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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pushpa, R. Rekha, H. Akshaya Institute of Technology Department of computer Science and Engineering Tumakuru India Shridevi Institute of Engineering and Technology Department of Information Science and Engineering Tumakuru India

ISBN: (纸本)9798331300579

The incorporation of Machine Learning (ML) and Artificial Intelligence (AI) technologies across diverse industries presents considerable potential for both economic expansion and societal ***, which includes both Narrow and General forms, in conjunction with ML, enables data-driven decision making and automation in numerous industries, including healthcare, finance, and ***, the extensive implementation of these technologies is beset by obstacles such as ambiguity in algorithms, ethical concerns, and *** paper studies the developments and obstacles in Al and ML, as well as their practical implementations in nanotechnology, medicine, governance, and *** the potential for economic benefits and societal advancements is apparent, it is imperative to prioritize responsible and equitable implementation of Al by addressing organizational shortcomings, personnel issues, and economic *** is imperative that governments, organizations, and academia work together in concert to surmount these challenges and fully exploit the capabilities of Al and ML for the betterment of *** paper emphasizes the significance of conducting a thorough analysis of the ethical, societal, and economic consequences in order to effectively achieve the potential of Al and ML. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Construction of a Quantum Random Number Generator 4

Construction of a Quantum Random Number Generator

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4th International Conference on Innovations in computer Science, ICONICS 2024

作者： Karera, Kashish Khan, Areej Tariq, Humanyou Nadeem, Mantasha Zafar, Roohi Kamran, Muhammad Khan, Muhammad Mubashir NED University of Engineering and Technology Department of Physics Karachi Pakistan NED University of Engineering and Technology Department of Computer Science and Information Technology Karachi Pakistan

ISBN: (纸本)9798331504342

Quantum random number generators (QRNG) are the prime source of randomness, by exploiting the unpredictable nature of Quantum mechanics QRNG aims to provide a string of completely random utterly unpredictable numbers. QRNGs are crucial for cryptography, scientific calculation, simulation, and many more real-world applications. Our Optical Quantum Random Number Generator (OQRNG) utilizes a high-speed pulsating laser, a Polarizing beam splitter (PBS), photodetectors, and advanced signal processing units that capture and digitize random photon intensity forming the raw random bit stream. This technique is famously known as Branching Path Technique. After that, we implement rigorous testing that encompasses the statistical randomness tests developed by National Institute of Standards and technology (NIST) to ensure the guaranteed ran-domness meets security standards. A user-friendly Graphic user interface (GUI) simplifies interaction with the QRNG allowing the user to configure device settings, monitor system performance, and securely extract random numbers. Our findings help us demonstrate the superiority of QRNGs over classical random number generators, and highlight the potential of photon-based QRNGs and their environmentally friendly and efficient random number generation. © 2024 IEEE.

关键词： Random number generation

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Hybrid Digital-Analog Semantic Communications

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IEEE Journal on Selected Areas in Communications 2025年第7期43卷 2478-2492页

作者： Xie, Huiqiang Qin, Zhijin Han, Zhu Letaief, Khaled B. Jinan University College of Information Science and Technology Guangzhou China Tsinghua University Department of Electronic Engineering Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China University of Houston Department of Electrical and Computer Engineering Houston United States The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digitalanalog approach for multimedia transmission. This is achieved through the introduction of analog-digital allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDA-DeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality. © 1983-2012 IEEE.

关键词： Digital communication systems

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A Diagnosis Model Based on Federated Learning for Lung Cancer Classification 2nd

A Diagnosis Model Based on Federated Learning for Lung Cance...

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2nd International Conference on Science, engineering Management and information technology, SEMIT 2023

作者： Babu, Ann Mary Bellamkonda, Sivaiah Computer Science and Engineering Department Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9783031722837

Lung cancer, causing a significant number of fatalities annually, is a prominent contributor to global mortality rates among individuals of both genders, with an estimated five million cases resulting in death each year. The utilization of Computed Tomography (CT) scans contribute valuable data for the detection of lung cancer. The main objectives of this study are to diagnose malignant lung nodules in the provided input lung images and to classify lung nodule based on its level of severity. This study proposes a solution wherein locally computed updates are combined to stimulate the learning of a shared model, while the training data remains dispersed throughout mobile devices. Federated Learning (FL) is an advanced technology that operates in a collaborative and decentralized manner, with the primary objective of preserving privacy. Its purpose is to address the obstacles posed by data silos and the sensitivity of data. Therefore, the FL approach is employed for the classification of medical images in collaborative and real-world circumstances. A new approach is proposed in this paper, where the model is trained on data distributed across multiple institutions, and the training process occurs locally at each institution. The performance of the proposed method in terms of accuracy, specificity and sensitivity is evaluated and compared with that of the existing models such as Googlenet, Alexnet, and SVM. The proposed method performs comparably better in all three performance measures indicating that it works better than other methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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Decentralized Influencer Marketing and Sponsorships 3

Decentralized Influencer Marketing and Sponsorships

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Malvi, Pranav Shelke, Priya Shewale, Chaitali Dedgoankar, Suruchi Kadam, Sandeep BRACT's Vishwakarma Information Institute Technology Department of Information Technology Pune India BRACT's Vishwakarma Institute of Information Technology Department of Information Technology Pune India Kse Department of Computer Engineering Handewadi Pune India

ISBN: (纸本)9798350354348

In the realm of digital marketing, influencer marketing has emerged as a potent strategy for brands to engage with their target audience through trusted social media personalities. However, the industry faces challenges related to transparency, accountability, and fair compensation for influencers. In this research we have explored how blockchain technology can address these challenges by recording sponsorship agreements and performance metrics on a public ledger. Additionally, smart contracts can automate payment disbursements based on predefined campaign objectives and key performance indicators, ensuring fair compensation for influencers. By leveraging blockchain, influencer marketing can become more transparent, accountable, and mutually beneficial for both brands and influencers. This paper also explores the broader implications of blockchain-enabled decentralized influencer marketing beyond transparency and accountability. The objective of this study is to provide valuable insights into the transformative potential of emerging technologies in reshaping the influencer marketing landscape. This study investigates the potential of blockchain technology to optimize payment procedures, reduce fraudulent activities, and facilitate smooth transactions, ultimately transforming the operational effectiveness of influencer marketing initiatives. © 2024 IEEE.

关键词： Smart contract

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Algorithmic Proficiency in Spark Configuration Tuning: An Empirical Study using Execution Time Metrics across Varied Workloads 2

Algorithmic Proficiency in Spark Configuration Tuning: An Em...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sewal, Piyush Singh, Hari Computer Science & Engineering Department Jaypee University of Information Technology Solan173234 India

In the realm of big data, where datasets of immense scale pose processing challenges, distributed processing platforms like open-source Apache Spark have emerged to address these issues. Spark's internal configuration parameters exert varying impacts on execution times based on job characteristics, making manual optimization daunting. The core focus of this study lies in optimizing Spark's internal configurations, with specific attention directed towards three types of workloads: Iterative-intensive, Memory-intensive, and CPU-intensive. Employing Grid Search, Random Search, and Evolutionary Optimization algorithms yields substantial execution time reductions: 23.24% with Grid Search, 19.71% with Random Search, and 23.06% with Evolutionary Optimization. Notably, Evolutionary Optimization achieves optimal configurations approximately 29% faster than Grid Search. While Random Search and Evolutionary Optimization share similar time requirements, Random Search's execution time reduction for a given Spark workload is relatively lower. This research sheds light on algorithmic configuration tuning intricacies and its influence on Spark workload execution times, contributing to the exploration of optimizing big data processing platforms. © 2024 Elsevier B.V.. All rights reserved.

关键词： Iterative methods

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Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Fake News Detection on Social Media Using Ensemble Methods

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computers, Materials & Continua 2024年第12期81卷 4525-4549页

作者： Muhammad Ali Ilyas Abdul Rehman Assad Abbas Dongsun Kim Muhammad Tahir Naseem Nasro Min Allah Department of Computer Science COMSATS UniversityIslamabad45550Pakistan School of Computer Science and Engineering Kyungpook National UniversityDaegu41566Republic of Korea Department of Computer Science and Engineering Korea UniversitySeoul02841Republic of Korea Department of Electronic Engineering Yeungnam UniversityGyeongsan-si38541Republic of Korea Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam34223Saudi Arabia

In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of distinguishing between truthful and deceptive *** news,a prevalent issue,particularly on social media,complicates the assessment of news *** pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources,creating confusion and polarizing *** the volume of information grows,individuals increasingly struggle to discern credible content from false narratives,leading to widespread misinformation and potentially harmful *** numerous methodologies proposed for fake news detection,including knowledge-based,language-based,and machine-learning approaches,their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or *** study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news *** employ multiple feature extraction methods,including Count Vectorizer,Bag of Words,Global Vectors for Word Representation(GloVe),Word to Vector(Word2Vec),and Term Frequency-Inverse Document Frequency(TF-IDF),alongside feature selection techniques such as information Gain,Chi-Square,Principal Component Analysis(PCA),and Document *** comprehensive approach enhances the model’s ability to identify and analyze relevant features,leading to more accurate and effective fake news *** findings highlight the importance of a multi-faceted approach,offering a significant improvement in model accuracy and ***,the study emphasizes the adaptability of the proposed ensemble model across diverse datasets,reinforcing its potential for broader application in real-world *** introduce a pioneering ensemble

关键词： Fake news detection Machine Learning(ML) Deep Learning(DL) Chi-Square ensembling

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