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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Nayim, Meshal Mohan, Vishnu Pandey, Trilok Nath Dash, Bibhuti Bhusan Dash, Bibek Bikram Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to be University School of Computer Applications Bhubaneswar India HealthWorksAI Bangalore India

ISBN: (纸本)9798350360660

This research explores the challenge of distinguishing between authentic and AI-generated images using advanced convolutional neural network (CNN) architectures. Leveraging the CIFAKE dataset, which includes a diverse array of AI-generated and real images, we evaluated the performance of three pre-trained models: ResNet50, EfficientNetB0, and DenseNet121. Our study involved training these models with images of 32x32 and resized 64x64 pixel resolutions to assess their classification accuracy, precision, recall, and computational efficiency. DenseNet121 emerged as the most effective model, achieving an accuracy of 98.49% on the test dataset, significantly outperforming the other models. This work highlights the potential of deep learning methodologies in addressing the growing need for reliable AI image detection, providing a robust foundation for future advancements and applications in this critical field. © 2024 IEEE.

关键词： Convolutional neural networks

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An Improved KNN Algorithm to Identify Parkinson's Disease on Voice Ailment

An Improved KNN Algorithm to Identify Parkinson's Disease on...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sukanya, N.S. Shinty, P.K. New Horizon College Of Engineering Department Of Master Of Computer Applications Karnataka Bengaluru India School Of Science And Computer Studies Cmr University Karnataka Bengaluru India

ISBN: (纸本)9798350371567

In recent times, researchers have extended their research on Parkinson's Disease (PD) using Artificial Neural Network techniques. PD is an age related progressive neurological ailment that affects millions of older people worldwide. There is a need to identify PD accurately as early as possible. Identifying PD at an early stage is a challenging procedure. This paper aims to use machine learning models such as Artificial Neural Networks (ANN) and an enhanced k-Nearest Neighbour (kNN) techniques to identify PD at an early stage. To detect the PD, an audio data of older people is used in this study. An automatic process classifies an audio data into two categories such as PD patient and healthful individual. The proposed model uses the weights and the nearest neighbourhood concepts to predict the audio samples. The results of the improved kNN classifier produces optimal results by classifying the audio datasets into healthy and unhealthy categories. © 2024 IEEE.

关键词： Diagnosis

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

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computer Systems science & engineering 2023年第6期45卷 2351-2362页

作者： S.Aanjanadevi S.Aanjankumar K.R.Ramela V.Palanisamy Department of Computer Applications Alagappa UniversityKaraikudi630003TamilnaduIndia Department of Computer Science and Engineering Sri Raaja Raajan College of Engineering and TechnologyAmaravathipudur630301TamilnaduIndia Department of Electrical and Electronics Engineering Ultra College of Engineering and TechnologyMadurai625020TamilnaduIndia

Due to the enormous usage of the internet for transmission of data over a network,security and authenticity become major *** challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database *** describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical(Face,fingerprint,Ear etc.)and behavioural(Gesture,Voice,tying etc.)by means of matching and verification *** this work,biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential *** proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary *** Face Attribute Convolutional Neural Network(FACNN)is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further *** Artificial Intelligence(AI)into the proposed system,automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while *** codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and *** main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify *** system enhances the system performance by 8%matching and verification and minimizes the False Acceptance Rate(FAR),False Rejection Rate(FRR)and Equal Error Rate(EER)by 6 times and increases the data privacy through the biometric cryptosystem by 98.2%while compared to other work.

关键词： FACNN biometric cryptography AI FAR FRR EER

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BiT5: A Bidirectional NLP Approach for Advanced Vulnerability Detection in Codebase 5

BiT5: A Bidirectional NLP Approach for Advanced Vulnerabilit...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Prabith, G.S. Rohit Narayanan, M. Arya, A. Aneesh Nadh, R. Binu, P.K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

In this research paper, a detailed investigation presents the utilization of the BiT5 Bidirectional NLP model for detecting vulnerabilities within codebases. The study addresses the pressing need for techniques enhancing software security by effectively identifying vulnerabilities. Methodologically, the paper introduces BiT5, specifically designed for code analysis and vulnerability detection, encompassing dataset collection, preprocessing steps, and model fine-tuning. The key findings underscore BiT5's efficacy in pinpointing vulnerabilities within code snippets, notably reducing both false positives and false negatives. This research contributes by offering a methodology for leveraging BiT5 in vulnerability detection, thus significantly bolstering software security and mitigating risks associated with code vulnerabilities. © 2024 The Authors. Published by Elsevier B.V.

关键词： Natural language processing systems

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Insightful Clinical Assistance for Anemia Prediction with Data Analysis and Explainable AI 5

Insightful Clinical Assistance for Anemia Prediction with Da...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Kasthuri, E. Subbulakshmi, S. Sreedharan, Rajasree Department of Computer Science and Engineering Department of Computer Science and Applications Amrita School of Computing Amritapuri India Division of Nephrology Department of Pediatrics Medical College of Wisconsin MilwaukeeWI United States

Anemia among young people has positive correlation with susceptibility and impede cognitive *** per WHO 2019 statistics global prevalence rate of anemia is 33 percent. This work aims to predict anemia with inferences to identify the root cause of anemia. Prediction is done with machine learning algorithms with proper pre-processing, data normalization, and class imbalance approaches. Explainable AI, a transparent model, is used to enhance the prediction which enables to draw inferences. LGM Boost learning model prediction has a 91% accuracy and explainable AI framework predictions of most contributing attribute is accurate when compared to other models. This approach helps healthcare professionals make informed decisions on the most optimal treatment for anemia. © 2024 The Authors. Published by Elsevier B.V.

关键词： Forecasting

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Surveying Keyword Extractors: Classification, applications, and Empirical Analysis 6

Surveying Keyword Extractors: Classification, Applications, ...

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Musunuru, Varun Nagam, Sai Pavan Krishna Thushara, M.G. Villuri, Praneeth Vasantha, Gopikrishna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350369748

Natural Language Processing (NLP) has seen some rapid developments. One of the areas is keyword Extraction which has seen rapid growth. Many varied and complex extractors have been brought to light recently, and several extractors have been upgraded. These can be used in several NLP applications. The analysis and working of some of the newest and benchmark extractors that shed new light in this field have been discussed. The goodness of any extractor can only be measured by how it works on real-life data. Instead of going down the set route, a detour was taken, and the working of keyword extractors on movie plots was observed and discussed. Thus for the analysis part, a new dataset has been created from several movie plots taken from various credible sources like IMDB, Rotten Tomatoes, etc. © 2024 IEEE.

关键词： Natural language processing systems

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Hybrid Actor-Action Relation Extraction: A MachineLearning Approach 5

Hybrid Actor-Action Relation Extraction: A MachineLearning A...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Nair, Reshma P. Thushara, M.G. Department of Computer Science and Engineering Amrita Vishwa Vidhyapeetham Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita Vishwa Vidhyapeetham Amrita School of Computing Amritapuri India

Software architects and developers face challenges while trying to explain the complex functional relationships between actors and systems in the process of system design. The field of natural language processing (NLP) and information extraction is always growing. One of the biggest problems that comes up time and time again is finding relationships between entities and actions in textual data. Actor-action relation extraction (AARE) is a hybrid model that combines NLP with rule-based and machine-learning models. It effectively analyzes unstructured data and takes into account different contextual factors, saving time and reducing errors. The proposed approach extracts Actors, actions, entities, and relationships (AARE) from natural language text more accurately and completely using machine learning techniques and rule-based systems. The hybrid model handles unstructured input and adapts to changing linguistic signals using machine learning. The hybrid approach uses Named Entity Recognition, rule-based extraction, and machine learning principles to convert unstructured data into structured format. It uses tokenization, part-of-speech tagging, nlp, and semantic role labeling for relationship categorization. The model has a 93% accuracy rate and is effective in extracting actor-action relations. Future research should focus on improving rule-based techniques, semantic learning, and addressing complexities in UML diagrams. © 2024 The Authors. Published by Elsevier B.V.

关键词： Machine learning

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Enhancing Productivity and User Experience with Advanced Notepad: A Comprehensive Study 10

Enhancing Productivity and User Experience with Advanced Not...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Solanki, Chetna Bana, Garima Singh, Royal Goyal, Mayank Kumar Sharan, Bhagwati Gupta, Raj School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India School of Engineering and Sciences Srm University-AP Department of Computer Science and Engineering Amaravati India

ISBN: (纸本)9789380544472

The current work outlines the creation of a system to facilitate collaborative software design. Based on the observation of system use during real-world design sessions, an iterative development strategy has been employed. As a result, we can fix interface flaws and gain additional knowledge about collaborative design. The proposed work is based on developing an advanced Notepad using Python and Tkinter Library to enhance productivity and user experience. It will provide the user with multiple themes and Fonts. The work is experimented using Visual Studio Code. The outcomes provide better quality of user experience. Additionally, we observe how conceptions change over time, frequently resulting in quick revisions that change the structure. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Sentiment analysis

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AST-Based and Token-Based Neural Networks for Source Code Classification: A Comparative Performance Analysis 15

AST-Based and Token-Based Neural Networks for Source Code Cl...

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

作者： Parvathy, R. Thushara, Mg Amrita School of Computing Department of Computer Science & Engineering Amrita vishwa Vidyapeetham Amritapuri India Amrita School of Computing Department of Computer Science & Applications Amrita vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Programming languages function as a medium for expressing instructions in the context of computer program creation. A prevalent convention in programming is categorizing source code into fragments, a strategy that enhances understanding and simplifies the upkeep process. This research study presents a comparative examination of two distinct models utilized to classify source code. The first model employs a neural network approach that heavily relies on Syntax Trees (ASTs). In contrast, the second model is built on a neural network framework that utilizes the tokenization process. The AST-based model employs a methodology that entails decomposing extensive ASTs into smaller clusters of statement trees. These clusters are subsequently transformed into vectors by capturing the lexical and syntactical characteristics of the statements. The naturalness of certain statements is captured by employing a bidirectional Recurrent Neural Network (RNN) model, which utilizes a collection of statement vectors. In contrast, the token-based paradigm diverges from ASTs and emphasizes tokens obtained through tokenization. The tokens undergo preprocessing and vectorization before inputting into a bidirectional RNN model to evaluate the naturalness of the statements. To choose the optimal model, the accuracy of the two models are compared. © 2024 IEEE.

关键词： Recurrent neural networks

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