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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Sahu, Yatendra Bhargava, Arpita Thakur, Ghanshyam Singh Jain, Rishi IIIT Department of Computer Science and Engineering Bhopal India MANIT Department of Computer Application Bhopal India

ISBN: (纸本)9798350391565

The apple industry faces significant economic losses due to diseases and pests, contributing to low productivity levels. Detecting apple diseases promptly is essential for controlling their spread and improving overall productivity. However, early identification and diagnosis of these diseases are challenging due to various factors. In this work, we evaluated the performance of three distinct models: a custom CNN, Xception, and Resnet152V2. These models were tested on an expert-annotated dataset of apple diseases, comprising approximately 1821 high-quality RGB images across different categories of following types: C1: scab affected apple, C2: cedar apple affected by rust, C3: diseases of complex nature, and C4 samples of healthy apples. The custom CNN based proposed model trained for 50 epochs could yield an approximate accuracy of around 95%. © 2024 IEEE.

关键词： Plant diseases

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An Analytical Survey on Credit Scoring Mechanism in India 2nd

An Analytical Survey on Credit Scoring Mechanism in India

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Bhowmik, Ankan Basu, Arkajyoti Basu, Arittra Paul, Anish Roy, Atrri Department of Computer Application and Science Institute of Engineering and Management Kolkata India

ISBN: (纸本)9789819735938

Credit score is an important indicator to lenders of your capability to repay loans. A consumer's creditworthiness is indicated by a number between 300 and 850 called a credit score. The advanced the score, the better a borrower looks to implicit lenders. It is predicated on data gathered from lenders and is consolidated in the Credit Information Report or CIR. This paper reviews to check one’s report regularly, also helps you to identify any disagreement that may be lowered your score, and helps to recover the lower score to advanced situations score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Credit ratings Credit score Loan Net profit

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Skin Lesion Segmentation Using Deep Learning 2nd

Skin Lesion Segmentation Using Deep Learning

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Sarkar, Kaustav Haralalka, Varun Shaw, Vishal Singh, Rishav Raj Jaiswal, Rayirth Pramanick, Tridip Department of Computer Application and Science Institute of Engineering and Management Kolkata India

ISBN: (纸本)9789819735938

Dermoscopic image analysis has gained significant importance for dermatological applications due to its potential in eliminating observer bias. Segmenting lesion areas automatically in these images is crucial for expedited disease diagnosis. In this study, an innovative deep learning-based approach for precise lesion delineation has been proposed. Leveraging a deep convolutional neural network, where input images are included after appropriate preprocessing. Pedro Hispano Hospital (PH2) 2013 dataset has been employed, comprising dermoscopic images and lesion binary masks as ground truth for evaluation. The approach uses the SegNet architecture, which achieves remarkable accuracy of 99.14%, surpassing benchmark standards (DC 96.94, JI 99.11).This research offers a promising solution for automated lesion segmentation in dermoscopic images, advancing the field of medical image analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image analysis

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Genetic Algorithm–Aided Deep Feature Selection for Improved Rice Disease Classification

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Operations Research Forum 2025年第1期6卷 1-24页

作者： Sharma, Rahul Singh, Amar Kumar, Prashant Singh, Mahipal Department of Computer Application GDC Marh Jammu India School of Computer Application Lovely Professional University Phagwara India School of Mechanical Engineering Lovely Professional University Phagwara India

Pests and diseases pose significant threats to crop safety and accessibility. Deep learning integration with traditional pest management fosters sustainable agriculture, minimizes chemical pesticide use, and facilitates early detection of pests and crop diseases through image and sensor data analysis. To ensure food security, reduce costs, and enhance overall production, computer vision techniques are essential for processing complex, high-dimensional real-world data. However, data dimensionality reduction is crucial for achieving accurate disease identification. Pre-trained models can extract valuable features from data. In this study, InceptionV3 and MobileNet V2 were utilized to extract comprehensive features from a small UCI rice leaf disease dataset. However, classifier performance using these features is uncertain due to factors such as domain mismatch, high-level data representation, model biases, dataset variability, and task complexity. To address these challenges, a rigorous evaluation of pre-trained feature suitability is necessary. A genetic algorithm–based feature selection (FS) approach was employed. FS streamlines data, reducing the information required for disease identification. An optimized ANN classifier, MobileNet-GA-ANN, achieved a 96.58% accuracy, outperforming other methods. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Feature selection Genetic algorithm Plant disease detection Soft computing Transfer learning

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Predictive Analytics for Parkinson's Disease Detection Leveraging Deep Learning Algorithms

Predictive Analytics for Parkinson's Disease Detection Lever...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kalpana, T. Thamilselvan, R. Chitra, K. Priyanka, S. Jahaan, R. Reshmi Thenmalar, T. Kongu Engineering College Department of Computer Application Erode India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331543617

Parkinson disorder is a neurological disease that progresses gradually which is typified by the brain's dopamine-producing neurons becoming exhausted. This neuronal loss results by symptoms including tremors, muscle stiffness, bradykinesia, and balance difficulties. Early detection and accurate prediction of PD progression are especially crucial for improving management and intervention strategies. This study builds a prediction model with the use of MRI datasets. To detect Parkinson's disease from MRI data, this study uses deep learning algorithms, such as Inception, Xception, and VGG19, and evaluates how well they perform in terms of accuracy. © 2024 IEEE.

关键词： Neurons

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ADNeuroNet: a neuroevolution-based neural network algorithm for the diagnosis of neurodegenerative diseases

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Neural Computing and applications 2025年第14期37卷 8593-8627页

作者： Khan, Afreen Zubair, Swaleha Ali, Irfan Department of Computer Application Faculty of Engineering & IT Integral University Lucknow226026 India Department of Statistics and Operations Research Aligarh Muslim University Aligarh India Department of Computer Science Aligarh Muslim University Aligarh India

Neurodegenerative disorders such as dementia and Alzheimer’s disease (AD) have adversely devastated the health and well-being of the older community. Given that early detection might help prevent or delay cognitive disorders associated with AD, developing an effective diagnostic technique is deemed appropriate to control the disease. Despite advances in clinical diagnostic standards and treatment techniques, the global prevalence of cognitive disturbance and behavioral problems remains steep. Besides the above challenge, the scarcity of AD-related open-source raw data prompted us to tailor ADNeuroNet, a neuroevolution-based neural network (NN) designed for predicting cognitively normal, mild cognitive impairment, and AD instances. To construct a robust NN algorithm that can predict the disease with high accuracy and improved efficiency, we worked on the combined cognitive and demographic clinical data to create a comprehensive model. Primarily, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository was used to develop this program. We employed three distinct datasets and developed a predictive model that achieved the maximum performance accuracy of 93.42%. Utilizing only baseline details, the as-developed model successfully diagnosed and predicted neurodegenerative disorders and is likely to emerge as an effective clinical tool in future endeavors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Disease control

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An Analytic Review on Stock Market Price Prediction using Machine Learning and Deep Learning Techniques

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Recent Patents on engineering 2024年第2期18卷 88-104页

作者： Rath, Swarnalata Das, Nilima R. Pattanayak, Binod Kumar Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

Anticipating stock market trends is a challenging endeavor that requires a lot of attention because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, loud, and chaotic data, stock market prediction is challenging. Investors need help to forecast where they should spend their money to make a profit. Investment methods in the stock market are intricate and based on the analysis of large datasets. Expert analysts and investors have placed a high value on developments in stock price prediction. Due to intrinsically noisy settings and increased volatility concerning market trends, the stock market forecast for assessing trends is tricky. The intricacies of stock prices are influenced by several elements, including quarterly earnings releases, market news, and other altering habits. Traders use a number of technical indicators based on stocks that are collected on a daily basis to make decisions. Even though these indicators are used to analyze stock returns, predicting daily, and weekly market patterns are difficult. Machine learning techniques have been extensively studied in recent years to see if they might boost market predictions compared to legacy or conventional methods. The existing methodologies have devised several strategies for predicting stock market trends. Various machine learning and deep learning algorithms, such as SVM, DT, LR, NN, kNN, ANN, and CNN, can boost performance in predicting the stock market. Based on a survey of current literature, this work aims to identify future directions for machine learning stock market prediction research. This research aims to provide a systematic literature review process to discover relevant peer-reviewed journal papers from the last two decades and classify studies with similar methods and situations into the machine learning approach and deep learning. In the current article, the methods and the performance of those adopted methods will be id

关键词： Financial markets

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A Collaborative Framework for Intrusion Detection in Cloud Computing Based on HLA-CNN-BiLSTM-SVM Model

A Collaborative Framework for Intrusion Detection in Cloud C...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Department of Computer Science & Application School of Computer Science and Engineering Sandip University Nashik India

ISBN: (纸本)9798350374933

In addition to describing a way to make better use of preexisting computer infrastructures, the phrase "cloud computing" describes a technology that facilitates the sale of access to shared computer resources and services. The flip side is that these dispersed and convoluted infrastructures are attractive targets for attackers. Because cyberattacks can reduce the quality of service offered to clients, the system constitute a substantial risk. This proposed method consists of three steps like preprocessing, feature selection, and model training. Data preprocessing, which includes both feature discretization and feature normalization, is a critical step in any knowledge discovery process. In this proposed to evaluate the impact of feature selection on improving classifier algorithm accuracy by comparing two feature selection methods: one that takes linear correlation into account, and another that takes mutual information into account. When training the model, an HLA-CNN-BiLSTM-SVM was utilized. With a 92.35 percent average accuracy, the suggested method surpasses CNN-SVM and BiLSTM. © 2024 IEEE.

关键词： Network intrusion

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Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

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Multimedia Tools and applications 2025年第1期84卷 1-36页

作者： Singh, Pratima Tripathi, Mukesh Kumar Patil, Mithun B. Shivendra Neelakantappa, Madugundu Department of Computer Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering N K Orchid College of Engineering & Technology Maharastra Solapur India Department of Computer Application D. K. College Dumraon Bihar Buxar India Department of Information Technology Vasavi College of Engineering Telangana Hyderabad India

In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. This paper proposes a novel multimodal emotion recognition model that employs a hybrid model with AMIG-based feature fusion on facial and EEG feature sets. The EEG signal and facial image are subjected to preprocessing to remove unwanted background noises with the Butterworth filter and Viola Jones Algorithm, respectively. While considering the pre-processed EEG signal, features such as EWFS-transform, wavelet features, and CSP-based features are extracted. In the proposed EWFS-transform, the window function is modified by using the frequency function and updated STFT. Conversely, the features including SE-AMM-EST-based features;LGXP and GLCM are extracted while considering the preprocessed face image. In the proposed SE-AMM-EST-based features, the mean of shape is updated and covariance in PCA for extracting texture-based features. In order to extract redundant free essential features, AMIG-based feature fusion is proposed. Further, the fused features are fed into a proposed hybrid model that includes LSTM and AML-CNN models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

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Illumination-Free Clustering Using Improved Slime Mould Algorithm for Acute Lymphoblastic Leukemia Image Segmentation

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Journal of Bionic engineering 2023年第6期20卷 2916-2934页

作者： Krishna Gopal Dhal Swarnajit Ray Sudip Barik Arunita Das Department of Computer Science and Application Midnapore College(Autonomous)Paschim MedinipurMidnaporeWest BengalIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Kalyani Government Engineering CollegeKalyaniWest BengalIndia

Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image ***,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local *** addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing ***,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)*** ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some *** paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image *** and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image ***-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus ***,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,*** the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).

关键词： Pathology image Image segmentation Clustering Color space White blood cell Optimization Swarm intelligence Fuzzy clustering Rough clustering

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