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7th IET Smart Cities Symposium (SCS 2023)

作者： R. Surendran M. S. Seena Aravind Swaminathan Joel Benjamin Julius Babu Visal Vinod Department of computer science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai India Data Science Department of Computer Science Middlesex University Knowledge Park Dubai UAE

Air pollution, a major global concern resulting in numerous annual fatalities, has been associated with various health disorders. This study focuses on understanding the impact of residing in heavily polluted cities on mental and behavioral patterns, specifically exploring the potential link between air pollution and autism. Supervised classification algorithms, including logistic regression, random forest, decision tree, and AdaBoost, were employed to predict the risk of autism in individuals residing in severely polluted countries by 2030. The research aims to identify the relationship between genetic variations in adults with Autism Spectrum Disorder (ASD) and harmful air pollutants, investigate gender differences in autism frequency, and determine the neurotoxicant posing the greatest danger for neurodegeneration and its impact on autistic individuals. This study successfully employed supervised classification models to uncover hidden patterns between air pollution and autism risk. The binary prediction model achieved an average accuracy of 70%, with the AdaBoost algorithm demonstrating the highest accuracy at 73% in predicting autism prevalence. Increased PM2.5 concentrations correlated with higher autism risk compared to other neurotoxicants. These findings underscore the importance of sustainable practices and pollution reduction to safeguard human health.

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Cutting-Edge Machine Learning Algorithms for In-Depth Characterization and Efficiency Improvement of Phase Change Heat Transfer Mechanisms

Cutting-Edge Machine Learning Algorithms for In-Depth Charac...

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

作者： Khambra, Geetanjli Praveena, K. Manjunatha Dutt, Amit Khan, Irfan Issa, Ali Ashoor College Department of Computer Applications M.P Bhopal India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This study introduces a sophisticated and holistic methodology for the enhanced understanding and optimization of phase change heat transfer mechanisms. The proposed framework integrates Deep Learning Neural Networks (DLNN), Random Forest (RF), Long Short-Term Memory (LSTM) Networks, Gaussian Processes (GP), and Reinforcement Learning (RL) in a synergistic manner, emphasizing the iterative and adaptive nature of each algorithm. A detailed flowchart for each algorithm delineates the process, showcasing their roles in refining predictions and optimizing models. An ablation study underscores the crucial contribution of each component, revealing their collective efficacy. Comparative analyses against existing algorithms highlight the proposed method's consistent superiority in accuracy, precision, recall, mean squared error, and Rsquared. Resource efficiency metrics further affirm its computational effectiveness. Visualizations provide a comprehensive and intuitive representation of the methodology's strengths. The proposed framework offers a promising avenue for characterizing and optimizing phase change heat transfer, contributing to advancements in thermal sciences and real-world applications. © 2024 IEEE.

关键词： Deep reinforcement learning

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Assessing the Societal Impacts of Emerging Distributed Intelligence Technologies

Assessing the Societal Impacts of Emerging Distributed Intel...

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

作者： Khan, Irfan Alekhya, V. Rajalakshmi, B. Lakhanpal, Sorabh Alkhafaji, Mohammed Ayad Santhi, K. Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Science Bangalore India Lovely Professional University Phagwara India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq St. Martin's Engineering College Dept. of It Telangana Secunderabad India

ISBN: (纸本)9798350373783

The Integrated Impact Assessment Framework (IIAF) is established in this study to examine how new distributed intelligence technologies (DIT) influence society. The framework comprises three powerful algorithms: SNDA, EIS, and EIAM. These illustrate DIT's various benefits. SNDA examines the complex social web, identifying hubs, community dynamics, and future trends. EIS predicts economic repercussions and provides GDP, expenditures, and job losses. computer faults and privacy are important ethical considerations for EIAM. These strategies paint a comprehensive picture of the effect. Fake data suggests the new strategy outperforms six others. The recommended strategy scores higher on significance, economic indicators, and ethics. The IIAF's DIT impact evaluation is more complete, educated, and responsible than other techniques. This work contributes to our understanding of the problem by proposing a flexible and resilient paradigm for dealing with the societal consequences of diverse technologies. The IIAF may demonstrate to politicians the immediate impacts of DIT adoption and discuss morality and society. The framework's holistic approach helps us understand how technology and society interact and evolve. © 2024 IEEE.

关键词： Inflation

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Efficient Data Sampling and Reduction Methods in Large-Scale Forensic Analysis

Efficient Data Sampling and Reduction Methods in Large-Scale...

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

作者： Harika, Ala Sharma, Praveen Aravinda, K. Nagpal, Amandeep Praveen Albawi, Ali Institute of Aeronautical Engineering Hyderabad Dundigal India Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M. P Bhopal India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350373783

In this era of enormous, continuously changing datasets, forensic analysis needs innovative approaches that can adapt to changing data characteristics while maintaining data security. To satisfy this demand, our recommended system includes all algorithms for large forensic investigations. The adaptive stratified sampling algorithm sets the scene. It adjusts strata and sample quantities based on data attributes. The Feature Weighting with Machine Learning Algorithm improves selection by weighting features using machine learning. Based on them, the dynamic cluster-based sampling algorithm organizes data points dynamically. By determining how much information each data point has, the entropy-guided data reduction algorithm improves the framework. The Temporal Correlation Analysis Algorithm finally allows you to view changing data. Tables3 and Tables4 indicate that our approach outperforms others in several key areas. The recommended technique outperforms others in accuracy, memory, F1 score, usability, stability, legal acceptability, ease of application, computing complexity, and freedom. The findings in Figures.6-10 indicate that the proposed framework can balance accuracy and memory, decrease data, and be highly usable, stable, and versatile. Finally, our technology represents a novel technique to conduct large-scale research. It is fast, adaptable, and data-safe. Machine learning, dynamic changes, and temporal variables make our platform robust and comprehensive for current forensic investigations. © 2024 IEEE.

关键词： Adaptive Stratified Sampling Algorithm Algorithm Optimization Data Reduction Data Sampling Dynamic Cluster-based Sampling Entropy-guided Data Reduction Feature Weighting Forensic Analysis Machine Learning Temporal Correlation Analysis

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MKGL: mastery of a three-word language 24

MKGL: mastery of a three-word language

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lingbing Guo Zhongpu Bo Zhuo Chen Yichi Zhang Jiaoyan Chen Yarong Lan Mengshu Sun Zhiqiang Zhang Yangyifei Luo Qian Li Qiang Zhang Wen Zhang Huajun Chen College of Computer Science and Technology Zhejiang University and ZJU-Ant Group Joint Lab of Knowledge Graph Ant Group Department of Computer Science The University of Manchester School of Computer Science and Engineering Beihang University School of Computer Science Beijing University of Posts and Telecommunications School of Software Technology Zhejiang University and ZJU-Ant Group Joint Lab of Knowledge Graph

ISBN: (纸本)9798331314385

Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of triplets and allow minimal hallucinations, remains an underexplored frontier. In this paper, we investigate the integration of LLMs with KGs by introducing a specialized KG Language (KGL), where a sentence precisely consists of an entity noun, a relation verb, and ends with another entity noun. Despite KGL's unfamiliar vocabulary to the LLM, we facilitate its learning through a tailored dictionary and illustrative sentences, and enhance context understanding via real-time KG context retrieval and KGL token embedding augmentation. Our results reveal that LLMs can achieve fluency in KGL, drastically reducing errors compared to conventional KG embedding methods on KG completion. Furthermore, our enhanced LLM shows exceptional competence in generating accurate three-word sentences from an initial entity and interpreting new unseen terms out of KGs.

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Certificate management system using blockchain 11th

Certificate management system using blockchain

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11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired Computing, NaBIC 2019

作者： Endurthi, Anjaneyulu Khare, Akhil Computer Science and Engineering Department Rajiv Gandhi University of Knowledge Technologies BasarTelangana India Computer Science and Engineering Department MVSR Engineering College HyderabadTelangana India

ISBN: (纸本)9783030493448

Overcoming the problems of existing file storage and sharing approaches, this paper gives an efficient way of storing and sharing the files using blockchain and smart contracts. As an example, Managing student’s information and sharing them or giving access to unauthorized people in a much secured way is one of the major responsibilities of any university or organization. Instead of storing the certificates in a normal database, blockchain technology can be used to store the certificates. In our approach, the certificates and other documents will be uploaded to the blockchain and will be shared securely when any third party needs to access the certificates. The university administrator has ultimate rights to upload student’s certificates and managing them. Students can view their documents and certificates;other unauthorized people (third party/organization/employer/others) can access the files after taking permission of the college administrator. These types of approaches are very useful in this era where efficient storage and sharing the files plays a crucial role. The feature which differentiates our approach of certificate management system from existing certificate management system is, our system is decentralized, cryptographically secure, immutable and efficient. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Smart contract

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Security Issues and Defensive Approaches in Deep Learning Frameworks

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Tsinghua science and Technology 2021年第6期26卷 894-905页

作者： Hongsong Chen Yongpeng Zhang Yongrui Cao Jing Xie Department of Computer Science University of Science and Technology Beijing(USTB)Beijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Department of Computer Science and Technology University of Science and Technology Beijing(USTB)Beijing 100083China Defense Electronics Institute China Industrial Control System Cyber Emergency Response TeamBeijing 100040China

Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous ***,the security issues of deep learning frameworks are among the main risks preventing the wide application of *** on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and *** start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in *** propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive ***,we analyze a case of the physical-world use of deep learning security *** addition,we discuss future directions and open issues in deep learning *** hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks.

关键词： adversarial examples deep learning frameworks defensive approaches security issues

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Cloud Forensics Analysis Framework for Secure and Efficient Data Retrieval

Cloud Forensics Analysis Framework for Secure and Efficient ...

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

作者： Revathi, V. Aishwarya, B.K. Tripathi, Manju Singh, Navdeep Alabdely, Haider Kumar, Ashwani New Horizon College of Engineering Department of Applied Sciences Bangalore India Institute of Aeronautical Engineering Hyderabad Dundigal India Ies College of Technology Department of Computer Science & Engineering M. P. Bhopal India Lovely Professional University Phagwara India The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9798350373783

This study's Cloud Forensics Analysis Framework seeks secure and effective data recovery methods. The framework's five strategies make digital evidence retrieval methodical and versatile. These include adaptive evidence collecting, secure data retrieval, machine learning-based anomaly detection, intelligent automation, and context-aware metadata analysis. Each approach targets a distinct forensic process step. This makes cloud forensics investigations seem difficult. The customizable Evidence gathering Algorithm adapts to cloud characteristics to collect evidence. This allows contextually aware and flexible digital evidence retrieval. The secure Data Retrieval Techniques Algorithm protects data privacy and chain of custody with advanced encryption and validation. The Machine Learning-Based Anomaly Detection Algorithm checks protected data for anomalies before sending it, making the system safer. Investigators may focus on more complex cases by simplifying and sorting basic forensic activities with the Intelligent Automation Algorithm. The Context-Aware Metadata study Algorithm weights and prioritizes cloud metadata. This completes metadata analysis. Compared to other approaches, the framework has greater Precision, Recall, F1 Score, Processing Time, Resource Utilization, and Compatibility Score. Charts convey system operation in a simple manner. This powerful and adaptable Cloud Forensics Analysis Framework improves cloud forensics by providing a thorough and effective solution to recover and safeguard data. The Cloud Forensics, This paper discusses context-aware metadata analysis, data retrieval, intelligent automation, machine learning-based anomaly detection, performance metrics, secure data retrieval techniques, support vector machines, and visual representation. © 2024 IEEE.

关键词： Cloud security

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Speech-to-text Transfiguration in Language Numerals for Perpetual Deaf Patients

Speech-to-text Transfiguration in Language Numerals for Perp...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： B P Pradeep Kumar Venkatesan C Suhas A R Balamurugan D Sathiyapriya V Department of Electronics and Communication Engineering HKBK College of Engineering Bangalore India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India

This paper primarily designates the isolated words transfiguration in the Kannada language based on a few techniques like Mel Frequency Cepstral Coefficients (MFCCs), Euclidean distance measure, Wavelet Transforms and Vector Quantization (VQ) technique. The contiguous match to the test speech exclamation is found by comparing the Euclidean distance value of the MFCC coefficients with that of the utterances present in the training set. The matched sampled word is detected by the display of its text equivalent message trained during the test phase. MATLAB R2019b software is employed to build the framework.

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Hybrid Threshold Speech Enhancement Scheme Using TEO And Wavelet Coefficients

Hybrid Threshold Speech Enhancement Scheme Using TEO And Wav...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Latha R Suhas A R B P Pradeep Kumar M.Mohammed Ibrahim Sathiyapriya V Department of Electronics and Communication Engineering HKBK College of Engineering Bangalore India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India

Speech Enhancement (SE) aims to improve the quality of degraded speech while maintaining its intelligibility. The Wavelet Transform (WT) has become a powerful tool of signal analysis thereby widely used in signal detection and signal denoising. In this paper, we propose an effective means of SE by a hybrid threshold scheme using WT. The proposed methodology looks into both falling the noise and preserving edges of the speech signal unlike the conventional Hybrid Threshold (HT) and Soft Threshold (ST) in the wavelet domain. The threshold value in the wavelet domain is maintained constant for all sub-bands of the signal which reduces denoising efficiency. A novel speech augmentation technique built on the wavelet onsets and time adaption of introduced by calculating wavelet coefficients of the Teager Energy. Performance analysis of speech enhancement techniques using Wavelet coefficients and Teager Energy Operator (TEO) with hybrid threshold method is done. The experiment is carried out for speech data with various values of SNR vacillating from -10 to +10 db with Additive White Gaussian Noise (AWGN).

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