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P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Intelligent Automation & Soft Computing 2023年第1期35卷 553-566页

作者： Ayman Altameem Ramesh Chandra Poonia Ankit Kumar Linesh Raja Abdul Khader Jilani Saudagar Department of Computer Science and Engineering College of Applied Studies and Community ServicesKing Saud UniversityRiyadh11533Saudi Arabia Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Engineering and Applications GLA UniversityMathuraUPIndia Department of Computer Application Manipal University JaipurRajasthan303007India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Data clustering is crucial when it comes to data processing and *** new clustering method overcomes the challenge of evaluating and extracting data from big *** or categorical data can be *** clustering methods favor numerical data clustering and ignore categorical data *** recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering ***,these algorithms could not use the concept of categorical data for *** that,suggestions for expanding traditional categorical data processing methods were *** addition to expansions,several new clustering methods and extensions have been proposed in recent *** is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster *** paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster *** parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also *** results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.

关键词： ROCK K-means algorithm clustering approaches unsupervised learning K-histogram

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Assessment and Prediction of Urban Pollutants and its Influence on Human Health using Deep Learning Algorithm 9

Assessment and Prediction of Urban Pollutants and its Influe...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Kavathekar, Vaishali Tripathy, Amiya Kumar Chettri, Sarat Kumar Bhanage, Vinayak Zaidansari, Mohammad Babu, Lavena Joshua, Allen Assam Don Bosco University Computer Science And Engineering Assam India Don Bosco Institute of Technology Mumbai India Don Bosco Institute of Technology Computer Engineering Mumbai India Assam Don Bosco University Computer Application Assam India Hiroshima University Advance Science And Engineering Hiroshima Japan Don Bosco Institute of Technology Information Technology Mumbai India

ISBN: (纸本)9798350394474

Mumbai, the financial center of India, is a thriving city where the urban environment is constantly changing. Degradation of the environment is a result of these changes. Recent research reveals a recurring problem, wherein pollutant levels regularly exceed advised thresholds, thus having an adverse effect on human health. The research makes use of meteorological data from four weather stations (Powai, Sion, Colaba and Borivali) that span the years 2000-2023. The chosen contaminants for study are PM2.5, PM10, and air temperature. All of the contaminants are growing exponentially, according to the study. For future prediction, deep learning techniques are used. In comparison to the other algorithms, the results show that Bi-LSTM is more accurate. Based on this model, it appears that regular places will soon become hotspots and maybe dangerous. With the use of this model, Mumbai metropolitan planners and authorities may make timely and well-informed decisions that would promote the city's sustainable growth and citizens well-being. © 2024 IEEE.

关键词： Data visualization

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FEMD: Feature Enhancement-aided Multimodal Feature Fusion Approach for Smart Contract Vulnerability Detection 30

FEMD: Feature Enhancement-aided Multimodal Feature Fusion Ap...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Li, Juan Li, Rui Lu, Youshui Cai, Bowen Cao, Yulin Lin, Hang Li, Xingxing Li, Chan Qinghai Normal University School of Computer Science Xining China Xidian University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Qinghai Normal University School of Computer Science and Technology Xian China School of the Instrument Science and Technology School of Electrical Engineering Xi'an Jiaotong University Xian China School of Science and Engineering University of Minnesota - Twin City Minneapolis United States

ISBN: (纸本)9798331515966

Smart contracts, due to their immutability and transparency upon deployment, entail significant economic and systemic risks from any vulnerabilities present. Traditional vulnerability detection methods suffer from low automation and high false positive rates, while existing deep learning-based approaches inadequately extract contract features, thereby limiting detection accuracy. To address these issues, this paper proposes FEMD: a feature-enhanced aided multimodal feature fusion method for smart contract vulnerability detection. Building on the foundation of addressing the low automation of traditional detection tools, our method improves the model's feature extraction performance and detection capabilities. Specifically, we construct a contract graph through Comprehensive Expert-Graph Fusion, combining multi-modal feature fusion using a multi-head attention mechanism with expert patterns to ensure the capture and effective preservation of all critical information during the fusion process. To delve deeper into potential information within smart contract graphs, we employ a Feature Enhancer that leverages transpose operations on feature matrices to extract complex interaction patterns across different dimensions. Our approach is validated through batches of experiments on the Ethereum open dataset focusing on reentrancy and timestamp dependency vulnerabilities, demonstrating significant improvements in detection accuracy and robustness. © 2024 IEEE.

关键词： Deep learning

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PriRoAgg: Achieving Robust Model Aggregation With Minimum Privacy Leakage for Federated Learning

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5690-5704页

作者： Hou, Sizai Li, Songze Jahani-Nezhad, Tayyebeh Caire, Giuseppe Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong Southeast University School of Cyber Science and Engineering Nanjing 211189 China Supervision and Management (Southeast University) Ministry of Education Engineering Research Center of Blockchain Application Nanjing 211189 China Technische Universität Berlin Department of Electrical Engineering and Computer Science Berlin 10587 Germany

Federated learning (FL), as a promising machine learning paradigm for large-scale distributed data, faces two security challenges of privacy and robustness: the transmitted model updates potentially leak sensitive user information, and the lack of central control over local model updates leaves the global model susceptible to malicious attacks. Current solutions attempting to address both problems under the one-server FL setting fall short in the following aspects: 1) design for simple validity checks that are insufficient against advanced attacks (e.g., checking norm of individual update);and 2) have partial privacy leakage for more complicated robust aggregation algorithms (e.g., distances between model updates are leaked for multi-Krum). In this work, we formalize a novel security notion of aggregated privacy that characterizes the minimum amount of user information, in the form of aggregated statistics of users’ updates, that is necessary to be revealed to accomplish more advanced robust aggregation. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy. As concrete instantiations of PriRoAgg, we construct two secure and robust protocols based on state-of-the-art robust algorithms, for which we provide full theoretical analyses on security and complexity. Extensive experiments are conducted for these protocols, demonstrating their robustness against various model integrity attacks, and their efficiency advantages over baselines. © 2005-2012 IEEE.

关键词： Byzantine resilience Federated learning privacy preservation secure and robust aggregation

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Play Store App Analysis & Rating Prediction Using Classical ML Models & Artificial Neural Network 7

Play Store App Analysis & Rating Prediction Using Classical ...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Moharana, Bhimasen Biswal, Bhramara Bar Dey, Snehasis Rath, Manas Kumar Banerjee, Shobhan Lovely Professional University Department of Computer Science and Engineering Punjab India College of Engineering P.G. Department of Computer Science & Applications Bhubaneswar India College of Engineering Electronics & Telecommunication Engineering Bhubaneswar India Maharaja Sriram Chandra Bhanja Deo University Department of Computer Application Baripada India Birla Institute of Technology and Science Department of Engineering Technology Pilani India

ISBN: (纸本)9798350304268

Each app available in the App Store or Play Store has various aspects linked to it such as its version, category, number of installations, genre, etc. which makes it unique, robust & popular. The reviews and ratings given by end users play an important role for both the developers and other users for the performance and survival of the app in the marketplace. Reviews are just a description for justification of the rating given by a user & the rating plays an important role in the first look. In this paper, we have used the Play Store Analysis Dataset to descriptively analyze the various attributes present in it and create models that predict the rating of an app, given its specifications. The models have been compared based on a common accuracy metric based on which their reliability can be judged and used in real-time rating predictions for a new application in the store for which a certain amount of data is available over some period. © 2023 IEEE.

关键词： Descriptive Statistics ML Classification Models Model Comparison Play Store App Analysis Rating Prediction

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Advanced Machine Learning Approach for Suspicious Coded Message Detection using Enigma Cipher 2

Advanced Machine Learning Approach for Suspicious Coded Mess...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Hussain, Syed Mohideen, S. Pakkir Institute of Science and Technology B.S Abdur Rahman Crescent Computer Science and Engineering Department Chennai India Institute of Science and Technology B.S Abdur Rahman Crescent Computer Application Department Chennai India

ISBN: (纸本)9798350346640

Framework to avoid global crime especially terrorist attacks, bomb blasts, and drone attacks all major and minor attacks with the help of SCMD (Suspicious Cryptographic Message Detection) such as cryptanalysis of SMS or any messaging system. When the server received a suspicious encrypted message, the proposed framework will decrypt the suspicious message and facilitate in predicting the crime, criminal name, location, and criminal details and report to the electronic crime agency. To date, no proper solution to avoid such a big crime as the world is facing problems still today along with this machine learning technique, Artificial Intelligence and semantic web ontology and Cryptanalysis of Enigma Cipher. Terrorists communicate with their teammates in different locations of the world and instruct with resources to attack the government or public while communicating they use the encrypted message to maintain secrecy. Our framework at the server site will detect encrypted messages and decrypt them with the help of Cryptanalysis of Enigma Cipher techniques. The framework will decrypt a suspicious message and expedites predicting the type of crime from microblogs before it gets executed by criminals. Details of criminals will be alerted to cybercrime the department that reduces the tension for the various security departments. Criminals use an encrypted message to pass activity among teammates who may be in living in any corner of the World and other people may decrypt and convert it to plain text and takes action. © 2023 IEEE.

关键词： Cryptography

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Determining human-coronavirus protein-protein interaction using machine intelligence

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Medicine in Novel Technology and Devices 2023年第2期18卷 123-142页

作者： Arijit Chakraborty Sajal Mitra Mainak Bhattacharjee Debashis De Anindya J.Pal Bachelor of Computer Application Department The Heritage AcademyKolkataIndia Department of Computer Science and Engineering Heritage Institute of TechnologyKolkataIndia Department of Biotechnology Heritage Institute of TechnologyKolkataIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataIndia University of Burdwan BurdwanIndia

The Severe Acute Respiratory Syndrome CoronaVirus 2(SARS-CoV-2)virus spread the novel CoronaVirus−19(nCoV-19)pandemic,resulting in millions of fatalities *** research demonstrated that the Protein-Protein Interaction(PPI)between SARS-CoV-2 and human proteins is accountable for viral ***,many of these PPIs are poorly understood and unexplored,necessitating a more in-depth investigation to find latent yet critical *** article elucidates the host-viral PPI through Machine Learning(ML)lenses and validates the biological significance of the same using web-based *** classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins,namely Amino Acid Composition,Pseudo Amino Acid Composition,Conjoint Triad,Dipeptide Composition,and Normalized Auto Correlation.A majority voting rule-based ensemble method composed of the Random Forest Model(RFM),AdaBoost,and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this *** proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor≥70%,validated by utilizing Gene Ontology(GO)and KEGG pathway enrichment ***,this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

关键词： Coronavirus Ensemble learning Machine intelligence Protein-protein interaction

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CLAACS-IOD: Certificate-embedded lightweight authentication and access control scheme for Internet of Drones

CLAACS-IOD: Certificate-embedded lightweight authentication ...

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作者： Sadhukhan, Dipanwita Ray, Sangram Dasgupta, Mou Rodrigues, Joel J. P. C. Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla India Department of Computer Application National Institute of Technology Raipur Raipur India Qingdao China Instituto de Telecomunicações Covilhã Portugal

In recent era, the unmanned aerial vehicles (UAVs) commonly known as drones has emerged as a one of the most significant and promising tools which has demonstrated its wide range of implementations variating from commercial domain to the field of defense due to its distinct capabilities such as inspection, surveillance, precision and so forth. Internet connected drones provides a propitious trend that boosts the flying safety, and service qualities of the UAVs where numerous low-altitude drones winged in different flying regions for executing a precise task such as gathering the real-time information from the unuttered environment to be interpretated by users. Nevertheless, the open-access insecure communications in hostile environment, the issues like safety and confidentiality threats, various security concerns such as the leakage of flying courses, identities, position, and gathered data by the drones are upstretched. To address these security concerns, access control mechanism provides a potential service in terms of authentication and key agreement for securing the communication between the individual drones within their respective flying regions. This article introduces a robust, efficient, lightweight, and privacy preserving ECC integrated access control approach by employing digital certificate with considering the high dynamicity and mobility of the drones. The designing of this proposed approach, that is, CLAASC-IoD is influenced by the aim of inter-drone and drone-to-ground station communication in the IoD paradigm. The detailed analysis of security using probabilistic random oracle model as well as simulation using well-accepted security verification tool AVISPA and comparative performance evaluation supports the claim of robustness, effectiveness, and proficiency. © 2023 John Wiley & Sons Ltd.

关键词： Internet of things

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Improving Connectivity of IoT-Enabled Networks Through Big Data Analytics 15

Improving Connectivity of IoT-Enabled Networks Through Big D...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kiran, K.S. Sen, Sumanta Sharma, Sunil School of Engineering and Technology Karnataka Bangalore India ARKA JAIN University Department of Pharmacy Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

The net of things (IoT) has quick grow to be the backbone of many corporations, taking into account real-time statistics collection and automatic tasks. However, the connectivity of these IoT-enabled networks can become without difficulty congested when huge amounts of data are generated in a brief time frame. To be able to hold a high stage of overall performance, big facts analytics can be used to research the massive volumes of generated statistics, provide insights and locate anomalies to improve community connectivity. Massive statistics analytics will permit higher records processing and evaluation competencies, become aware of regions of improvement and optimize network operations. Additionally, predictive analytics techniques can be used to forecast upcoming community congestion and take movement that could enhance network performance in real time. This, but, requires green facts collection, garage and control gear to ensure that the big records analytics answer is effective and efficient. This paper investigates how massive records analytics may be used to improve the connectivity of IoT-enabled networks through leveraging the energy of predictive analytics. © 2024 IEEE.

关键词： Predictive analytics

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Classification of Foods based on Bijective Soft Set

Classification of Foods based on Bijective Soft Set

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International Conference on Computing and Networking Technology (ICCNT)

作者： Priyanka Das Rupam Bhattacharya Dept. of Computer Application and Science Institute of Engineering & Management Kolkata

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cuisine data is typically classified using images of the cuisine, cooking methods, cooking utensils, and, most importantly, ingredients. The vast amount of recipe data available on the internet necessitates the creation of specialised systems for storing, retrieving, and effectively utilising stored knowledge and data. Neural networks based expert systems are steadily being used to classify the cuisine data. But it lacks in dealing the vagueness present in the categorical cuisine data. Also, most of the popular recipes available online are mostly unhealthy. As a result, the Bijective soft set theory has been introduced into this study since it is an intelligent method for developing classification rules from food data and then recognising ambiguity and data reliance. The classification guidelines also address the necessity to select healthy/nutritious meals. The investigation outcomes from using bijective soft set analysis to a set of data samples are presented and appraised in this work. Furthermore, the classification rules derived from this approach provides valuable help for developing food recommendation systems. The accuracy of this classification scheme has got really influenced by application of the bijective soft set when compared to other state-of-the-art classifiers.

关键词： Accuracy Neural networks Set theory Expert systems Recommender systems Guidelines

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