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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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Aspect-Based Sentiment Analysis for Social Multimedia:A Hybrid Computational Framework

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computer Systems science & Engineering 2023年第8期46卷 2415-2428页

作者： Muhammad Rizwan Rashid Rana Saif Ur Rehman Asif Nawaz Tariq Ali Azhar Imran Abdulkareem Alzahrani Abdullah Almuhaimeed University Institute of Information Technology–PMAS Arid Agriculture University Rawalpindi46000Pakistan Department of Creative Technologies Air UniversityIslamabad44000Pakistan Computer Engineering and Science Department Faculty of Computer Science and Information TechnologyAl Baha UniversityAl Baha65799Saudi Arabia Digital Health Institute King Abdulaziz City for Science and TechnologyRiyadh11442Saudi Arabia

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest *** share their thoughts and feelings about various topics,including products,news,blogs,*** user reviews and tweets,sentiment analysis is used to discover opinions and *** polarity is a term used to describe how sentiment is ***,neutral and negative are all examples of *** area is still in its infancy and needs several critical *** and hidden emotions can detract from the accuracy of traditional *** methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative *** existing strategies are *** proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion ***,classification was performed using *** proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and *** results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

关键词： Aspects deep learning lexicon sentiments reviews

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Enhancing Sparse Data Performance in E-Commerce Dynamic Pricing with Reinforcement Learning and Pre-Trained Learning 9

Enhancing Sparse Data Performance in E-Commerce Dynamic Pric...

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9th International Conference on Platform Technology and Service, PlatCon 2023

作者： Liu, Yuchen Man, Ka Lok Li, Gangmin Payne, Terry Yue, Yong Xi'an Jiaotong-Liverpool University Department of Computing Suzhou China University of Bedfordshire Faculty of Creative Arts Technologies and Science Luton United Kingdom University of Liverpool Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9798350305999

This paper introduces a reinforcement learning-based framework designed to tackle dynamic pricing challenges in e-commerce. Prior research has predominantly concentrated on algorithm selection to enhance performance in dense data scenarios. However, many of these models fail to robustly address sparse data structures, such as low-traffic products, leading to the 'cold-start' problem [4]. Through numerical analysis, our framework offers innovative insights derived from the design of the reward function and integrates product clustering with pre-trained learning to mitigate this issue. As a result of this optimization, the performance of predictive models on sparse data is expected to see substantial improvement. © 2023 IEEE.

关键词： Markov processes

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Machine Learning Techniques for Permission-based Malware Detection in Android Applications 9

Machine Learning Techniques for Permission-based Malware Det...

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9th International Conference on Information Technology Trends, ITT 2023

作者： Khan, Inam Ullah Ali Khan, Zahoor Ahmad, Mir Khan, Ali Haider Muahmmad, Fida Imran, Azhar Ahmed, Sheeraz Hamid, Muhammad Khalid University of Lakki Computer Science & It Marwat Pakistan Higher Colleges of Technology Cis United Arab Emirates Lahore Garrison University Faculty of Computer Science Lahore54000 Pakistan University of Science & Technology Department of Computer Science Bannu Pakistan Air University Department of Creative Technologies Islamabad Pakistan Iqra National University Department of Computer Science Peshawar Pakistan

ISBN: (纸本)9798350327502

Most smartphones and tablets have either been produced or are about to be released, and the Android operating system is swiftly gaining market share. These days, customers utilize Android applications often for a broad variety of tasks. As a result, attackers now frequently target the Android platform. Many harmful applications have been discovered in Information technology, and they frequently act maliciously in ways that don't correspond to their intended characteristics. Therefore, it's essential to identify harmful Android applications. This article describes multiple techniques for identifying fraudulent programmers using app permissions. © 2023 IEEE.

关键词： Static analysis

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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A Transfer Learning Based Detection and Grading of Cataract using Fundus Images 25

A Transfer Learning Based Detection and Grading of Cataract ...

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25th International Multi Topic Conference, INMIC 2023

作者： Abbas, Ahsan Alzahrani, Abdulkareem Imran, Azhar Almuhaimeed, Abdullah Khan, Ali Haider Air University Department of Creative Technologies Islamabad Pakistan Al-Baha University Faculty of Computer Science & It Saudi Arabia Digital Health Institute King Abdulaziz for Science and Technology Riyadh Saudi Arabia Lahore Garrison University Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9798350317701

Among the most prominent causes for visual impairment, especially among older adults, is cataract. As per the World Health Organisation (WHO), there are 2.2 billion individuals globally are estimated to be blind or possess an eyesight problem. One of the most identified and important causes of this is cataracts. Cataracts should be identified and treated as soon as possible to avoid blindness. Ophthalmologists use an expensive slit lamp to diagnose cataracts in regions with few medical facilities. Consequently, the issue is that a lack of skilled ophthalmologists may delay the identification of cataracts, for which medical treatment is unavoidable. Medical image analysis based on artificial intelligence provides a rapid and precise diagnosis in modern healthcare. We utilized deep learning models based on transfer learning, namely VGG19, and ResNet-50, to diagnose cataracts using fundus images and enhance classification accuracy. The performance metric for the model is accuracy;the highest achieved accuracy was 98%. © 2023 IEEE.

关键词： Diagnosis

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Twitter Sentimental Analysis using Machine Learning Approaches for SemeVal Dataset 1

Twitter Sentimental Analysis using Machine Learning Approach...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Imran, Azhar Fahim, Marwa Alzahrani, Abdulkareem Fahim, Safa Alheeti, Khattab M. Ali Rehman, Saif Ur Air University Dept. of Creative Technologies Islamabad Pakistan Al Baha University Faculty of Computer Science and IT Albaha Saudi Arabia University of Anbar Computer Networking Systems Dept. Ramadi Iraq University Institute of I.T. Arid Agriculture University Rawalpindi Pakistan

ISBN: (纸本)9781665472753

In recent years, Sentimental Analysis has enticed many researchers in this field. Due to the lack of suitable datasets, many scientists and researchers faced hindrances in their research. We used Semeval dataset because it's the authentic dataset for computational sentimental Analysis. When it comes to natural catastrophes, political turmoil, and terrorism, social scientists and psychologists are interested in learning how individuals express their feelings and opinions. In this paper, we present the approach to the SemEva1-2017 dataset. As we know, with the advancement of technology, social media have established strong worldwide connectivity and information sharing. The wide use of social media and media-networking sites produced an unprecedented amount of data. Sharing information using these websites has become very common. To detect the triggering factors has become necessary to understand the behavioural and emotional state to avoid anti-social behaviour and extreme or impulsive responses. We reveal to identify the emotional textual data using different strategies. Classification of the Tweets according to the Sentimental Analysis has an important role in the social, economic, and political world s. The effective strategy for tackling and coping with it is to use computational techniques to identify the speech type. For feature extraction, we use a variety of machine learning classifiers. It's crucial to detect related features in a text correctly. As a result, using and improving NLP approaches can aid in improved understanding and analysis of data. Briefly, we use an unsupervised. TF-IDF for the Feature Extraction to train the word Embedding Techniques that are tuned into transform training data and transformed Test data. The model is finally initialized using vectorization on Twitter sentiment analysis to train the latter. Then, transformed the model to create the transformed dataset. The major findings and outcomes of SemEva1-2017 on Identifying and Catego

关键词： Embeddings

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Diagnosis of Liver Disease Induced by Hepatitis Virus Using Machine Learning Methods 8

Diagnosis of Liver Disease Induced by Hepatitis Virus Using ...

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8th International Conference on Information Technology Trends, ITT 2022

作者： Hafeez, Muhammad Ahmad Imran, Azhar Khan, Muhammad Imran Khan, Ali Haider Nawaz, Asif Ahmed, Sheeraz Air University Department of Creative Technologies Islamabad Pakistan Air University Faculty of Computing and A.I Department of Creative Technologies Islamabad Pakistan School of Systems and Technology University of Management and Technology Department of Computer Science Lahore Pakistan Faculty of Electrical Engineering Higher Colleges of Technology Dubai United Arab Emirates Iqra National University Department of Computer Sciences Peshawar Pakistan

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Liver, the largest vital organ in the human body, helps in digestion, energy storage, and toxic removal processes. Hepatitis A, B, C, fatty liver, cirrhosis, and liver cancer all cause swelling of the belly and legs, changes in the color of urine, and jaundice. Early identification is necessary to save the lives of those infected with the Hepatitis virus. Although clinical tests such as Abdominal Ultrasound, Chloride Blood Test, Liver Biopsy, Liver Function Tests (LFT), Magnetic Resonance Cholangio Pancreatography (MRCP), Total Protein and Albumin/Globulin (A/G) Ratio, and others are used in the diagnosis of Hepatitis virus, but high-level expertise of the doctors are also required. With continual advancements in Artificial Intelligence (AI) and Machine Learning algorithms, medically intelligent systems for early detection of fatal liver disease can be developed for prediction of deadly liver disease in the early stage. The goal of this paper is to examine the accuracies of several Machine Learning algorithms such as Linear Regression, Decision Tree, Random Forest, KNN, and SVM. For this work, author used the data set accessible at the UCI database and proved that the Support Vector Machine perform better than all other methods with.91.84% accuracy. © 2022 IEEE.

关键词： Decision trees

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Multi-Modal Data Fusion Approach Using the Structured and Sparse Principal Component Analysis (MMSSCA)

Multi-Modal Data Fusion Approach Using the Structured and Sp...

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2023 IEEE International Conference on Paradigm Shift in Information technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Kandpal, Jyoti Jonitha, A. Sivakumar, Janaki Ray, Rejon Kumar School of Computing Graphic Era Hill University Uttarakhand Dehradun India Universiti Malaysia Faculty of Data Science & Computing Kelantan Malaysia Global College of Engineering and Technology Computer Science and Creative Technologies Muscat Oman Gannon University Department of Business Analytics PA Erie United States

ISBN: (纸本)9798350329773

There are many different sorts of data that can be gathered and analyzed, including pictures, videos, texts, speeches, music, and other noises, Video content, for example, generally includes minimum some types of audible sounds or any type of visual image, and often also useful text data that can be of some specific type of titles or subtitles, which may consist of some unique type text language which is different type of speech. 'Modality' is basically defines each different type of acquisition strategy. As natural phenomena tend to be quite complex, it is unusual for a single modality to provide exhaustive understanding of the phenomenon of interest. The process of combining and interpreting the useful data between the same modal or in different modal, and also interaction with the human beings with multi-modal data fusion &the accurate and deep interpretation and understanding of data obtained from the combining process from it, are central to the research challenge on multimodal data analysis. We contend that many of these challenges are shared by researchers in different fields. Both the 'why' and the 'how' of data fusion are addressed in this paper. Various examples from the realms of science and technology serve as inspiration for the first problem, which is then suggested by a various types of mathematical based model framework demonstrating the utility of data fusion. To solve the second problem, we introduce the idea of 'diversity' as a central concept and discuss several data-driven solutions based on matrix and tensor decompositions, highlighting how they take into account diversity in the underlying data sets to address the aforementioned issues, this research suggests a novel approach for modeling multi data fusion implemented using sparse technique and structural analysis. © 2023 IEEE.

关键词： Modal analysis

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