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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Santhosh, Sethulakshmi Meenakshi, M. Avani, F. Abhishek, S. Anjali, T. Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350340600

In order to predict Myers-Briggs personality types from text input, this research article compares the abilities of Stochastic Gradient Descent (SGD), Naive Bayes, k-Nearest Neighbours (KNN), and Logistic Regression models. The Myers-Briggs Type Indicator(MBTI) captures distinctive patterns of behaviour, cognition, and preferences of human personality. It divides people into one of sixteen personality types. This study assesses the accuracy, support, recall, precision, and F1-score for the chosen models using a dataset made up of text inputs and matching Myers-Briggs types. The project attempts to determine the most successful model among the evaluated ways for precisely predicting Myers-Briggs personality types through extensive experimentation and review. The findings of this study have applications in building accurate and reliable models for personality prediction utilizing natural language processing methods. © 2023 IEEE.

关键词： Stochastic systems

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Metaheuristic Secure Clustering Scheme for Energy Harvesting Wireless Sensor Networks

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computer Systems science & engineering 2023年第4期45卷 497-512页

作者： S.Nithya Roopa P.Anandababu Sibi Amaran Rajesh Verma Department of Computer Science and Engineering Kumaraguru College of TechnologyCoimbatore641049India Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of Computing Technologies SRM Institute of Science and TechnologyChennai603203India Department of Electrical Engineering King Khalid UniversityAbha61411Kingdom of Saudi Arabia

Recently,energy harvesting wireless sensor networks(EHWSN)have increased significant attention among research *** harvesting energy from the neighboring environment,the sensors in EHWSN resolve the energy constraint problem and offers lengthened network *** is one of the proficient ways for accomplishing even improved lifetime in *** clustering process intends to appropriately elect the cluster heads(CHs)and construct *** several models are available in the literature,it is still needed to accomplish energy efficiency and security in *** this view,this study develops a novel Chaotic Rider Optimization Based Clustering Protocol for Secure Energy Harvesting Wireless Sensor Networks(CROC-SEHWSN)*** presented CROC-SEHWSN model aims to accomplish energy efficiency by clustering the node in *** CROC-SEHWSN model is based on the integration of chaotic concepts with traditional rider optimization(RO)***,the CROC-SEHWSN model derives a fitness function(FF)involving seven distinct parameters connected to *** accomplish security,trust factor and link quality metrics are considered in the *** design of RO algorithm for secure clustering process shows the novelty of the *** order to demonstrate the enhanced performance of the CROC-SEHWSN approach,a wide range of simulations are carried out and the outcomes are inspected in distinct *** experimental outcome demonstrated the superior performance of the CROC-SEHWSN technique on the recent approaches with maximum network lifetime of 387.40 and 393.30 s under two scenarios.

关键词： Clustering wireless sensor networks network lifetime energy efficiency metaheuristics energy harvesting rider optimization

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Health and Environment Monitoring System for Viral Respiratory Diseases 14

Health and Environment Monitoring System for Viral Respirato...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Shwetha, S. Nithuna, N. Oviya, B. Swetha, J. Harini, N. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335095

The proposed system in this work is a contactless health and environment monitoring system designed after conducting a literature review on the most recent research on viral respiratory diseases and their impact on businesses, risk factors of a location, and surveillance of people for various safety measures and causes leading to its spread. The proposed system takes the form of an Android Mobile Application, designed to function as a safety entry checkpoint for various organizations, including businesses and educational institutions. It aims to detect viral respiratory diseases by leveraging the capabilities of a smartphone camera without requiring any additional external hardware specifications. Furthermore, the system incorporates users' vaccination information, enabling a comprehensive assessment of health and safety. The key algorithms presented in this work focus on measuring critical body vital signs, including heart rate, temperature, and oxygen saturation (Oxygen saturation (SpO2)) levels. By utilizing advanced techniques such as mask detection and social distance monitoring, the system ensures the recognition of disease indicators and triggers appropriate actions to minimize the risk of transmission. To assess the safety of an area or environment, the proposed system employs the Euclidean distance metric and MobileNetV2, a state-of-the-art deep learning model. This combination enables real-time evaluation and analysis of the surroundings, providing crucial information for making informed decisions regarding the level of safety in a given location. © 2023 IEEE.

关键词： Python

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Towards a lightweight security framework using blockchain and machine learning

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Blockchain(Research and Applications) 2024年第1期5卷 107-118页

作者： Shereen Ismail Muhammad Nouman Diana W.Dawoud Hassan Reza School of Electrical Engineering and Computer Science University of North DakotaND 58202USA Department of Computer Science COMSATS University IslamabadIslamabad 44000Pakistan College of Engineering and Information Technology University of DubaiDubaiUnited Arab Emirates

Cyber-attacks pose a significant challenge to the security of Internet of Things(IoT)sensor networks,necessitating the development of robust countermeasures tailored to their unique characteristics and *** prevention and detection techniques have been proposed to mitigate these *** this paper,we propose an integrated security framework using blockchain and Machine Learning(ML)to protect IoT sensor *** framework consists of two modules:a blockchain prevention module and an ML detection *** blockchain prevention module has two lightweight mechanisms:identity management and trust *** management employs a lightweight Smart Contract(SC)to manage node registration and authentication,ensuring that unauthorized entities are prohibited from engaging in any tasks,while trust management uses a lightweight SC that is responsible for maintaining trust and credibility between sensor nodes throughout the network’s lifetime and tracking historical node *** and transaction validation are achieved through a Verifiable Byzantine Fault Tolerance(VBFT)mechanism to ensure network reliability and *** ML detection module utilizes the Light Gradient Boosting Machine(LightGBM)algorithm to classify malicious nodes and notify the blockchain network if it must make decisions to mitigate their *** investigate the performance of several off-the-shelf ML algorithms,including Logistic Regression,Complement Naive Bayes,Nearest Centroid,and Stacking,using the WSN-DS *** is selected following a detailed comparative analysis conducted using accuracy,precision,recall,F1-score,processing time,training time,prediction time,computational complexity,and Matthews Correlation Coefficient(MCC)evaluation metrics.

关键词： Blockchain Machine learning IoT Security Integration Smart contracts

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Deep learning based approaches for accurate automated nuclei segmentation of Pap smear images 14

Deep learning based approaches for accurate automated nuclei...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Hridya Nair, S. Nair, Kasthuri A. S. Padmanabhan, Niharika Ratnakaran, Riya Vinitha Panicker, J. Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350335095

Cervical cancer is a type of cancer that occurs among women. The cancerous cells are developed in the cells of the cervix which is the lower part of the uterus. The human papillomavirus (HPV) is responsible for the vast majority of cervical cancer cases. A Papanicolaou test (pap smear test) is the most reliable screening method for identifying cancerous cells. Pathologists visually examine the slide after performing the test to look for malignancy. But this is a time-consuming and challenging task, often limited by the availability of certified professionals. Additionally, the diagnostic efficacy of visual screening is compromised by the increased workload of pathologists, leading to decreased diagnostic accuracy. Thus, a computer-aided diagnosis system is essential to enhance the quality of the Pap test results and enable early diagnosis. Nuclei segmentation is a crucial step in automated cervical cancer detection, as the nucleus carries essential information about the alterations occurring during the disease's progression. Thus, automated segmentation of nuclei can significantly and ultimately influence the diagnostic results. Recent surveys found that neural networks outperformed various traditional segmentation methods. We conducted experiments on the Herlev dataset using U-Net with ResNet and EfficientNet encoders, PAN (Pyramid Attention Network), and LinkNet with a ResNet encoder. For each of these models, our results showed IOU scores of 0.76, 0.938, 0.79, and 0.69, respectively. © 2023 IEEE.

关键词： Diseases

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Semantic Analysis Using Deep Learning for Predicting Stock Trends 2

Semantic Analysis Using Deep Learning for Predicting Stock T...

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

作者： Galphade, Manisha Nikam, V.B. Yedurkar, Dhanalekshmi Singh, Prabhishek Stephan, Thompson Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Recursive Descent Parser for Abstract Syntax Tree Visualization of Mathematical Expressions 7

Recursive Descent Parser for Abstract Syntax Tree Visualizat...

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Pecheti, Shiva Teja Basavadeepthi, M.H. Kodurupaka, Nithin Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350343144

This study presents a novel method for finding and evaluating mathematical expressions using Abstract Syntax Trees (AST). The approach is particularly adaptable for both academic and professional applications since it accurately parses complex equations and accommodates a large number of mathematical operations. The AST model ensures a hierarchical expression structure, enabling accurate and efficient evaluation. Parsed expressions are improved by incorporating interactive graph vi-sualisation, which simplifies mathematical computations and engineering tasks for understanding and application. The project's complete approach to detecting mathematical expressions stands out since it takes into account a wide range of functions and variables and ultimately enhances the system's accuracy and adaptability. The visual representation of AST also helps with debugging and checking the accuracy of parsed expressions, which adds to its significance in the field of mathematical expression recognition. © 2023 IEEE.

关键词： Syntactics

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends

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SN computer science 2024年第6期5卷 717页

作者： Valizadeh, Alireza Amirhosseini, Mohammad Hossein NEX Power Ltd Department of Computer Science and Digital Technologies School of Architecture Computing and Engineering University of East London

Machine Learning has garnered significant attention in lithium-ion battery research for its potential to revolutionize various aspects of the field. This paper explores the practical applications, challenges, and emerging trends of employing Machine Learning in lithium-ion battery research. Delves into specific Machine Learning techniques and their relevance, offering insights into their transformative potential. The applications of Machine Learning in lithium-ion-battery design, manufacturing, service, and end-of-life are discussed. The challenges including data availability, data preprocessing and cleaning challenges, limited sample size, computational complexity, model generalization, black-box nature of Machine Learning models, scalability of the algorithms for large datasets, data bias, and interdisciplinary nature and their mitigations are also discussed. Accordingly, by discussing the future trends, it provides valuable insights for researchers in this field. For example, a future trend is to address the challenge of small datasets by techniques such as Transfer Learning and N-shot Learning. This paper not only contributes to our understanding of Machine Learning applications but also empowers professionals in this field to harness its capabilities effectively. © The Author(s) 2024.

关键词： Artificial intelligence Data-driven approach Lithium-ion battery Machine learning

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Block chain-based decentralized public auditing for cloud storage with improved EIGAMAL encryption model

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International Journal of Information Technology (Singapore) 2024年第2期16卷 697-711页

作者： Kumar, R. Prasanna Bandanadam, Showri Rayalu Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

With the growing relevance of data integrity, public auditing mechanisms for cloud storage models are intensively researched. In public auditing systems, a third person is used to ensure the outsourced data’s integrity on the user’s behalf. A lot of public verification models regarding blockchain are developed to combat cruel trusted Third Public Auditors (TPAs). Conventional models for auditing are based on TPA and are prone to an auditor enticing who colludes with the hostile block-chain to provide prejudiced results for auditing. This research work plans to propose a blockchain-based public auditing scheme to verify data integrity in cloud storage. The first people upload the data to the cloud and retrieve the data when they need it. The data must be encrypted and private among the second people and DO, since both DO and the second person do not believe each other. Therefore, the DO encrypts his/her data using the Improved EI-GAMAL (IEI-GAMAL) encryption algorithm and uploads the encrypted data to the Cloud Service Provider (CSP) via the blocks of the blockchain. For preserving the privacy of the data, the key for data encryption as well as decryption is generated using the new hybrid optimization model. The DO selects one TPA to audit the data on behalf of the DO. Generally, a TPA concentrates the data of numerous DOs and offers the audit for DOs. The DO requests a public audition to generate proof of the encrypted block stored in the blockchain when he requires verifying the data present in cloud storage. In order to provide strong updates and lessen the computation expenses, the new hybrid optimization algorithm is used. The new hybrid algorithm is the theoretical unify of the Adaptive Position-based Arithmetic with the Sunflower Optimization Algorithm (APA-SFOA). Several investigations are executed to prove the supremacy of the developed model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2

关键词： Adaptive position-based arithmetic with sunflower optimization algorithm Blockchain technology Improved EI-GAMAL Public auditing for cloud storage

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