and diabetic retinopathy. The detection of abnormalities in fundus images using traditional methods is often challenging due to high computational demands, scalability issues, and the requirement of large labeled datasets for effective training. To address these limitations, a new method called triplet-based orchard search (Triplet-OS) has been proposed in this paper. In this study, a GoogleNet (Inception) is utilized for feature extraction of fundus images. Also, the residual network is employed to detect abnormalities in fundus images. The Triplet-OS utilizes the medical imaging technique fundus photography dataset to capture detailed images of the interior surface of the eye, known as the fundus and the fundus includes the retina, optic disk, macula, and blood vessels. To enhance the performance of the Triplet-OS method, the orchard optimization algorithm has been implemented with an initial search strategy for hyperparameter optimization. The performance of the Triplet-OS method has been evaluated based on different metrics such as F1-score, specificity, AUC-ROC, recall, precision, and accuracy. Additionally, the performance of the proposed method has been compared with existing methods. Few-shot learning refers to a process where models can learn from just a small number of examples. This method has been applied to reduce the dependency on deep learning [1]. The goal is for machines to become as intelligent as humans. Today, numerous computing devices, extensive datasets, and advanced methods such as CNN and LSTM have been developed. AI has achieved human-like performance and, in many fields, surpasses human abilities. AI has become part of our daily lives, but it generally relies on large-scale data. In contrast, humans can often apply past knowledge to quickly learn new tasks [2]. For example, if given

关键词： Medical imaging

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