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Enhancing Split Computing and Early Exit Applications through Predefined Sparsity

Enhancing Split Computing and Early Exit Applications throug...

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Forum on Specification, Verification and Design Languages, FDL

作者： Luigi Capogrosso Enrico Fraccaroli Giulio Petrozziello Francesco Setti Samarjit Chakraborty Franco Fummi Marco Cristani Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Computer Science University of North Carolina Chapel Hill USA Dept. of Computer Science University of Verona Italy

ISBN: (数字)9798331504571

ISBN: (纸本)9798331504588

In the past decade, Deep Neural Networks (DNNs) achieved state-of-the-art performance in a broad range of problems, spanning from object classification and action recognition to smart building and healthcare. The flexibility that makes DNNs such a pervasive technology comes at a price: the computational requirements preclude their deployment on most of the resource-constrained edge devices available today to solve real-time and real-world tasks. This paper introduces a novel approach to address this challenge by combining the concept of predefined sparsity with Split Computing (SC) and Early Exit (EE). In particular, SC aims at splitting a DNN with a part of it deployed on an edge device and the rest on a remote server. Instead, EE allows the system to stop using the remote server and rely solely on the edge device’s computation if the answer is already good enough. Specifically, how to apply such a predefined sparsity to a SC and EE paradigm has never been studied. This paper studies this problem and shows how predefined sparsity significantly reduces the computational, storage, and energy burdens during the training and inference phases, regardless of the hardware platform. This makes it a valuable approach for enhancing the performance of SC and EE applications. Experimental results showcase reductions exceeding 4× in storage and computational complexity without compromising performance. The source code is available at https://***/intelligolabs/sparsity_sc_ee.

关键词： Performance evaluation Training Smart buildings Source coding Artificial neural networks Medical services Real-time systems

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Intelligent Resource Allocation for RIS-Assisted Cell-Free Massive MIMO Systems

Intelligent Resource Allocation for RIS-Assisted Cell-Free M...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Gloria Mollah Majumder Haider Danda B. Rawat Imtiaz Ahmed Dept. of Electrical Engineering & Computer Science Howard University Washington DC USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Cell-free massive multiple-input multiple-output (mMIMO) systems distribute a large number of access points (APs) over a wide geographic region, improving coverage, capacity, and user experiences notably. Similarly, reconfigurable intelligent surface (RIS) allows a significant leap forward in signal quality and energy efficiency by adding additional flexibility and intelligent control over the propagation environment of wireless systems. However, optimal resource allocation in RIS-integrated cell-free mMIMO systems faces substantial challenges due to the presence of a large number of APs and passive reflecting elements (PREs) at RIS while simultaneously providing network coverage to aerial and ground users. To efficiently navigate the high-dimensional optimization space, this paper proposes a novel framework for optimal resource allocation in RIS-assisted cell-free mMIMO systems employing deep learning (DL) techniques. In particular, we train a deep neural network offline to optimize the transmit powers at APs and the phase shift of PREs at RIS. The trained model is tested online to perform real-time optimization with low computational complexity. Simulation results demonstrate the effectiveness of the proposed data-driven resource allocation scheme while comparing it with the state-of-the-art baseline scheme.

关键词： Navigation Simulation Wireless networks Artificial neural networks Massive MIMO Reconfigurable intelligent surfaces Real-time systems Resource management Optimization Intelligent control

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Autism Spectrum Disorder Detection Based on Optimal Feature Selection Technique

Autism Spectrum Disorder Detection Based on Optimal Feature ...

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Smart Technologies, Communication and Robotics (STCR)

作者： Lakshaya Kaushik Ranjit Singh Manjit Singh Dept. of Computer Science & Engineering Chandigarh University Gharuan Mohali Punjab India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

A neurodevelopmental disorder autism spectrum disorder (ASD) still remains a challenge among research fields, and the current diagnostic procedures still need to be improvised for more precise detections. Although several investigations by involving the usage of different machine learning (ML) algorithms toward the identification of ASD among individuals, there is still a large gap in optimizing feature selection methods toward better accuracy and efficiency. Systematic comparisons among various feature selection methods are rather neglected in prior studies, so there is less potential for discovering the best diagnostic markers. In this light, our work brings forth a comprehensive framework that investigates three different kinds of feature selection methods-mutual information, wrapper-based selection by Random Forest, and LASSO regularization by XGBoost. Our methodology implements structured evaluations toward the assessment of the impact of each tool for the identification of detecting accuracy and computation. Also, comparative evaluation results that though wrapper-based selection increased the first model’s accuracy up to 90%, the use of XGBoost with LASSO regularization outperformed it with an accuracy of 99.8%. Results establish the importance of our approach in streamlining clinical assessment processes and identifying features about ASD that are crucial towards advancing data-driven detection methodologies.

关键词： Autism Accuracy Systematics Machine learning algorithms Computational modeling Pipelines Feature extraction Robots Random forests Mutual information

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Technology and Community Interventions for food Security and Waste Reduction

Technology and Community Interventions for food Security and...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Arunkumar K Muthupandi G S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Food waste and insecurity using a multifaceted approach that includes nutrition-focused interventions in supportive housing, culturally appropriate food programs for Indigenous communities, and mobile applications to cut down on food waste. Geographical barriers, cultural significance, and logistical inefficiency are among the major barriers. This study assesses the impacts of digital food-waste apps, targeted nutritional interventions, and Indigenous food sovereignty initiatives using a mixed-methods approach. The integration of community-based and technology-driven solutions is unprecedented. The results highlight the worth of cooperative, sustainable approaches toward improving community well-being, environmental resilience, and food access.

关键词： Food waste Waste reduction Economics Food security Collaboration Medical services Mobile applications Global communication Cultural differences Resilience

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AES vs AES-GCM for Data Protection: A Comprehensive Security Comparison

AES vs AES-GCM for Data Protection: A Comprehensive Security...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sharma, Tanishque Singh, Anmol Raj, Gaurav Sar, Ayan Choudhury, Tanupriya Saraf, Santosh Dewangan, Bhupesh Kumar Sharda University Dept. of Computer Science Greater Noida201310 India School of Computer Science Uttarakhand Dehradun248007 India Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Department of Ec Uttarakhand Dehradun248002 India Op Jindal University Department of Computer Science and Engineering Chhattisgarh Raigarh India

ISBN: (纸本)9798350373783

In our increasingly digital world, the protection of sensitive data is paramount. This research conducts a meticulous comparative study of the two stalwart encryption algorithms: the There are two types of encryption algorithms: AES and AES-Galois/Counter Mode (AES-GCM). Our investigation delves deep into the inner workings of these algorithms, unveiling their respective strengths and vulnerabilities. The primary objective is to provide practical guidance for selecting the most suitable encryption algorithm to meet specific confidentiality requirements. By evaluating the confidentiality and processing efficiency of AES and AES-GCM, we aim to equip decision-makers with the knowledge necessary to make informed choices. The implications of this research are significant, as they promise to enhance data confidentiality practices, reinforcing trust in the safeguarding of sensitive information. In an era where data breaches and cyber threats loom large, this study contributes to the ongoing efforts to fortify data confidentiality, ensuring the confidentiality and integrity of digital assets. © 2024 IEEE.

关键词： Data integrity

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Advances in Deep Learning for Tuberculosis Detection in Chest X-Rays: A Review of Diagnostic Accuracy

Advances in Deep Learning for Tuberculosis Detection in Ches...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Jayakumar K G Muthupandi Dhayanidhi R S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. Optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model.

关键词： Deep learning Accuracy Tuberculosis Transfer learning Medical services Feature extraction X-ray imaging Optimization Tuning Biomedical imaging

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Fine-Tuning of Large Language Models for Solving DSA Problems Using QLoRA

Fine-Tuning of Large Language Models for Solving DSA Problem...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Pavan Kalyan S Arun Kumar B R Satish Kumar T Prem Raj S Pratik B Patil Pothireddy Harshini Dept. Computer Science and Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Fine-tuning large language models (LLMs) for domain specific tasks is often an expensive resource intensive procedure requiring large computing and memory resources. In this paper, We introduce finetuned-leetcode-CodeLlama-7b, a version of CodeLlama that was fine tuned for solving LeetCode problems, specifically. Using QLoRA (Quantized Low Rank Adaptation) techniques, we show that smaller models achieve high performance when solving Data Structure and Algorithm (DSA) problems, providing a cost effective alternative to larger models. The proposed incremental approach reduces memory and computational needs, keeping high accuracy while providing advanced problem-solving in resource constrained environments.

关键词： Adaptation models Computational modeling Large language models Memory management Data structures Data models Computational efficiency Problem-solving Sustainable development Optimization

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Diving into Brain Complexity: Exploring Functional and Effective Connectivity Networks 18

Diving into Brain Complexity: Exploring Functional and Effec...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Siddique, Abu Bakar Khalid, Hassan Bin Ali, Raja Hashim University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan

ISBN: (纸本)9798350328172

The human brain's intricate functions are under-pinned by a vast network of synapses that enable chemical impulses between neurons. Neuroscientists employ two key approaches, functional and effective connectivity, to understand the brain's complexity, focusing on its operations, cognition, and behavior. While both methods utilize graph theory for network analysis, functional connectivity, which investigates associated brain activity, has seen more substantial research efforts than effective connectivity, which examines information processing's inter-regional impacts. In this research paper, we aim to present an extensive examination of the emerging discipline that combines graph theory with the analysis of brain characteristics, shedding light on how these characteristics arise from the interactions among different groups of neurons. Our primary focus lies in exploring the diverse cognitive and neurological applications that leverage functional Magnetic Resonance Imaging (fMRI) as a tool. Additionally, we offer a comprehensive overview of the methods employed to construct brain networks based on functional and efficient connections. Throughout the discussion, we emphasize the advantages and limitations associated with these approaches. © 2023 IEEE.

关键词： Graph theory

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A Review on AI-Driven Predictions of Respiratory Health Impacts from Long-Term Air Quality Trends

A Review on AI-Driven Predictions of Respiratory Health Impa...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Pravallika Kondapalli Pardeep Kumar Dept. of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Public health problem of air pollution is growing close to the respiratory health. In this review, the use of AI driven models to forecast disease such as asthma and chronic obstructive pulmonary disease (COPD) is studied in relation to air quality trends and health; in particular, both the disease and exposures are forecast. With this, these models take advantage of the prediction accuracy of air pollution and impacts on health through machine learning (ML), deep learning (DL), and Internet of things (IoT) based systems. Real time environmental data used by advanced AI techniques are long short term memory (LSTM) networks, convolutional neural networks (CNNs), and hybrid AI-IoT systems. These techniques are discussed in the paper to achieve a more precise forecasting. It reveals how AI can anticipate exacerbations of respiratory diseases, and hospital admissions, and hence extract further information for the individual’s personalized health risk assessment. Furthermore, the review also discusses the impediments in implementing AI based model such as data quality, model interpretability, privacy and fairness. It finally presents research directions to further improve the model transparency, real time data integration, predictive capability refinement to improve public health policies and early intervention for respiratory health.

关键词： Atmospheric modeling Predictive models Market research Air pollution Data models Real-time systems Chronic obstructive pulmonary disease Artificial intelligence Public healthcare Long short term memory

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Natural Language Processing for Conversational AI: Chatbots and Virtual Assistants 3

Natural Language Processing for Conversational AI: Chatbots ...

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3rd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Shrivastava, Neeraj Tewari, Pushpa Sujatha, S. Bogireddy, Srinivasa Rao Varshney, Neeraj Sharma, Vinod Medicaps University Dept. of Engineering Indore India Guru Nanak College Dept. of Vocational Studies Dhanbad India K. Ramakrishnan College of Technology Dept. of Civil Engineering Trichy India Horizon Systems Inc Dept. of Software Engineer Phoenix AZ United States Gla University Dept. of Cse Mathura India Jiwaji University Dept. of Computer Science Gwalior India

ISBN: (纸本)9798331521691

The frontline in developing the capabilities of chatbots and virtual assistants involves the use of NLP, enabling these machines to evolve into truly advanced, interactive machines that are able to comprehend and respond to human language with an almost unimaginable degree of accuracy. The paper will analyze some of the major NLP techniques and methodologies used for such systems: machine learning algorithms, neural networks, and semantic analysis. This would also mean that, with the help of big data and continuous learning, the work of chatbots and virtual assistants will range from answering queries to personalized recommendations. Language understanding, generation, and context management are discussed in detail, underlining how NLP improves user experience by making interactions more natural and intuitive. The challenges of ambiguous language processing, conversational context, and ensuring data privacy and security are discussed as well. Case studies and applications in real life show the impact of NLP on different sectors: customer service, healthcare, and e-commerce. The findings bring out the potential of NLP to change human-computer interaction and allow for more responsive and intelligent digital assistants. © 2025 IEEE.

关键词： Artificial Intelligence Chatbots Natural Language Processing NLP Sentiment Analysis User Experience Virtual Assistants Voice Recognition

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