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EdgeHARNet: An Edge-Friendly Shallow Convolutional Neural Network for Recognizing Human Activities Using Embedded Inertial Sensors of Smart-Wearables

EdgeHARNet: An Edge-Friendly Shallow Convolutional Neural Ne...

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International Conference on Communication, computing and Digital Systems (C-CODE)

作者： Hamza Ali Imran Ataul Aziz Ikram Saad Wazir Kiran Hamza Department of Computing National University of Sciences & Technology (NUST) Islamabad Pakistan Department of Computer Science Korea Advanced Institute of Science & Technology (KAIST) Daejeon South Korea

Human Activity Recognition (HAR) is a well-known area of study in the Internet of Medical and Health Things. The goal of HAR is to monitor complex, subtle, and postural human behaviours in the domains of Ambient Assisted Living (AAL), injury prevention, well-being management, medical diagnostics, and, in particular, geriatric care. The use of inertial sensors in smart devices for HAR is becoming more common, as it eliminates all of the constraints of traditional computer vision techniques. The usage of artificial neural networks improves classification, but their larger complexity makes them harder to deploy near the edge, where latency is reduced. This article presents a deep learning model that is lite in terms of trainable parameters and hence enables Edge-AI. The model is named EdgeHARNet. It assessed WISDM Activity Prediction and WISDM Actitracker datasets. The presented model has only 2031 trainable parameters. It has achieved 94.036% average accuracy for the WISDM Activity Prediction dataset and 74.06% average accuracy for the WISDM Actitracker dataset. Fl-score have also been reported for all individual classes. The performance comparison is carried out with existing models. We have done inference on Raspberry Pi 4 which is a single board computer as well.

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Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis

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computer Systems science & Engineering 2023年第10期47卷 1249-1266页

作者： Abdelwahed Motwakel Najm Alotaibi Eatedal Alabdulkreem Hussain Alshahrani MohamedAhmed Elfaki Mohamed K Nour Radwa Marzouk Mahmoud Othman Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Sentiment Analysis(SA)of natural language text is not only a challenging process but also gains significance in various Natural Language Processing(NLP)*** SA is utilized in various applications,namely,education,to improve the learning and teaching processes,marketing strategies,customer trend predictions,and the stock *** researchers have applied lexicon-related approaches,Machine Learning(ML)techniques and so on to conduct the SA for multiple languages,for instance,English and *** to the increased popularity of the Deep Learning models,the current study used diverse configuration settings of the Convolution Neural Network(CNN)model and conducted SA for Hindi movie *** current study introduces an Effective Improved Metaheuristics with Deep Learning(DL)-Enabled Sentiment Analysis for Movie Reviews(IMDLSA-MR)*** presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible ***,the Term Frequency-Inverse Document Frequency(TF-IDF)model is exploited to generate the word vectors from the pre-processed *** Deep Belief Network(DBN)model is utilized to analyse and classify the ***,the improved Jellyfish Search Optimization(IJSO)algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model,which shows the novelty of the *** experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR *** comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%.

关键词： Corpus linguistics sentiment analysis natural language processing deep learning word embedding

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Transforming Greenhouse Horticulture Using Artificial Intelligence and Digital Twin

Transforming Greenhouse Horticulture Using Artificial Intell...

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2023 IEEE International Conference on Quantum Technologies, Communications, computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Joshi, Kapil Gupta, Ashulekha Rawat, Amar Jeet Kumar, Rajiv Gupta, Omdeep Uttaranchal Institute of Technology Uttaranchal University Department of Cse Dehradun India Department of Management Studies Dehradun India Uttaranchal University Uttaranchal School of Computing Sciences Dehradun India Shivalik College of Engineering Computer Science and Engineering Department Dehradun India Graphic Era Hill University School of Management Dehradun India

ISBN: (纸本)9798350314946

In todays, to cope with the problems caused by the rising worldwide need for basic foodstuffs, it is vital to discover ecologically friendly solutions. Vegetable crops may be grown all year long, regardless of the climate or season, thanks to horticulture. The production process for horticulture may be categorized as an unstructured & extremely complicated system that is influenced by the climate, environment, pests, disease, herbicides, nutrients, pricing, & combinations of these numerous aspects. Digital twins might be seen as the beginning of a new era in data-driven & intelligent greenhouse horticulture. The study of the article is about the advancement of horticulture via the application of reducing technologies, especially digital technology. The purpose of this article is to offer, with the need for technology, the vital information that producers may utilize to harvest a range of crops to reap the most rewards. This study describes the outcomes of structured use of the Digital Twin application throughout greenhouse horticulture, as Artificial Intelligence applies to learn approaches throughout its applications to proclaim the best cost-effective & maximal benefit for the agriculturist. Based on the analysis, the article identifies challenges and suggests a few vital recommendations for future work. The findings of this study on the efficiency of concept in the growth of concentrated fruits and vegetables are examined. © 2023 IEEE.

关键词： Horticulture

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Quantum algorithms for sampling log-concave distributions and estimating normalizing constants 22

Quantum algorithms for sampling log-concave distributions an...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Andrew M. Childs Tongyang Li Jin-Peng Liu Chunhao Wang Ruizhe Zhang Joint Center for Quantum Information and Computer Science Department of Computer Science and Institute for Advanced Computer Studies University of Maryland Center on Frontiers of Computing Studies and School of Computer Science Peking University Simons Institute and Department of Mathematics UC Berkeley Department of Computer Science and Engineering Pennsylvania State University Department of Computer Science The University of Texas at Austin

ISBN: (纸本)9781713871088

Given a convex function f : ℝd → ℝ, the problem of sampling from a distribution ∝ e-f(x) is called log-concave sampling. This task has wide applications in machine learning, physics, statistics, etc. In this work, we develop quantum algorithms for sampling log-concave distributions and for estimating their normalizing constants $\int_{\mathbb{R}^d}e^{-f(x)}\mathrm{d} x$. First, we use underdamped Langevin diffusion to develop quantum algorithms that match the query complexity (in terms of the condition number κ and dimension d) of analogous classical algorithms that use gradient (first-order) queries, even though the quantum algorithms use only evaluation (zeroth-order) queries. For estimating normalizing constants, these algorithms also achieve quadratic speedup in the multiplicative error e. Second, we develop quantum Metropolis-adjusted Langevin algorithms with query complexity Õ(κ1/2d) and Õ(κ1/2d3/2/ε) for log-concave sampling and normalizing constant estimation, respectively, achieving polynomial speedups in κ, d, ε over the best known classical algorithms by exploiting quantum analogs of the Monte Carlo method and quantum walks. We also prove a 1/ε1-o(1) quantum lower bound for estimating normalizing constants, implying near-optimality of our quantum algorithms in ε.

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Enhancing Early Diagnosis of Autism Spectrum Disorder Using a Bayesian-Optimized Random Forest Algorithm

Enhancing Early Diagnosis of Autism Spectrum Disorder Using ...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Babu, Tina Nair, Rekha R Kishore, S. Mahapatra, Sheila Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Nitte Institute of Professional Education Mangalore India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by social communication difficulties, re- stricted interests, and repetitive behaviors. Early and accurate diagnosis is critical for timely intervention and improved outcomes. Traditional diagnostic methods, which rely on clinical observations and assessments, can be subjective and time-consuming. This study introduces a novel approach to predicting ASD using a machine learning technique, specifically a Bayesian-optimized Ran- dom Forest classification algorithm. The proposed method uses a comprehensive dataset containing various behavioral, cognitive, and demographic features associated with ASD. The Optimized Random Forest with Bayesian approach is used to categorize indi- viduals into two groups: those who have ASD and those who do not. To develop this ensemble learning technique, multiple decision trees are used, and to achieve higher performance as well as better capability to generalize, the hyperparameters are optimized with Bayesian optimization. Preliminary analysis and comparison with existing databases substantiated the findings and revealed the effectiveness of the proposed approach. The Bayesian-optimized Random Forest classifier achieved an accuracy value of 0.9890. Such performance is highly promising and could enable increased use of machine learning methods in timely and accurate diagno- sis of ASD. The proposed approach provides recommendations for a verified diagnostic technique when implemented and creates a strong foundation for future investigations into the use of machine learning for identifying and supporting children with ASD. © 2025 The Author(s).

关键词： Random forests

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Neural-kernel conditional mean embeddings 24

Neural-kernel conditional mean embeddings

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Proceedings of the 41st International Conference on Machine Learning

作者： Eiki Shimizu Kenji Fukumizu Dino Sejdinovic Department of Statistical Science Graduate University of Advanced Studies (SOKENDAI) Tokyo Japan and The Institute of Statistical Mathematics Tokyo Japan The Institute of Statistical Mathematics Tokyo Japan and Department of Statistical Science Graduate University of Advanced Studies (SOKENDAI) Tokyo Japan School of Computer and Mathematical Sciences The University of Adelaide Australia

Kernel conditional mean embeddings (CMEs) offer a powerful framework for representing conditional distribution, but they often face scalability and expressiveness challenges. In this work, we propose a new method that effectively combines the strengths of deep learning with CMEs in order to address these challenges. Specifically, our approach leverages the end-to-end neural network (NN) optimization framework using a kernel-based objective. This design circumvents the computationally expensive Gram matrix inversion required by current CME methods. To further enhance performance, we provide efficient strategies to optimize the remaining kernel hyperparameters. In conditional density estimation tasks, our NN-CME hybrid achieves competitive performance and often surpasses existing deep learning-based methods. Lastly, we showcase its remarkable versatility by seamlessly integrating it into reinforcement learning (RL) contexts. Building on Q-learning, our approach naturally leads to a new variant of distributional RL methods, which demonstrates consistent effectiveness across different environments.

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Candidate Evaluation with Multimodal Data-Driven for Recruitment 27th

Candidate Evaluation with Multimodal Data-Driven for Recru...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Liu, Kehong Wang, Jianjia Yao, Junfeng Deng, Bin Lv, Rongqi Song, Jun School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou215412 China CSSC Seago System Technology Co. Ltd. Shanghai200010 China Science and Technology on Near-Surface Detection Laboratory Jangsu Wuxi214035 China Department of Geography Hong Kong Baptist University 999077 Hong Kong

ISBN: (纸本)9783031781858

In the field of intelligent recruitment, automated resume matching and non-contact interviews have significantly improved the efficiency of companies in finding suitable candidates. This corresponds to the techniques of person-job matching and AI interviews. However, current person-job matching methods lack substantial data support, while AI interview methods struggle to integrate deep information from multimodal data and provide comprehensive evaluations of candidates’ responses. To address these challenges, we propose a multimodal data-driven person-job evaluation model, comprising two key stages: a person-job matching method based on graph attention and a multimodal AI interview method. Using a dual-perspective graph neural network approach, we accomplish the screening of candidates and positions. In the second stage, we conduct a comprehensive evaluation of candidates’ interview performance based on text, audio, and image modalities, providing a more objective, consistent, and efficient interview assessment method. Experimental results demonstrate that our person-job matching method surpasses current popular techniques and effectively transfers features to the next stage. In our multimodal AI interview method, we achieve accurate scoring of candidate responses, assessment of intonation stress levels, and inference of their Big Five personality traits, comprehensively evaluating candidates from multiple perspectives. This confirms the superiority and efficiency of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Cloud-based Decentralized Smart Healthcare for Patient Monitoring on Deep Learning 2

Cloud-based Decentralized Smart Healthcare for Patient Monit...

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2nd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2023

作者： Arunachalam, Sripriya Shanthi, H.J. Sivagurunathan, G. Das, Shyamali Anand, D. Raj M, Thanga School of Computing Sciences Vels Institute of Science Technology & Advanced Studies - Vistas Computer Applications- Pg India Amet University Ecr Kanathur India St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Chennai600119 India Cmr Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Persistent Systems Limited Pune India

ISBN: (纸本)9781665456302

Over the past few years, there has been a meteoric surge in the quantity of digital information available online for instantaneous sharing, persistent archiving, and inquiring. It has expanded the possibilities for using digital data that is both decentralised and ad hoc in order to make decisions quickly. At present, e-Healthcare is among the most sought-after sectors for EHR and telemedicine communication. Securing electronic health records (EHR) has become a topic of intense interest in recent years, with previous works employing a wide range of methods to better ensure the confidentiality and security of EHR at a reasonable price. There are a number of serious problems with the current research, including computational complexity, increased process time, information leakage, vulnerability to various assaults, scalability difficulty, etc. Clinical data analysis presents several difficulties, but disease prediction is one of the most significant ones. The suggested study aims to apply deep learning (DL) classification algorithms for disease prediction. A technique that utilises cloud computing, fog computing, and IoMT more recently has been presented for diagnosing illness. Fast DL classification analysis is performed in the fog layer. When compared to the alternative proposed model Bi-CNN, the healthcare model's efficiency in the Bi-LSTM simulation yields significantly better results: 97.31% of accuracy, 97.58% of recall, 96.90% of precision, 94.90% of F1-measure, 97.25% of specificity, and 94.80% of G-mean. © 2023 IEEE.

关键词： Fog computing

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Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation

arXiv

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arXiv 2024年

作者： Sani, Samin Mahdizadeh Sadeghi, Pouya Vu, Thuy-Trang Yaghoobzadeh, Yadollah Haffari, Gholamreza Department of Electrical and Computer Engineering University of Tehran Iran Tehran Institute for Advanced Studies Khatam University Iran Department of Data Science & AI Monash University Australia

Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited understanding of Persian) using parameter-efficient fine-tuning. We employ a multi-stage approach involving pretraining on monolingual Persian data, aligning representations through bilingual pretraining and instruction datasets, and instruction-tuning with task-specific datasets. We evaluate the model’s performance at each stage on generation and classification tasks. Our findings suggest that incorporating the Persian language, through bilingual data alignment, can enhance classification accuracy for Persian tasks, with no adverse impact and sometimes even improvements on English tasks. Additionally, the results highlight the model’s initial strength as a critical factor when working with limited training data, with cross-lingual alignment offering minimal benefits for the low-resource language. Knowledge transfer from English to Persian has a marginal effect, primarily benefiting simple classification tasks.1 Copyright © 2024, The Authors. All rights reserved.

关键词： Translation (languages)

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Enhancing Chest X-ray Pathology Prediction through Data-Driven Approaches and Transfer Learning

Enhancing Chest X-ray Pathology Prediction through Data-Driv...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Nair, Rekha R. Kishore, S. Babu, Tina Vishnu Vilashini, S. Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Nitte Institute of Professional Education Mangalore India Analyst Capgemini Karnataka Bellandur India

Chest X-ray pathology prediction play a very important role in early disease detection, enabling timely intervention and improving patient outcomes. This project undertakes a comprehensive exploration of medical imaging analysis, focusing on chest X-rays for pathological identification. Starting this inquiry is thorough getting of information and exploratory information assessment (EDA), which demonstrates the significant elements of the dataset's people, disease characteristics, and intrinsic predicaments. By making use of sophisticated image processing techniques, the study uses a Super-Resolution Convolutional Neural Network (SRCNN) in refining the resolution of chest X-ray scans, leading to improvement in diagnostic accuracy. Dealing with natural data imbalance found in medical data sets, this project proposed a new method by including a weighted loss function during training. Thus designed, this system allows the model to practically learn from uneven class distributions ensuring strong efficiency throughout different pathologies. The neural architecture for chest X-ray classification is built upon transfer learning principles, utilizing pretrained ResNet18, Alex net, Custom Neural Network models. The models are designed to meet the characteristics of the medical dataset in particular way, having replaced last fully connected layer with one that gives outputs across 14 different pathologies for predictions. The training process involves fine-tuning the models with a meticulously crafted weighted loss function and adaptive learning rate schedules, ensuring optimal convergence. © 2025 The Authors. Published by Elsevier B.V.

关键词： Analytics Data Logging MQ2 Sensor NodeMCU Real-time Alerts

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