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6th IEEE International Conference on Advances in Science and Technology, ICAST 2023

作者： Bharadi, Vinayak Hamdule, Zohaib Kambli, Sujan Salvi, Raj Chavan, Raj Nimbalkar, Vrishali Finolex Academy of Management and Technology Department of Information Technology Ratnagiri India Finolex Academy of Management and Technology Dept. of Computer Science and Engineering Ratnagiri India

ISBN: (纸本)9798350359817

Online signature verification remains a pivotal component in digital security, ensuring the authenticity of digital transactions and documents. This paper offers a comprehensive overview of prevailing online signature verification techniques, with a particular emphasis on methods employing Deep Learning. RNNs, with their inherent capability to process sequential data, have been instrumental in improving the accuracy and efficiency of signature verification systems. However, as the digital forgery landscape becomes increasingly sophisticated, there is a pressing need to explore advanced neural architectures. In this context, the paper introduces a transformer-based approach for signature verification. Unlike traditional models, transformers, with their self-attention mechanisms, have the potential to capture intricate signature patterns and nuances, offering a promising avenue for future research and application. © 2023 IEEE.

关键词： Recurrent neural networks

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Vertical Fragmentation using K-Medoid in Distributed Database 9

Vertical Fragmentation using K-Medoid in Distributed Databas...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Aggarwal, Mukta Bajaj, Shalini Bhaskar Jaglan, Vivek Amity University Haryana Dept. of Computer Science and Engineering Gurugram India Dpg Institute of Technology and Management Dept. of Computer Science and Engineering Gurugram India

ISBN: (纸本)9789380544441

Fragmentation and allocation are major challenges that improve the performance of distributed databases meticulously. Many algorithms are presented in the literature for horizontal and vertical fragmentation. By considering the advantages of vertical fragmentation, this paper presents the k-medoid hard clustering technique which is used for non-overlapping vertical fragmentation. Dissimilarity of each attribute with respect to the medoid is calculated and least dissimilar attributes are placed together to form fragments. Dissimilarity Count is used as a measure to check the appropriateness of the fragments formed. The proposed algorithm provides optimal results for fragmentation as compared to Enhanced Minimum Spanning Tree (EMST). © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Trees (mathematics)

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Advancing Fake News Detection: From Traditional Machine Learning to Transformer-based Approaches 3

Advancing Fake News Detection: From Traditional Machine Lear...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhinand, I.V. Ajal Krishna, K.P. Krishna, Ajay Jayadev, J. Reji Rahmath, K. Divya, T. Dept. of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavannur A. P. J. Abdul Kalam Technological University kerala Thiruvananthapuram India

ISBN: (纸本)9798331527549

Fake news on online platforms is spreading at a rapid rate, posing great threats to social, political, and economic stability in linguistically diverse regions like Kerala, India. This paper reviews comprehensively the techniques of fake news detection from traditional machine learning methods to transformer-based models. Special attention is given to the detection of fake news in regional languages like Malayalam, characterized by complex morphology and syntax. The study investigates applications of ensemble-based learning as well as state-of-the-art transformer models, BERT, and XLNet, focusing on their ability to capture nuanced linguistic features and contextual relationships. Evaluation results show how significantly better the transformer-based models fare than traditional methods with respect to accuracy and robustness. This review also captures current limitations - such as resource constraints for regional languages - as well as future opportunities in developing scalable, multilingual fake news detection systems. © 2025 IEEE.

关键词： Adversarial machine learning

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A Machine Learning Approach for Predicting Personality Types from Text Patterns Using Natural Language Processing(NLP)

A Machine Learning Approach for Predicting Personality Types...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Kinshuk Ganguly Subhajit Saha Anupam Mondal Monalisa Dey Dept. of Computer Science & Engineering Institute of Engineering & Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This study explores the use of Natural Language Processing (NLP) approach to predict Myers-Briggs Type Indicator (MBTI) personality types from Twitter texts. Initially, we approached the problem as a 16-class classification task, but encountered significant challenges due to the lack of instances representing certain class labels adequately. To address this, we decomposed the MBTI types into four dimensions and trained individual classifiers for each dimension. Leveraging the best performing classifiers, we constructed a stacking ensemble model to predict personality classes corresponding to each dimension. The results demonstrate improved accuracy compared to the initial 16-class classification approach. Additionally, the performance of ensemble model is found to be better than the individual classifiers. Thus, ensemble approach paired with the separate dimension based classification method can be used for effective prediction of personality types for psychological profiling.

关键词： Support vector machines Training Logistic regression Accuracy Social networking (online) Stacking Blogs Predictive models Natural language processing Robustness

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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment System “Stationary 3-Axis Fire-Emergency Response Base”

Designing and Implementing an IoT-Based Long-Distance Fire-E...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Shyantan Kundu Swarajit Dey Md. Mustafa Abdullha Gobinda Sen Malay Gangopadhyay Dept. of Computer Science & Engineering Institute of Engineering & Management Kolkata India Dept. of Electronics & Communication Engineering Institute of Engineering & Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named “Stationary 3-Axis Fire-Emergency Response Base” (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management.

关键词： Industries Projectiles Forestry Flame retardants Hazards Real-time systems Production facilities Time factors Reliability Internet of Things

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A Machine Learning Approach for Predicting Personality Types from Text Patterns Using Natural Language Processing(NLP) 8

A Machine Learning Approach for Predicting Personality Types...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Ganguly, Kinshuk Saha, Subhajit Mondal, Anupam Dey, Monalisa Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798331520762

关键词： Natural language processing systems

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Xception CNN-Ensemble Learning Based Facial Emotion Recognition 6

Xception CNN-Ensemble Learning Based Facial Emotion Recognit...

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6th International Conference on Computing, Communication, control and Automation, ICCUBEA 2022

作者： Shirsath, Supriya Vikhe, Vaishali Vikhe, Pratap Pravara Rural Engineering College Dept. of Computer Engineering Ahmednagar India Pravara Rural Engineering College Dept. of Instrumentation & Control Engineering Ahmednagar India

ISBN: (纸本)9781665484510

Emotions in human is an effective medium to study the mindset of a person. Since, expression on the face of human is significant approach to understand the condition and communicate with him to release the pressure or stress of person. The emotions and behavior have a strong relationship and is noteworthy in non-verbal form of communication. The advancement in medical science and use of image processing tools like Artificial Intelligence (AI) and Machine Learning (ML), can be helpful to recognize emotion, detect stress and depression level of a person. Thus, in this research work emotion recognition system is developed using Convolution Neural Networks (CNNs) with increased dept. and width. CNNs have been shown to enhance prediction accuracy. In terms of proper emotion categorization and accurate prediction, the suggested ensemble technique is effective. In this paper Xception CNN architecture is proposed for accurate facial emotions prediction along with an ensemble model, using Max Voting ensemble technique, mainly contributes in accurate classification. In proposed technique trained CNN models were loaded and for each trained model prediction probabilities were generated. Furthermore, it is then used for Max Voting to generate final emotion prediction. The CNN models are trained and evaluated on FER-2013 dataset. © 2022 IEEE.

关键词： Emotion Recognition

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Block-Hash Signature (BHS) for Transaction Validation in Smart Contracts for Security and Privacy using Blockchain

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Journal of Mobile Multimedia 2023年第4期19卷 935-962页

作者： Bhatnagar, Sonika Dayal, Mohit Singh, Deepti Upreti, Shitiz Upreti, Kamal Kumar, Jitender Dept. of Computer Science & Engineering Government Polytechnic Baheri Bareilly India Dept. of Applied Science Bharati Vidyapeeth’s College of Engineering New Delhi India Dept. of Information Technology ABES Institute of Technology Ghaziabad India Dept.of Information Technology Asian Education Group (AEG) Noida India Dept. of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Dept. of Computer Science & Engineering J.C Bose University of Science & Technology YMCA Haryana Faridabad India

Some of the well-known signature techniques like Winternitz and Lamport are not considered to be very appropriate for the usage of hashing or smart contracts in Blockchains security because of their size O(n²), which is prominently too high. Although in Blockchain, the security concern is on the top priority because of its distributed P2P design still, the security enhancement is required to sign and verify the documents forwarded to the peers, especially in Hyperledger Fabric. Here, this paper presents a new signature technique “Block-Hash” to enhance Blockchain security by using it in smart contracts as well as hashing with size 3Xn bits (n = 256, generally for SHA-256 Hashing) and which can score 112 bits security. The proposed signature can be used appropriately for signing a smart contract by the endorser or committer node. Also, it can be used with a hash algorithm in forming a Merkle tree. Apart from the description and implementation of Block-Hash Signature, this paper has covered the analysis of its security and correctness measures with a table for result comparison. © 2023 River Publishers.

关键词： blockchain security Digital signature hyperledger fabric smart contract

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Optimal control Strategy to Reduce HIV-1 Infection in the Brain

Optimal Control Strategy to Reduce HIV-1 Infection in the Br...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Sofia Baiocchi Federica Blengini Francesca Caleno Paolo Di Giamberardino Daniela Iacoviello Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

The study of Human Immunodeficiency Virus (HIV) and of its progression to Acquired Immune Deficiency Syndrome (AIDS) has been a challenge by scientists since the early 1980s; the improvements in the knowledge of its transmission and of the evolution of the disease have strongly reduced the mortality, even if up to know an effective vaccine is not available. Nevertheless, the Highly Active Antiretroviral Therapy (HAART) allows a high life expectancy. Some of the mechanisms of HIV reproduction are under investigation and recent observations are suggesting the existence of virus reservoir in the brain, the liver, the lungs and the gut. In particular, due to untreated amounts of virus, neurocognitive disorders as well as motor nerves diseases and weakness in muscles are improved in patients already weakened. In this paper it is considered a mathematical modelling to study the within-host viral dynamics, analysing the dynamics of the CD4-T cells, of the macrophages both inside and outside the brain, uninfected and infected, along with the virions carrying on the infection. An optimal control strategy is proposed and implemented,aiming at maximizing the number of healthy cells, while reducing the number of infected ones, scheduling suitably the therapy.

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Unleashing the Potential of Women in Leadership Roles within the Evolving Landscape of Machine Learning and Technology

Unleashing the Potential of Women in Leadership Roles within...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Tiwari, Madhuri Giri, Neeraj Suman, Sanjay Kumar Chandanan, Amit Kumar Peneti, Subhashini Choudhury, Shibanjali GGITS Dept of Data Science MP Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science and Engineering MP Bhopal462044 India St. Martin's Engineering College Dept. of ECE Telangana Secunderabad India Department of Computer Science and Engineering C G Bilaspur India MLR Institute of Technology Dept. of CSE Telangana Hyderabad India Techno College of Engineering Dept. of CSE Tripura Agartala India

ISBN: (纸本)9798331539948

The innovative Gender-Empowered Leadership Development (GELD) programme promotes gender equality in fast-paced technology and machine learning leadership positions. This was a US government initiative. BiasMitigator, Skillenhancer, and CultureCatalyst work together to eliminate gender prejudice, improve employee skills, and improve workplace morale. Programs collaborate to achieve these aims. CultureCatalyst promotes a pleasant environment, SkillEnhancer improves skills over time, and BiasMitigator eliminates gender prejudice across the firm. All three projects assist the organization achieve its aims. This study compares GELD to normal approaches to better understand the situation. This illustrates how intricate its procedure is. © 2024 IEEE.

关键词： BiasMitigator CultureCatalyst GenderEmpowered Leadership Development Inclusion Leadership Development Machine Learning SkillEnhancer Technology Traditional Methods Women in Leadership

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