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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Deepika, G. Vinothiyalakshmi, P. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Chennai India

ISBN: (纸本)9783031736162

Parkinson’s Disease (PD) is a neuro-degenerative disorder that affects the motor skills of a person when the production of dopamine is reduced and eventually motor dysfunction. Dopamine is a chemical produced by brain cells, which is responsible for controlling movements. Thus, if Parkinson’s disease is detected earlier, then the progression and the effects of its symptoms can be controlled. The proposed Parkinson’s Disease Detection (PDD) model used for the detection of Parkinson’s Disease using various Machine Learning (ML) algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Voting Classifier, and Random Forest (RF). The algorithms are applied on balanced data scales and on features selected using LDA and PCA from the speech recording dataset. On experimental analysis, the SVM algorithm is found to be more efficient on the balanced dataset and PCA extracted features, giving an accuracy of 96.73% and 95.42% respectively as compared to the other classification algorithms. While the Random Forest algorithm gives the best results on LDA applied dataset giving an accuracy of 95.85%. © IFIP International Federation for Information Processing 2025.

关键词： Random forests

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Credit card fraud detection using state-of-the-Art AI methods 4th

Credit card fraud detection using state-of-the-Art AI method...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Piyush Gagandeep Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Credit card fraud has become a major problem in the financial industry, causing huge financial losses for financial institutions and cardholders. Because internet banking and e-commerce are being used more often there have been an exponential rise in the online transaction and similarly in the online fraud happening nowadays. As fraud procedures become more complex, it is clear that legitimate fraud is not sufficient to detect fraud. As a result, machine learning (ML) algorithms are increasingly used for fraud detection due to their ability to adapt to and learn from patterns in data. Various machine learning methods to detect and prevent credit card fraud. This study examines different learning techniques such as logistic regression, decision trees, random forests, support vector machines (SVM), neural networks, and ensemble methods, highlighting their advantages and disadvantages in statistical analysis. Machine learning offers effective solutions for credit card fraud detection, and a comprehensive approach that considers algorithmic robustness, interpretability, and ethics is essential. Future research directions aim to improve the accuracy and efficiency of machine learning-based fraud detection systems while ensuring fairness and transparency. © 2025 the Author(s).

关键词： Logistic regression

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A Multi-Model Intelligent Approach for Rumor Detection in Social Networks

A Multi-Model Intelligent Approach for Rumor Detection in So...

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2022 International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022

作者： Santhosh, Nikita Mariam Cheriyan, Jo Nair, Lekshmi S Saintgits College of Engineering Department of Computer Science and Engineering Kerala India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (数字)9781665468831

ISBN: (纸本)9781665468831

The impact of social media on public opinion has far-reaching repercussions across society. Even though social media give outlets to share news and opinions, the sheer number of posts on Twitter and Facebook makes it challenging to maintain quality control. These platforms have many users and offer various services, such as content creation and distribution. Not all information disseminated via the internet is accurate and reliable. Many people try to spread false and misleading information to influence public opinion. This paper reviews different algorithms for rumor identification, particularly fake news detection. It also proposes detection and classifications of fake news and its corresponding classification. Misinformation, commonly known as rumors, can cause serious harm due to unverified information. Despite their widespread use, the uncontrollable nature of social media platforms frequently results in the spread of rumors. One of the most sought after study areas in social media analytics is automatically recognizing rumors from tweets and posts. © 2022 IEEE.

关键词： Social aspects

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Detection of Corn Leaf Infection Using CNN with Various Optimizers 2

Detection of Corn Leaf Infection Using CNN with Various Opti...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sreekanth, G.R. Latha, R.S. Roja, R. Sankarnanth, S. Gayathri, A. Kongu Engineering College Department of Computer Science and Engineering Erode India Kongu Engineering College Department of Computer Technology Erode India

ISBN: (纸本)9798350349221

Corn, a grain categorized within the grass family, stands as a fundamental staple crop globally. It plays a crucial role in supplying sustenance for both humans and livestock, in addition to serving as a raw material for various industries. In 2017, Africa produced 7.4% of the 1135 million tons produced worldwide in 40 million hectares. Throughout its growing process, corn plant is impacted by numerous illnesses. Lack of early detection of its diseases may result in a loss in productivity and even crop failure. It is proposed to analyze the leaves of the corn plant for identifying the diseases by exploiting deep learning methods. A wide range of deep learning algorithms are being utilized to detect the corn leaf with infection with good range of accuracy. To categorize the leaf, image processing models are mostly used for these kind of disease detections. Since it has in-built convolutional layer, CNN can reduce the image with high dimensionality without losing any kind of information. In this proposed work CNN with Adam optimizer has been implemented for better results and achieved 91.68 % of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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A study on the role of deep learning in the detection and diagnosis of epileptic seizures 4th

A study on the role of deep learning in the detection and di...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Chauhan, Riya Kaur, Sukhpreet Saini, Dapinty Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

In today’s world, epileptic seizures which is a chronic neurological disorder causes rapid disruptions to a person’s lifestyle. It is very common and the number of cases that involve patients that remain uncurable even after medication or surgery exceed than 30%. Predicting a seizure before it happens, however, can aid in preventing it. Screening techniques have been put forth to identify epileptic seizures utilizing magnetic resonance imaging (MRI) and electroencephalography (EEG) modalities. Deep learning (DL) is one of the many subfields that make up artificial intelligence. An EEG is one of the primary methods used to diagnose epilepsy. Typically, a human specialist uses the multi-channel electroencephalogram to identify distinct patterns that indicate the presence of epileptic activity. Many attempts are made to automate this activity using both conventional and Deep Learning (DL) techniques because it is challenging and time-consuming. © 2025 the Author(s).

关键词： Electroencephalography

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An Explainable AI for Prostate Cancer Analysis 5th

An Explainable AI for Prostate Cancer Analysis

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Kumar, M. Rakesh Athish, B.S. Devi, A. Chandini Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College TN Chennai India

ISBN: (纸本)9789819771776

Prostate cancer comes under SDG 3—Good Health and Well-Being is a global health concern affecting millions of men, demanding precise analysis for early detection and effective treatment. This project pioneers, innovative techniques for prostate cancer analysis, utilizing AI-driven segmentation and advanced analytical methods to address this pressing concern. Prostate cancer poses a significant worldwide health challenge, potentially metastasizing to neighboring organs and bones, leading to pain and fractures. The primary aim of this project is to enhance the precision and efficiency of prostate cancer analysis for early detection and timely intervention. Approximately one in eight men receive a prostate cancer diagnosis during their lifetime, with a higher prevalence among older individuals and non-Hispanic black men, particularly those aged 65 and older. Awareness levels vary, with 47.3% of survey respondents aware of the condition, primarily through media outlets (21.2%) and healthcare professionals (9%). This project addresses varying prostate cancer rates in distinct regions (peripheral zone: 70–80%, transitional zone: 10–20%, central zone: 5% or less). It employs AI-driven segmentation, image processing, and machine learning for accurate tumor detection, classification, and risk assessment. An innovative aspect is developing an explainable AI system for transparent and expert-empowered analysis. The project uses AI to diagnose prostate cancer accurately and early. It combines AI-driven segmentation, advanced analytical methodologies, refined algorithms, and a user-friendly interface. The aim is to enhance patient care through accurate and interpretable prostate cancer analyses in underserved areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Deep Learning based Approach for Cassava Leaf Disease Detection 8

Deep Learning based Approach for Cassava Leaf Disease Detect...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Kalpana, T. Thamilselvan, R. Chitra, K. Kaviya, S. Kiruthiga, M. Mahesh, C. Kongu Engineering College Department of Computer Applications Erode India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350367904

Cassava is a vital staple crop in numerous tropical countries, to enhance plant disease detection using deep learning architecture including ResNet, EfficientNet, Incetion V3 and simple CNN. By levering these advance model significant improve accuracy compared to previous methods. This approach addresses the pressing issue of food insecurity by efficiently identifying diseases in crops. Types of Cassava disease are Cassava Mosaic Disease (CMD), Cassava Green Mottle (CGM), Cassava Brown Streak Disease (CBSD), and Cassava Bacterial Blight (CBB), along with the presence of Whitefly. The proposed work uses Convolution Neural Network (CNN) to identify the leaf diseases to quickly identify the specific disease that is affecting the crop. CNN can automatically detect important features without any human supervision. A vast collection of labelled cassava leaf photos comprising multiple disease classes is utilized for training the CNN model. To advance deep learning architectures, our research integrates techniques for data augmentation and transfer learning to further enchance model performance. © 2024 IEEE.

关键词： Plant diseases

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Lightweight Deep Learning Models for Robust Hand Gesture Recognition 5th

Lightweight Deep Learning Models for Robust Hand Gesture Rec...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Nisha Sonu Narayan, Satya Gajrani, Jyoti Department of Computer Science and Engineering Engineering College Ajmer Rajasthan Ajmer India

ISBN: (纸本)9789819761050

Hand gesture recognition is a burgeoning area of study with wide-ranging applications, including human–computer interaction, sign language translation, and virtual reality control. In this work, we introduce an innovative lightweight DL model designed for robust HGR and provide an in-depth analysis of its classification performance. We meticulously evaluate key performance metrics such as precision, detection rate, Jaccard index, and false positive rate and compare these results with existing methodologies. Our proposed model stands out by surpassing the performance of current models. It exhibits higher accuracy rates during both training and validation, a substantially improved detection rate, and an almost perfect Jaccard index. Most impressively, our model achieves a remarkable false positive rate of zero, showcasing its exceptional reliability in distinguishing between positive and negative instances. These findings underscore the effectiveness of our approach in addressing challenges related to overlapping gestures, unbalanced class distributions, and precise HGR. In our conclusion, we identify areas for potential enhancement and further research. To clarify the novelty and research gap of our proposed model, we emphasise its lightweight nature and articulate how it differs from existing methods. This distinction lies in its capacity to deliver accurate and efficient HGR while imposing minimal computational burden, setting it apart from more resource-intensive approaches. In summary, our study and potential to revolutionise practical applications that demand dependable and effective hand gesture recognition systems, bridging existing gaps in the area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Palmprint recognition

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Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

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computer Systems science & engineering 2023年第1期44卷 501-517页

作者： K.Nagalakshmi Dr.S.Suriya Department of Computer Science and Engineering Sethu Institute of TechnologyVirudhunagar626115Tamil NaduIndia Department of Computer Science and Engineering PSG College of TechnologyCoimbatore641004Tamil NaduIndia

Breast cancer is one of the deadly diseases prevailing in *** detection and diagnosis might prevent the death *** diagnosis of breast cancer remains a significant challenge,and early diagnosis is essential to avoid the most severe manifestations of the *** existing systems have computational complexity and classification accuracy problems over various breast cancer *** order to overcome the above-mentioned issues,this work introduces an efficient classification and segmentation ***,there is a requirement for developing a fully automatic methodology for screening the cancer *** paper develops a fully automated method for breast cancer detection and segmenta-tion utilizing Adaptive Neuro Fuzzy Inference System(ANFIS)classification *** proposed technique comprises preprocessing,feature extraction,classifications,and segmentation ***,the wavelet-based enhancement method has been employed as the preprocessing *** texture and statistical features have been extracted from the enhanced ***,the ANFIS classification algorithm is used to classify the mammogram image into normal,benign,and malignant ***,morphological processing is performed on malignant mam-mogram images to segment cancer *** analysis and comparisons are made with conventional *** experimental result proves that the pro-posed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.

关键词： Breast cancer detection segmentation classification malignant

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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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