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computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

computer Systems science & engineering 2023年第8期46卷 1811-1826页

作者： M.Uvaneshwari M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia Department of Computing Technologies School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign *** benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in *** recognition of BT is highly significant to protecting the patient’s ***,the BT can be identified through the magnetic resonance imaging(MRI)scanning *** the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the ***,ML has prevailed against standard image processing *** studies denote the superiority of machine learning(ML)techniques over standard ***,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)*** accomplish the detection of brain tumor effectively,a computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research ***,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull ***,mayfly optimization with the Kapur’s thresholding based segmentation process takes *** feature extraction proposes,local diagonal extreme patterns(LDEP)are *** last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification *** accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research *** experimental validation of the proposed model demonstrates its promising performance over other existing methods.

关键词： Brain tumor machine learning segmentation computer-aided diagnosis skull stripping

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Exploring the opportunities and challenges of ChatGPT in academia

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Discover Education 2024年第1期3卷 1-14页

作者： Islam, Iyolita Islam, Muhammad Nazrul Department of Computer Science and Engineering Bangladesh University of Professionals (BUP) Dhaka 1216 Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Dhaka 1216 Bangladesh

The Artificial Intelligence (AI) natural language model ChatGPT (Chat Generative Pre-trained Transformer), often referred to as ChatGPT-4, has a wide range of possible uses in the fields of research, business, academia, health, and similar fields. This language model can accomplish a number of academic jobs that were previously completed by people, taking a significant amount of time and effort. The purpose of the article is to investigate ChatGPT’s potential opportunities and challenges in academia. To attain this objective, a review of relevant literature and online resources (news, articles, etc.) was carried out. The noticing-collecting-thinking approach was adopted to explore and categorize all observed concerns. The outcome of this study reveals that research, education, personal skill development, and social aspects constitute the four broad perspectives that articulate the opportunities and constraints of ChatGPT in academia. For instance, from the education perspective, ChatGPT can help students have a personalized learning experience. On the other hand, it might provide false information as well as lack the ability to generate responses on its own because those responses depend on training datasets, which may contain errors. Similarly, from the point of view of the personal skill development, this model may impair a person’s capacity for critical thought and content production;while providing reading and writing practice sessions and relevant content, it can improve a person’s language proficiency. © The Author(s) 2024.

关键词： ChatGPT, GPT-3, GPT-4 academia, Challenges, Opportunities, Conceptual analysis, Artificial Intelligence, Education, Writing

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An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

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computer Modeling in engineering & sciences 2024年第5期139卷 1521-1555页

作者： Abidhan Bardhan Raushan Kumar Singh Mohammed Alatiyyah Sulaiman Abdullah Alateyah Department of Civil Engineering National Institute of Technology PatnaPatnaIndia Department of Computer Engineering and Applications GLA UniversityMathuraIndia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Science and ArtsQassim UniversityUnaizahSaudi Arabia

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented *** proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal *** EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was *** train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was *** on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive *** consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)*** outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

关键词： Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence

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A Real time Application of Virtual Reality in Indian Agriculture

A Real time Application of Virtual Reality in Indian Agricul...

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2024 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2024

作者： Aradhya, Sumalatha Navya, V. Siddaganga Institute of Technology Tumakuru Department of Computer Science and Engineering India

ISBN: (纸本)9798350364040

Agriculture is the backbone of the economic system for any country and for ages, agriculture has been related with the production of vital food crops to satisfy the needs of consumers. Farmers must meet the changing needs of end users, customers, regulators, food supply chain bodies and retailers. Despite the important role that the farmer plays in the economic growth of the country, not many initiatives are taken to face the challenges in the agriculture market. The challenges include environmental factors such as climatic changes, rising demands for food and higher quality, supply chain issues, keeping track of the crops both grown, sold, and purchased, delineation of agriculture lands, and securing the crops till it reaches the agriculture market or end user etc. There is a need to educate farmers on the importance of digitization and how farmers can benefit in the right way by using modern tools. The challenges faced by the farmers can be handled by using an easy platform where they can know the accurate prices of different crops in and near their city. Through the Virtual Reality based platform, the farmers can easily explore the weather trends of their farm locations and can visualize the agriculture market dynamically from any remote location. By combining the power of virtual reality with agricultural knowledge, one can revolutionize agriculture practices and crop marketing. The framework enables different farming techniques, equipment operation, and crop management through interactive simulations with 98.8% accuracy. © 2024 IEEE.

关键词： Crops

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Spatio Temporal Tourism Tracking System Based on Adaptive Convolutional Neural Network

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computer Systems science & engineering 2023年第6期45卷 2435-2446页

作者： L.Maria Michael Visuwasam D.Paul Raj Department of Computer Science and Engineering Rajalakshmi Institute of TechnologyChennaiTamil Nadu600104India Department of Computer Science and Engineering R.M.D.Engineering CollegeChennaiTamil Nadu601204India

Technological developments create a lot of impacts in the tourism *** big data technologies and programs generate opportunities to enhance the strategy and results for transport ***,there is a difference between technological advances and their integration into the methods of tourism *** rising popularity of Freycinet National Park led to a master plan that would not address cultural and environmental *** study addresses the gap by using a synthesized application(app)for demographic surveys and Global Navigation Satellite System(GNSS)technology to implement research *** article focuses on managing visitors within the famous Freycinet National *** comprehensive structured data were analyzed in three phases,(1)identifying groups of visitors who are more likely to use the walking trails,(2)those who are more and less likely to visit during/peak crowding times,and(3)finally creating an integrated Spatio-temporal dependency model via a machine-based learning system for real-time *** research examines innovative techniques that can offer energy resources to managers and tourism agencies,especially in detecting,measuring,and potentially relieving crowding and over-tourism.

关键词： Spatio-temporal dependency machine-based learning global navigation satellite system(GNSS)technology smartphones

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Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. Salunke, Mahendra B. Bhosle, Amol A. Sangve, Sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, Sujata R. Sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Deep learning

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Detecting Inattentive and Aggressive Driver Behavior Using Deep Learning: Recent Advances, Challenges with Performance Evaluation 4th

Detecting Inattentive and Aggressive Driver Behavior Using D...

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Debbarma, Tina Pal, Tannistha Debbarma, Nikhil Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (纸本)9789819751990

Road safety faces a grave threat from the prevalence of aggressive and inattentive driving behaviors, contributing significantly to the global rise in traffic accidents and fatalities. Identifying, tracking, and proactively addressing instances of aggressive and inattentive driving are pivotal steps toward mitigating accidents and enhancing road safety. This research project seeks to explore the potential of deep learning methods for analyzing human driving behavior, with a specific focus on identifying inattentive and aggressive driving behaviors. The ultimate goal is to develop a robust and reliable system capable of accurately recognizing and categorizing such behaviors. To achieve this objective, we harness the power of deep learning algorithm. This paper also discusses the identification and categorization of aggressive and distracted driving behaviors among human drivers, using deep learning methodologies which offer substantial promise for elevating traffic safety. The literature review uncovered several noteworthy techniques, including CNN, MTCNN, transformer-based action detection systems, and fusion models inspired by densely connected convolution networks. Comparative analysis revealed improvements in accuracy and precision through the utilization of these techniques. However, the pursuit of larger and more diverse datasets, real-time implementation capabilities, and robustness in various environmental conditions remain essential requirements. Despite these challenges, deep learning-based systems offer the potential for automatic and precise analysis of intricate driving patterns, pivotal in accident prevention and overall road safety enhancement. Future research should focus on surmounting current limitations and advancing the development of more intricate deep learning models for accurate recognition and categorization of human driving behaviors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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A Novel Graph Clustering Algorithm with Enhanced Security using Power Method and Homomorphic Encryption 1

A Novel Graph Clustering Algorithm with Enhanced Security us...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Jayalatchumy, D. Arunkumar, G. Saravanan, D. Perunthalaivar Kamarajar Institute of Engineering and Technology Department of Computer Science and Engineering Nedungadu Karaikal India Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328042

In recent years, privacy concerns have led to an increasing demand for secure data clustering algorithms that can protect sensitive data while maintaining the accuracy of the clustering results. By using homomorphic encryption to enable safe computation on encrypted data is one interesting strategy. Homomorphic encryption approach that allows addition and multiplication to be performed on encrypted data is the Paillier cryptosystem. In this study, we present an innovative approach for using the power iteration algorithm for clustering with the Paillier cryptosystem. An effective and scalable method for grouping large data sets is the power iteration clustering algorithm. We show that by using the Paillier cryptosystem, we can perform the necessary computations for the power iteration clustering algorithm on encrypted data without revealing any sensitive information. We prove our approach can achieve excellent accuracy while maintaining data privacy by evaluating it on many benchmark datasets. Our results show that the Paillier cryptosystem can be a viable solution for privacypreserving clustering applications. © 2023 IEEE.

关键词： Clustering algorithms

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Performance in Credit Risk Analysis Using Statistical and Classification Models 5th

Performance in Credit Risk Analysis Using Statistical and Cl...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Jaiswal, Yashika Sahu, Sudeep Ziaurrahman Baldua, Shreya Gonge, Sudhanshu Joshi, Rahul Kotecha, Ketan Department of Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune India

ISBN: (纸本)9789819745395

The study of credit risk is a major concern for financial companies looking to make wise lending decisions and limit potential losses. In this study, the use of R, a potent open-source programming language, is examined in the context of credit risk analysis. This study offers a thorough framework to improve the accuracy and efficiency of credit risk assessment by utilizing the flexible data manipulation, statistical analysis, and machine learning capabilities of R. The importance of credit risk analysis in financial institutions is discussed in the paper’s opening section, along with some of the difficulties it faces. The rich libraries, data processing capability, and data visualization features of R highlight how well-suited it is for this purpose. Data quality and consistency are stressed in the technique portion since it encompasses data collection, preprocessing, and feature engineering. To predict credit risk, a variety of statistical methods and machine learning models are used, which offers details on their benefits and interpretability. The research study also looks at model validation and evaluation, which ensures the stability and dependability of the credit risk models. Model accuracy, precision, and recall are evaluated using methods including exploratory data analysis (EDA), ROC analysis, and model performance indicators. This study concludes by highlighting how the use of R in credit risk analysis might enable financial institutions to make more knowledgeable lending decisions, lowering financial risks and promoting the stability of the financial sector. The purpose of the article is to offer a thorough methodology that financial institutions can use when performing credit risk analysis. This entails data gathering, preprocessing, feature engineering, statistical analysis, choosing a machine learning model, and model assessment. The goal of the paper is to provide practitioners with a detailed manual for implementing R-based data-driven credit risk an

关键词： Data accuracy

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Harnessing the Power of Ensemble Algorithms for Diabetes Prediction: A Comparative Analysis 18

Harnessing the Power of Ensemble Algorithms for Diabetes Pre...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Gupta, Maulik Choudhary, Mrinal Garg, Divisha Rana, Prashant Singh Thapar Institute of Engineering & Technology Computer Science Engineering Department Punjab Patiala India Thapar Institute of Engineering & Technology Mechanical Engineering Department Punjab Patiala India

ISBN: (纸本)9798350391725

Effective diagnosis of diabetes is crucial for managing the disease and preventing complications. This study explores the use of machine learning for diabetes prediction, focusing on the impact of data preprocessing on model performance. Using four classifiers, namely logistic regression, naive Bayes, random forest, and gradient boosting, diabetes cases are diagnosed after extensive data preprocessing, including cleaning and feature engineering. Results show that the ensemble methods, random forest, and gradient boosting achieve high accuracy and robustness in diabetes prediction, especially with preprocessing. These findings underscore the importance of model selection and preprocessing in healthcare machine learning. © 2024 IEEE.

关键词： Prediction models

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