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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Dash, Shiba Prasad Sahoo, Rajesh Kumar Singh, Lipsa Priyadarshini Barik, Ram Chandra Biju Patnaik University of Technology Department of Computer Science and Engineering Rourkela India Centurion University of Technology & Management Department of Computer Science and Engineering Paralakhemundi India C. V. Raman Global University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350369175

In recent years, Recommender Systems (RS) have experienced significant growth across various sectors. Because of the recent growth of many e-commerce companies and online video services like Netflix, YouTube, Hotstar, etc., researchers are quite interested in it. The goal of the collaborative filtering-based recommender system is to provide users with movie or video recommendations based on their past viewing preferences. A rating matrix is typically used to display this data. In this study, the K-nearest neighbor and hierarchical clustering techniques are used to create a movie recommendation system. This work develops recommender systems using a range of instruments and methods. Several techniques, such as clustering techniques, KNN, collaborative filtering, and content-based filtering, are described in depth. The suggested approach for developing the movie recommender system is then illustrated, showing how it would be implemented and operated. First, the proposed model checks similar movies in the movie list and uses a hierarchical clustering technique to make the cluster of similar movies. For this process, Jaccard distance is used. Then the proposed methodology uses the K-nearest neighbor algorithm to recommend the best suitable movies to the users. To verify the suggested method, this study uses the Movielens dataset, which is available on Kaggle. Python is used as the programming language for implementing the system. © 2024 IEEE.

关键词： Hierarchical clustering

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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A Multifaceted Approach for Identifying Propaganda on Social Networks 1

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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Khanday, Akib Mohi Ud Din Baniya, Pashupati Bhushan, Bharat Alazzawi, Esraa Mohammed Jamil, Akhtar Agrawal, Atul United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Engineering University of Baghdad Al-Jadriya Baghdad10071 Iraq Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Department of Computer Science and Engineering IEC College of Engineering and Technology Greater Noida India

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

Online Social media facilitate human interaction, information sharing, and opinion expression in digital era. Misinformation, disinformation and propaganda propagate quickly through social media platforms and impacts public opinion, political debate, and societal views. Understanding the mechanics of propaganda dissemination and its repercussions on society is critical for reducing its negative implications. Recognizing propaganda will allow individuals to safeguard their values and resist attempts to manipulate them. In this paper we propose a hybrid feature selection technique to classify the propaganda and non propaganda tweets. Data is prepared based on the annotation scheme and features are selected by fused various state of art feature selection techniques. Various machine and ensemble learning classifiers are trained and tested based on the features selected. The results showed that the there is an increase in the performance of all the classifiers trained on the proposed hybrid selection criteria. In future Deep learning techniques may be incorporated to improve the efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Contrastive Learning

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Microplastic Identification in Seawater using Generative Adversarial Networks

Microplastic Identification in Seawater using Generative Adv...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Rajasekar, M. Geetha, Angelina Hindustan Institute Of Technology And Science Department Of Computer Applications Chennai India Departmnet Of Computer Science And Engineering Hindustan Insitute Of Technology And Science Chennai India

ISBN: (纸本)9798350364828

In recent years, microplastics-plastic particles smaller than five millimeters-have become a significant environmental contaminant. These tiny plastic pieces have been found permeating aquatic ecosystems globally, posing a threat to marine life upon ingestion. However, identification and quantification of microplastics in seawater samples remains an arduous task. The small size and translucent nature of microplastics makes them difficult to distinguish from natural particulates in seawater under a microscope. This has hindered efforts to accurately monitor microplastic pollution levels in the marine environment. Our goal in this study was to use cutting edge deep learning techniques to create an automated image based system for detecting microplastics in seawater. We chose to employ a generative adversarial network (GAN) architecture for its proven capability in generating highly realistic synthetic images. The GAN model was trained on a dataset of microscope images of microplastics and natural particulates extracted from seawater samples. Coastal seawater samples were first filtered to isolate particulates of a size range containing microplastics. Density separation was then used to separate the plastic microparticles from denser natural particulates. We implemented a deep convolutional GAN with a generator network to produce synthetic microplastic images, and a discriminator network to differentiate real from synthetic images. Through iterative adversarial training, the two networks evolved to produce and accurately classify images of microplastics with a high degree of realism. Advanced image processing techniques were used to enhance the training data. After training, the GAN model was evaluated on an annotated testing dataset of images containing real-world microplastics and natural particulates extracted from seawater. Our proposed GAN-based technique achieved a microplastic classification accuracy of 92.5% on the test set, demonstrating its effectiveness. The

关键词： Convolutional neural networks

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CelestLearn AI: Astronomical Image Generator and Classifier

CelestLearn AI: Astronomical Image Generator and Classifier

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Sadasivam, Tanuj Serrao, Ria Treza Prasad Bansod, Aditya Singh, Tanay Bharathi, R. PES University Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350361537

Humanity's long-standing fascination with the celestial realm and its intersection with recent technological advancements has led to CelestLearn AI. It is an interactive portal developed for Astronomical Image Generation and Classification. The portal provides an image generator that produces synthetic celestial images showcasing Stars, Nebulae, and Galaxies, along with a 3-way classifier that classifies the same. Galaxies are further categorized into five distinct morphologies: Cigar-shaped smooth, Completely round smooth, Edge-on, In-between smooth and Spiral galaxy morphologies. To achieve these goals, a dataset is curated from sources like GalaxyZoo2 and the ESA-Hubble Image Archive. Deep learning methods such as a Deep Convolutional GAN (DCGAN) and ResNet50V2 classifiers are employed. The ensuing discussion focuses on the web portal as well as the revelations and challenges encountered during the training of the DCGAN and ResNet50V2 classifiers. Performance evaluation metrics, such as the Inception Score and Structural Similarity Index are employed to assess the image generator's capabilities. Accuracy, precision, and recall are utilized to gauge the effectiveness of the classifier. CelestLearn AI is designed as a valuable tool for celestial exploration and educational purposes. © 2024 IEEE.

关键词： Deep learning

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Diffusion Posterior Sampling is Computationally Intractable 41

Diffusion Posterior Sampling is Computationally Intractable

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41st International Conference on Machine Learning, ICML 2024

作者： Gupta, Shivam Jalal, Ajil Parulekar, Aditya Price, Eric Xun, Zhiyang Department of Computer Science University of Texas Austin United States Department of Electrical Engineering and Computer Science University of California Berkeley United States

Diffusion models are a remarkably effective way of learning and sampling from a distribution p(x). In posterior sampling, one is also given a measurement model p(y | x) and a measurement y, and would like to sample from p(x | y). Posterior sampling is useful for tasks such as inpainting, super-resolution, and MRI reconstruction, so a number of recent works have given algorithms to heuristically approximate it;but none are known to converge to the correct distribution in polynomial time. In this paper we show that posterior sampling is computationally intractable: under the most basic assumption in cryptography-that one-way functions exist-there are instances for which every algorithm takes superpolynomial time, even though unconditional sampling is provably fast. We also show that the exponential-time rejection sampling algorithm is essentially optimal under the stronger plausible assumption that there are one-way functions that take exponential time to invert. Copyright 2024 by the author(s)

关键词： Heuristic algorithms

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Fake Currency Detection using Image Processing

Fake Currency Detection using Image Processing

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Sharma, Srishti Jain, Shreyansika Saurabh Kuhar, Satyam The NorthCap University Gurugram Department of Computer Science and Engineering India

ISBN: (纸本)9798350375237

The continuous advancements in technology make the process of production and dissemination of counterfeit currency easier than ever before. Counterfeit currency may be produced with the intent of making profit, engaging in illegal activities such as drug trafficking, money laundering or financing terrorism, all of which hurt society greatly. Most of the time, innocent individuals become the medium for circulation of counterfeit currency who inadvertently circulate counterfeit currency without realizing it is fake, especially if they are not familiar with the security features of legitimate currency. Banks and other financial or commercial organizations have equipment in place to verify the legitimacy of currency. However, the common man does not have access to such systems. Hence there is an urgent and dire need for a software that can be utilized by the common man at any place and time to identify counterfeit currency. This paper proposes an approach employing the image processing technique, Oriented Fast and Rotated Brief (ORB) to help the common man identify counterfeit currency. The approach shows promising results achieving up to 0.7674 SSIM score. © 2024 IEEE.

关键词： Image processing

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Fog-Assisted Blockchain-IoMT Healthcare Framework with Role-Based Access Control for Critically Ill Patients

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SN computer science 2024年第6期5卷 658页

作者： Mallick, Soubhagya Ranjan Lenka, Rakesh Kumar Tripathy, Pradyumna Kumar Rao, D. Chandrasekhar Sharma, Suraj Ray, Niranjan Kumar School of Technology Woxsen University Telangana Hyderabad 502345 India Department of Computer Science Central University Odisha Koraput 764021 India Computer Science and Engineering Silicon University Odisha Bhubaneswar 751024 India Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela 769015 India Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur 495009 India School of Computer Science and Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar 751024 India

Hospitals, clinics, pharmaceutical firms, diagnostics centres, pathology labs, insurance companies, and emergency vehicles must be connected in a unified system to better serve patients worldwide with cutting-edge medical care. Most previously existing systems rely on cloud-based platforms, which face several technological challenges, including data availability, data accessibility, processing delays, scalability concerns, problems gathering real-time data, and difficulties monitoring patients. Information about a person’s health is extremely private and should be protected with the utmost care. To address these issues, We propose a decentralised, secure, and efficient framework incorporating the internet of things, fog computing, and blockchain technology to collect, process, manage, and store healthcare data. A unique role-based access control is used in the proposed framework to prioritise critically ill patients while maintaining confidentiality, scalability, data availability, and system security. Finally, we evaluate the efficiency of our proposed framework by conducting experiments on device registration time, transaction latency, transaction throughput, and access control in medical environments using realistic healthcare scenarios. We then compare the results to existing fog models to determine the system’s performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain Cloud computing Fog computing Healthcare IoMT Scalability

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Stochastic Circuits for Computing Weighted Ratio With Applications to Multiclass Bayesian Inference Machine

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IEEE Transactions on computers 2024年第2期73卷 621-630页

作者： Chu, Shao-I Wu, Chi-Long Chien, Tzu-Heng Liu, Bing-Hong Nguyen, Tu N. National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung80778 Taiwan Kennesaw State University Department of Computer Science MariettaGA30060 United States

Bayesian inference is one method of statistical inference in machine learning. It predicts the probability that a given test belongs to a certain class and is widely used in various applications such as medical diagnosis, spam classification and fraud detection. The conventional binary architecture of computing the posterior probability is inefficient in practical implementation, which is involved in multiplication, addition and division operations. Recently, it has been shown that simple Muller C-elements, the asynchronous logic units, can perform stochastic Bayesian inference motivated by its truth table when the data is encoded as the bit-stream. The Bayesian inference machine is therefore implemented with low hardware cost. However, such an architecture is employed to compute the posterior probability of two classes only. This brief presents two stochastic circuit designs for computing the weighted ratio with multiple weights for generalized multi-class Bayesian machines. The first design is mainly based on the JK flip flop and multiplexers. The second approach is to construct the finite state machine (FSM) by manipulating the correlation between the input bit-streams. The FSM-based design requires fewer random number sources (RNSs) as compared to the JK flip flop-based implementation. These facts lead to a reduction of hardware area and energy. Simulation results show that the accuracy of the proposed JK flip flop-based and FSM-based designs is almost the same in the tested data sets. As compared to the traditional binary design, the circuit area of the proposed stochastic design is improved by 96%96% at least in the cases of three and four classes. The consumed energy per operation is reduced by 58.1%58.1% at least in the cases of three and four classes. © 1968-2012 IEEE.

关键词： Adders

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An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

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computer Systems science & engineering 2023年第7期46卷 819-834页

作者： Samah Alshathri Ayman El-Sayed Walid El-Shafai Ezz El-Din Hemdan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Recently,the Internet of Things(IoT)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console *** the increased use of Industrial IoT(IIoT)applications,the risk of brutal cyber-attacks also *** leads researchers worldwide to work on developing effective Intrusion Detection Systems(IDS)for IoT infrastructure against any malicious ***,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure.A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is *** data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT ***,this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT *** is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient ***,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion *** performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed *** experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed *** addition,the proposed work accomplishes high performance compared to other recent rela

关键词： Attacks intrusion detection machine learning deep learning industrial IoT TON_IoT dataset

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