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Optimal feature with modified bi-directional long short-term memory for big data classification in healthcare application

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4441-4450页

作者： Kamble, Subhash Arunalatha, J.S. Venugopal, K.R. Computer Science and Engineering Bangalore University Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore University

Artificial intelligence together with its applications are advancing in all fields, particularly medical science. A considerable quantity of clinical data is available, yet the vast majority of it is wasted. It will be valuable in diagnosing human life early if it is applied effectively. An excellent classification system assists medical practitioners in providing a suitable diagnosis at a younger stage. Medical data typically contain a large number of features, and including all of them in decision making may result in over fitting of the classification model, lowering accuracy. As a result, an effective dimensionality reduction strategy that minimizes the amount of features while also improving classification accuracy is required. Therefore in this study, efficient dimension reduction-based medical data classification is proposed. The proposed approach is made up of three stages namely, pre-processing, feature selection, and classification. Initially, the medical data are collected and pre-processed. Then, to minimize computational complexity and time consumption, optimal features are selected using the adaptive rat swarm optimization (ARWO) algorithm. Then, the selected features are given to the modified bi-directional long short-term memory (MBi-LSTM) classifier to classify data as normal or abnormal. Then, the performance of the model is evaluated using different datasets and the effectiveness of the proposed model is analyzed in terms of different metrics. The proposed model implemented in python. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ARWO Big data MBi-LSTM Medical data Optimal features

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A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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DeepExplain: Enhancing DeepFake Detection Through Transparent and Explainable AI model

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Informatica (Slovenia) 2024年第8期48卷 103-110页

作者： Sunkari, Venkateswarlu Srinagesh, A. Department of Computer Science and engineering Acharya Nagarjuna University Nagarjuna Nagar Andhra Pradesh Guntur522510 India Department of Computer Science and engineering RVR & JC College of Engineering Chowdavaram Andhra Pradesh Guntur522019 India

The rapid advancement of digital media technologies has given rise to DeepFake videos, synthetic content generated using deep learning algorithms that can convincingly mimic real individuals' appearances and actions. This presents a significant challenge in maintaining digital content integrity, as DeepFakes pose threats to information veracity, security, and public trust. Addressing this challenge necessitates robust detection methods that not only accurately identify DeepFakes but also ensure transparency and understandability in their operations. This study introduces DeepExplain, a new approach that combine convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, augmented with explainability features to enhance the detection of DeepFakes. Utilizing the comprehensive DeepFake Detection Challenge (DFDC) dataset, DeepExplain demonstrates superior accuracy and balanced performance across essential metrics such as recall, precision, and area under the receiver operating characteristic (AUROC) curve. Crucially, by integrating explainability mechanisms like Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) values, DeepExplain not only identifies DeepFakes but also provides insights into the decision-making process, fostering trust and facilitating broader understanding. The findings underscore the potential of explainable and transparent AI solutions in combating the evolving threat of DeepFakes, highlighting important directions for future research and practical applications in digital media verification and security. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Transparency

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An Enhanced Career Prospect Prediction System for Non-computer Stream Students in Software Companies 1st

An Enhanced Career Prospect Prediction System for Non-comput...

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1st International Conference on Computational Intelligence for engineering and Management Applications, CIEMA 2022

作者： Abraham, Biku Ambili, P.S. Department of Computer Applications Saintgits College of Engineering Kottayam India School of Computer Science and Applications REVA University Bangalore India

ISBN: (纸本)9789811984921

The stakeholders of the present education system have a key focus on the chances of on-campus employability of their wards. Since software companies play prominent role as campus recruiters, there is a low demand for the traditional engineering branches. The need to enhance skills for software related employment is a challenge to the private sector professional institutions for the existence of non-computer streams in this scenario. In this study, the authors being academic experts in the similar field, attempt to support institutions, students, curriculum designers, and the faculty by providing insight to better employment possibility in campus. Data set for the study is from the same institution for which the placement prediction was completed. The methodology models the problem as a sequential event prediction problem and employs deep learning techniques. The system extracts data from data set with 19 attributes. The results indicate that this predictive approach provides enhancement methods by which a non-computer stream student can ensure full-time employment in software related jobs through in-campus selection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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Enhancing MRI-Based Brain Tumor Classification Accuracy through EfficientNetB3 and Advanced Image Data Augmentation Techniques 2

Enhancing MRI-Based Brain Tumor Classification Accuracy thro...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vanitha, K. Mahesh, R.T. Aafiya Thahaseen, A. Manivannan, B. Anitha, K. Thakur, Arastu Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Department of Computer Science and Engineering Karnataka Bangalore India Al-Ameen Engineering College Department of Computer Science and Engineering Erode India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering India CSI College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798331534950

This work analyzes the possibilities of the EfficientNetB3 architecture, reinforced by modern image data augmentation methods, in the classification of brain cancers from MRI scans. Our key objective was to greatly boost the accuracy and reliability of brain tumor diagnosis by combining the advantages of a pre-trained EfficientNetB3 model, along with a custom-designed data augmentation strategy geared at effectively resolving the inherent variabilities in MRI images. To achieve this, comprehensive preprocessing protocols were applied to the MRI datasets designated for training, validation, and testing. These protocols included the resizing and normalization of images to ensure uniformity and compatibility with the neural network. Further, our approach utilized real-time image augmentation techniques such as rotations, shifts, and flips during the training process, thereby enhancing the model's exposure to a wide range of imaging scenarios and increasing its robustness against variations not present in the training set. The outcome of this research was a notable enhancement in the model's classification accuracy, achieving up to 99% accuracy in identifying various types of brain tumors. These results not only demonstrate the effectiveness of integrating sophisticated convolutional network architectures with extensive image augmentation in medical diagnostics but also suggest potential pathways for setting new standards in the accuracy and reliability of medical image analysis, particularly in the critical domain of brain tumor diagnosis. © 2024 IEEE.

关键词： Deep learning

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Assessing classification approaches for categorizing Ayurvedic herbs

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Multimedia Tools and Applications 2025年第9期84卷 6711-6735页

作者： Umadevi, V. Kamath, Vaishnavi Department of Computer Science and Engineering B.M.S. College of Engineering Bull Temple Road Karnataka Bangalore560019 India

Ayurvedic herbs hold immense importance in traditional Ayurvedic medicine. These herbs are considered vital elements in maintaining physical, mental, and spiritual balance in the human body. According to ayurveda, diseases arise due to imbalances in doshas namely Vata, Pitta, and Kapha. Ayurvedic practitioners diagnose diseases by analyzing the dosha imbalances in an individual’s body and mind. Ayurvedic herbs play a crucial role in balancing the doshas and promoting overall health and well-being. Each herb possesses specific properties that can either pacify or aggravate the three *** research employs machine learning models to classify Ayurvedic herbs according to their dosha balancing properties. Using a 90-10 split, the data was randomly split into a training set and a test set. Data preprocessing using random oversampling and artificial sample generation using CTGAN is also performed on the dataset. A total of six experiments are performed on the dataset. Various classification models like SVM, KNN, RF, DT and XGBoost were used for classification. Using four target classes without oversampling, the XGBoost classifier yielded the best classification models with an impressive accuracy of 96%, Precision of 97%, Recall of 96%, F1-score of 96% and Root mean square error of 0.16. The experimental results provide compelling evidence that the proposed model, which uses ensemble learning methods, significantly outperforms conventional methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Assessing the predictive power of boosting techniques for diabetes

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Multimedia Tools and Applications 2025年 1-25页

作者： V, Umadevi G A, Yashaswini Department of Computer Science and Engineering B.M.S. College of Engineering Bull Temple Road Karnataka Bangalore560019 India

Diabetes affects more than half a million individuals globally. Diabetes can cause heart attacks, renal damage, and even blindness. Machine learning algorithms, with their intelligence capacity can precisely anticipate diabetes and prevent its complications which will allow premature detection of diabetes. Boosting techniques offer a powerful approach to machine learning by combining weak models to form a strong ensemble. They excel in addressing challenging classification tasks and can significantly improve predictive performance compared to individual models. This study investigates the predictive efficacy of two widely recognized boosting techniques, namely Adaboost (AB) and Gradientboost (GB), across four distinct diabetes datasets. The selected datasets include Pima diabetes dataset (PDD), Early stage diabetes dataset (EDD), Mendeley Diabetes dataset (MDD), and BFRSS Diabetes dataset (BDD), to facilitate comprehensive analysis and investigation for diabetes classification. Among the two boosting techniques, the fine tuned GB showed highest performance with respect to Accuracy, Precision, Recall, F1-score, AUC score, MSE (Mean Squared Error), G-Mean (Geometric Mean), Kappa, MCC (Mathews Correlation Coefficient) for diabetes classification on all four diabetes datasets. The accuracy of prediction on four datasets obtained by GB was 87.66%, 98.07%, 99.00% and 85% respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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Identification of Different Medicinal Plants Using Machine Learning and Image Processing 5th

Identification of Different Medicinal Plants Using Machine L...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Pokkuluri, Kiran Sree Phaneendra Varma, Ch Gurujukota, Ramesh Babu Raja Rao, P.B.V. Usha Devi N, S.S.S.N. Prasad, M. Pamarthi, Nagaraju Shalem Raju, P.J.R. Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering University College of Engineering JNTU Kakinada Kakinada India

ISBN: (纸本)9783031770746

Medicinal plants have been an essential source of remedies and treatments for various ailments throughout human history. This paper presents an innovative approach that combines machine learning and image processing to identify various medicinal plants and extract information about their traditional and modern uses. This method offers efficiency and dependability by automating the identification procedure, which lessens the reliance on human expertise and may hasten the discovery and application of medicinal plants for therapeutic purposes. In this work identification of the medical plats is done thought the shape of the plant leave, text features using digital image processing techniques, color using Convolutional Neural Works (CNN). We have achieved an accuracy of 98.6% in identifying the medical plants and we have compared our work with the existing literature with various parameters like precision, accuracy and f1 score. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Adversarial machine learning

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Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

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computer Systems science & engineering 2022年第3期40卷 1127-1144页

作者： K.Abhirami M.K.Kavitha Devi Department of Computer Science and Engineering Kings College of EngineeringPudukkottai613303India Department of Computer Science and Engineering Thiagarajar College of EngineeringMadurai625015India

With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and ***-based learning promotes active and independent *** scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning *** digital learning improves the quality of teaching and also promotes educational ***,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning *** quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional *** is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning *** is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest *** this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective *** to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning *** adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.

关键词： Learner behavior modeling e-learning intelligent learning system machine learning algorithm

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