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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Nair, Rekha R. Babu, Tina Singh, Tripty Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Alzheimer's disease is the most prevalent cause of dementia, and its early diagnosis is crucial to prevent the progression to severe stages where cognitive abilities are severely impaired. This research paper presents an innovative approach to predict the severity of dementia through classification and grading. The research introduces an innovative adaptation of the DEMNET model, referred to as the DEMENtia network model. The research implements a novel methodology leveraging Convolutional Neural Networks (CNNs) to identify significant patterns within unorganized web-based data collections. The investigation employs a dataset com- prising four categories, obtained from the Kaggle platform. The developed model demonstrates exceptional performance, achieving 99.9% accuracy during training, 97.4% accuracy in testing, and an overall precision of 0.975. The DEMENtia network model suc- cessfully categorizes individuals into four groups: those without dementia, and those with moderate, mild, or very mild dementia. The model achieves a remarkable accuracy of 99.20% in classifying the Moderate demented class, a significant advantage over existing approaches. To understand this behavior, conducted an in-depth analysis by visualizing the pixel intensity distribution over the space. The proposed model validity has been confirmed through validation by a team of neurologists, ensuring its potential for real-world clinical applications. By accurately predicting dementia severity, the proposed model can aid in early diagnosis and treatment planning, contributing to improved patient care and management. © 2025 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

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Frontiers of computer science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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Robust Hybrid Machine Learning Model for Financial Fraud Detection in Credit Card Transactions 2

Robust Hybrid Machine Learning Model for Financial Fraud Det...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Jahnavi, Dingari Mona, A. Pulata, Sandeep Sami, Sasank Vakamullu, Bharadwaj Mohan Bharathi, G. Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

Financial fraud is a rising problem that affects both organizations and people, necessitating cutting-edge solutions to lessen its effects. The majority of machine learning models used now in the field of fraud detection include Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). While these traditional models frequently lack sensitivity, have trouble figuring out complex fraud patterns, and are rigid in their ability to adjust to new threats. To overcome them, this research work provides a significant advancement by combining Decision Trees and Logistic Regression into a hybrid model, boasting an impressive accuracy of 98.1%. The proposed methodology not only improves the system's ability to detect fraud accurately but also makes it more flexible to the changing fraud strategies. Additionally, a thorough comparison analysis highlights the approach's undeniable benefits. In a nutshell, this work provides a method for detecting financial fraud using a hybrid model that expertly strikes a compromise between sensitivity and accuracy. In addition to improving fraud detection, this innovation also strengthens the security of financial systems. © 2024 IEEE.

关键词： Comparison analysis Contemporary world Financial fraud Flexibility Fraudulent transactions Hybrid model

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Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays 3

Explainable AI-based Detection and Interpretation of Abnorma...

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3rd International Conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Muthukumar, Sai Smrithi Gandham, Bhavika Vijayekkumaran, Trisha Jyotsna, C. Aiswariya Milan, K. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523893

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes. © 2025 IEEE.

关键词： CheXpert Explainable AI Grad-CAM ResNet X-ray masks X-rays

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Customer Churn Prediction in Subscription-Based Services 5th

Customer Churn Prediction in Subscription-Based Services

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Kolli, Mahi Varadharajan, Nayantara Ajith, Keerthana Dinesh Kumar, K. Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Bangalore India

ISBN: (纸本)9789819788644

The rapid growth of subscription-based services in the telecom industry has led to a larger subscriber base for service vendors. However, customer churn, or the loss of clients, has become a critical issue for telecom companies. This paper addresses the problem of customer churn or attrition in the telecommunication industry and performs a churn prediction model that implements Machine Learning techniques on a big data platform. The study employs key algorithms, including Logistic Regression, Decision Tree, KNN Classifier, Random Forest and Support Vector Machine (RBF), to identify potential defectors and enable companies to implement retention techniques. This paper focuses on voluntary churn and implements engineering features and selection methods to improve predictive accuracy. As markets expand and customer acquisition costs increase, effective customer retention becomes paramount, prompting service companies to invest in prediction analytics for proactive churn management. The study leverages prior research and experiments with multiple machine-learning algorithms to build a predictive model of customer churn. The utilized model uses big data and innovative feature engineering and selection methods to predict customers who are most likely to churn. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Telecommunication industry

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Advancements in Multi-Objective Personalized Drug Design: A Comprehensive Review of Innovative Approaches and Technologies 2

Advancements in Multi-Objective Personalized Drug Design: A ...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Mallavarapu, Jashritha Reddy Navanithi, V.K. Bejjanki, Mohit Manomay Mithun Menon, M. Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

This comprehensive review paper presents an in-depth exploration of diverse approaches employed in multi-objective personalized drug design, encompassing cutting-edge methodologies that leverage advanced technologies. The multifaceted landscape of drug design is scrutinized through the lenses of neural network approaches, stochastic modeling, decentralization, blockchain-based solutions, topological optimization strategies, genetic algorithm optimization, energy consumption optimization, and blockchain-based pharmaceutical supply chain management. Each approach's unique strengths and applications are thoroughly examined, shedding light on their respective contributions to the burgeoning field of personalized medicine. This review not only synthesizes the current state of research but also identifies opportunities for synergistic integration of these approaches to further enhance the precision and efficacy of personalized drug design, ultimately paving the way for a transformative era in healthcare. © 2024 IEEE.

关键词： Blockchain

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Enhanced Steel Defect Detection using U-Net Model with Attention Mechanism 5

Enhanced Steel Defect Detection using U-Net Model with Atten...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sai, D. Gowtham Sri Mani Maheswari, Uma Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

In this research, detection of steel defects is a significant use of computer vision that can enhance industrial quality control. New prospects for automated defect segmentation are presented by recent developments in deep learning. This paper investigated the use of attention processes in a U-Net convolutional neural network architecture for improved steel flaw identification. Images of steel and matching defect masks from the Severstal Steel Defect Detection dataset were used to train the U-Net network. Preprocessing actions such as image rotation and scaling were performed in addition to data augmentation. When tweaked at various probability thresholds, the model produced defect mask predictions on a test set with good accuracy. When compared to ground truth masks, qualitative results showed that different defect types could be segmented well. Forecasts on fresh test pictures without any masks provided also shown a believable ability to identify flaws. According to this research, deep learning using attention blocks and U-Nets has the potential to improve automation in tasks related to semantic segmentation, such as steel inspection. To assess the model's efficacy on other defect types and steel materials, more work is required. The code and trained model serve as a basis for creating systems that are ready for production. © 2024 IEEE.

关键词： Attention mechanisms Convolutional neural networks Image processing Steel defect detection U-Net

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Crop Damage Detection using Machine Learning Approaches 6

Crop Damage Detection using Machine Learning Approaches

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Nair, Priyanka C Sampath, Nalini Reddy, P Praneeth Shruthi, P Sai Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (纸本)9798350381689

Agriculture is of vital importance to human life as it is the main source of livestock production and contributes significantly to the country's employment opportunities and economy. Ensuring high standards of production quality is essential. Recent technological advances are merging agricultural and machine learning techniques to help improve crop quality. The main objective of this work is to make use of machine learning approaches to identify the healthy status of the crop. Multi-class classification is applied to the dataset which helps classify crop conditions. As there is an imbalance of data of the 3 classes, Random under- sampling and Tomek under-sampling methods are applied on the dataset. A combination of Tomek undersampling and Smote undersampling has also been performed on this dataset. Various algorithms like Gradient Boosting, Gaussian Naïve Bayes, Neural Network (MLP) and k-Nearest Neighbors were performed on the dataset which helped show that Randomforest was the best model in terms of accuracy and F1 score. © 2024 IEEE.

关键词： Adversarial machine learning

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Beyond Search: A Network-Based Exploration of Buyer Behavior in the OLX Car Market 4

Beyond Search: A Network-Based Exploration of Buyer Behavior...

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4th International Conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024

作者： Ashvath, Y. Moin Ramya, G.R. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375466

The information on used cars offers a thorough analysis of the second-hand car industry. This dataset may be utilized to construct predictive models that can estimate the price of a used car based on its characteristics, including the make, model, year, mileage, fuel type, and condition. This dataset enables comprehensive data analysis to extract insights into market trends, pricing dynamics, and buyer-seller behavior. The main goals are to examine price patterns depending on different characteristics, assess the influence of fuel type and geographical factors on pricing and demand, investigate the correlation between mileage and resale value, and determine the most popular automobile models. Furthermore, the dataset enables the development of predictive models that can estimate both selling prices and the amount of time it takes to sell a product. Methodologies encompass the processes of data cleaning and exploratory data analysis (EDA). The results will assist in identifying communities or clusters within the dataset, uncovering groups of cars with similar attributes or market characteristics. The results aim to offer comprehensive market insights, improve comprehension of buyer behavior, and create predictive tools to assist sellers, buyers, and platform operators in decision-making. Ultimately, this will optimize strategies and operations in the used car market. © 2024 IEEE.

关键词： Sales

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