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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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Average AoI Minimization With Directional Charging for Wireless-Powered Network Edge

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4889-4906页

作者： Chen, Quan Guo, Song Xu, Wenchao Li, Jing Shi, Tuo Gao, Hong Cai, Zhipeng Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hong Kong University of Science and Technology Department of Computer Science and Engineering 999077 Hong Kong Hong Kong Polytechnic University Department of Computing 999077 Hong Kong City University of Hong Kong Department of Computer Science Hong Kong Zhejiang Normal University School of Computer Science and Technology Jinhua321017 China Georgia State University Department of Computer Science AtlantaGA30303 United States

Age of Information (AoI) has been proposed as a new performance metric to capture the freshness of data. At wireless-powered network edge, the source nodes first need to be charged ready for update transmissions, which means the system AoI is not only decided by the scheduling of update transmissions but also by the designing of charging plan. However, the existing works either only focused on the point of scheduling update transmissions or have a rigid assumption that only one source node can be charged per time. Aiming at making the work more practical and general, we investigate the average AoI optimization problem at wireless-powered network edge with directional charging. First, the theoretical bound of the weighted sum of average AoI of the entire network with a directional charger is analyzed, which is proved to be related to nodes’ maximum transmitting interval and the charging strategy. An optimal charging time computation algorithm is proposed to obtain the maximum transmitting interval of each source node by considering the overlapped areas of different charging orientations. After then, an AoI-aware periodical charging scheduling algorithm is proposed to compute a periodical charging schedule while the average AoI is bounded, including a charging period T and the charging orientations assigned to each time slot within T. The proposed algorithm is proved to have an approximation ratio of up to 1.5625. Furthermore, several approximate algorithms are also proposed for average AoI optimization with multiple chargers and network bandwidth constraint. Finally, the extensive simulations demonstrate the high performance of the proposed algorithm in terms of AoI. © 2002-2012 IEEE.

关键词： Inductive power transmission

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Automated Disease Detection and Classification in Jackfruit Leaves: An Efficient Deep Learning-Based Approach 3rd

Automated Disease Detection and Classification in Jackfrui...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Saha, Dip Kumar Mahmud Joy, Md. Ashif Reza, Md. Rokonuzzaman Shovon, Reduanul Bari Department of Computer Science and Engineering American International University-Bangladesh Kuratoli Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering The International University of Scholars Dhaka Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9789819739363

Jackfruit is the national fruit of Bangladesh, and one of the most consumed fruits in India, Sri Lanka, Philippines, Indonesia, Malaysia, Australia, and many more countries. The every year due to diseases jackfruit production stays lower than the expectation. The early identification of these diseases is crucial for agricultural productivity, and our research showcases the effectiveness of deep learning techniques in addressing this issue. This study introduces an innovative approach to detect and classify two common jackfruit leaf diseases, algal spot and black spot, combining the YOLOv8 object detection model and the EfficientNetB7 pre-trained deep convolutional neural network model. The models trained on 6,360 images of those two diseases and our methodology achieved an accuracy of 99.9% on training and 99.5% on testing. This work represents a valuable contribution to agriculture, emphasizing the synergy between cutting-edge technology and age-old challenges, with the potential to positively impact the agricultural industry and global food security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Citrus fruits

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Multi-modal fusion and vision transformers for robust early AMD prediction

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Evolving Systems 2025年第2期16卷 1-22页

作者： Annamalai, Akila Palani, Durgadevi Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus Chennai India

Age-related macular degeneration is a chronic disease affecting a central area of the retina. Accurate disease identification aids in slowing down the progression of age-related macular degeneration and preserving vision. Various traditional techniques have been developed for effective age-related macular degeneration detection. However, traditional approaches failed to detect and classify the disease accurately and it consumes more time. However, traditional approaches failed to detect and classify age-related macular degeneration accurately. This research paper proposed an efficient model named as Multi-Modal Vision transformer model for the early and accurate prediction of age-related macular degeneration. This study aims to combine information from the Color Fundus Photography and Optical Coherence Tomography streams for performing efficient age-related macular degeneration diagnosis. The input images are needed to be preprocessed to enhance the image quality and make it suitable for further processing. The proposed framework integrated a Cascaded group attention transformer block which extracts the significant features from these modalities effectively. This block has the ability to solve computational complexity issues and attention head redundancy problems. Further, the multi-modal fusion method based on self-attention is introduced for fusing the features from Color Fundus Photography and Optical Coherence Tomography images. This fusion model is trained by applying both standard backpropagation and random gradient descent algorithms. For multi-class classification tasks, the fused features are classified into different classes based on the decision score. To visualize the single-modal and multi-modal output images in a heat map we applied a Class Activation Mapping model. Furthermore, the proposed technique is conducted on the Python platform and the performance is evaluated on different datasets with significant evaluation measures. This technique achieves

关键词： Image enhancement

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Comprehensive analysis of various imputation and forecasting models for predicting PM2.5 pollutant in Delhi

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Neural Computing and Applications 2025年 1-18页

作者： Karnati, Hemanth Soma, Anuraag Alam, Adnan Kalaavathi, B. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

Understanding and predicting air quality is pivotal for public health and environmental management, especially in urban areas like Delhi. This study utilizes a comprehensive dataset from the Central Pollution Control Board, detailing air pollutant concentrations at the ITO (Income Tax Office), Delhi station, from 2017 to 2023. Our focus on pollutants such as PM2.5, PM10, NO2, NH3, SO2, ozone, and CO highlights the challenges posed by missing data in environmental studies. Through the application of several imputation models, including KNN, linear regression, forward fill + backward fill, Fourier + KNN, linear interpolation, and statistical methods, we identified combination of forward fill and backward fill as the most effective method for addressing data gaps, specifically for ozone measurements. Building on this imputed dataset, we employ various forecasting models to predict PM2.5 levels, a critical pollutant. Our exploration includes time series and deep learning approaches, such as LSTM, Bi-LSTM, CNN-LSTM, GRU, Deep AR, WaveNet, TCN, ARIMA, SARIMA, and Prophet. The performance of these models is evaluated using daily data, with Bi-LSTM and LSTM with attention emerging as the top performers due to their accuracy in predicting PM2.5 concentrations. The implications of our findings extend beyond academic interest, offering practical insights for environmental policy and health advisories in Delhi. By demonstrating the efficacy of specific imputation and forecasting techniques, our research contributes to the broader effort of improving air quality monitoring and prediction. The success of Bi-LSTM and LSTM with attention models, in particular, suggests a promising avenue for future studies aiming to enhance the precision of environmental forecasts. © The Author(s) 2025.

关键词： Time series

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Improved employee bee algorithm for team formation

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Evolving Systems 2025年第2期16卷 1-20页

作者： Shingade, Sandip Niyogi, Rajdeep Shelke, Sushila Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Computer Engineering and Information Technology Veermata Jijabai Technological Institute Matunga Maharashtra Mumbai400019 India Department of Computer Engineering Cummins College of Engineering for Women Karve Nagar Maharashtra Pune411052 India

Finding an appropriate subset of agents (a team) from a larger pool of agents (the source set) so that the team exhibits a desired quality is the essence of the team formation problem. This problem is recognized to have high computational complexity, classified as NP-hard. In order to reduce the state space, this research paper incorporates techniques including vertical and horizontal reductions, which convert data to binary representation. To simplify the data representation for analysis, these methods use vertical and horizontal reductions to convert data to binary format, generating a subgraph. With this, the Honeybee Optimization algorithm’s Employee Bee phase (EBP) swap operation characteristic is used to efficiently optimize team communication costs by making it easier to determine a team with a minimum communication costs (MCC). The proposed approach is assessed using two well-known real-world data sets, the ACM data set and the DBLP data set, demonstrating an improvement of 79% in the ACM dataset and 68% in the DBLP dataset compared to existing literature approaches like Improved Jaya Modified Swap Operation, Improved Grey Wolf Optimization, and State Space Reduction Techniques for Team Formation. This highlights the significant positive impact of state space reduction techniques combined with the IEB on team performance, particularly enhancing communication efficiency and overall team effectiveness, as measured by MCC metrics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： NP-hard

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DELTa: Dynamic Energy-and-Latency-aware Task scheduling for Fog-Cloud Paradigm

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IEEE Access 2025年 13卷 74617-74633页

作者： Mahapatra, Abhijeet Pradhan, Rosy Majhi, Santosh K. Mishra, Kaushik Department of Computer Science & Engineering Odisha Burla768018 India Sikkim Manipal University Sikkim Manipal Institute of Technology Department of Artificial Intelligence and Data Science Sikkim India Department of Electrical Engineering Odisha Burla768018 India Department of Computer Science and Information Technology Chhattisgarh Bilaspur495009 India Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Department of Computer Science and Engineering Manipal India

The rapid proliferation of IoT devices like smartphones, smartwatches, etc. has significantly elevated the quantity of data requiring execution. It poses challenges for centralized Cloud computing servers, such as latency overhead and increased consumption of energy. To address this, Fog computing has been incorporated in this research as a complementary paradigm to the Cloud-based computing model. It thereby reduces the huge distance between end-user IoT gadgets and the Cloud computing servers. In order to improve the latency and waiting time this research employs a Multi-Level Queue (MLQ) for classifying tasks based on their priority. For parallel processing, a Self-adaptive Fuzzy C-means++ (SaFCm++) approach has been applied to cluster the Fog computing nodes depending on the heterogeneity of the computational nodes. For efficient scheduling, a Dynamic Energy-and-Latency-aware Task scheduling (DELTa) approach has been proposed to optimize latency, makespan, and utilization of resources. Extensive simulations are carried out for different test cases considering task and machine heterogeneity, followed by a statistical analysis. The overall improvements of the proposed method over other baselines are 9.75% for makespan, 64.59% for service latency, 13.90% for resource utilization, and 12.75% for energy consumption. The proposed energy-efficient scheduling method effectively manages diverse tasks and resources, enhancing task processing efficiency while minimizing energy consumption and maximizing resource utilization. Furthermore, the value of the Friedman statistics FF for the proposed model is estimated at 5.44, which shows superiority over the crucial value for four methodologies and four evaluation matrices. © 2013 IEEE.

关键词： Fog computing

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Hybrid RMDL-CNN for speech recognition from unclear speech signal

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International Journal of Speech Technology 2025年第1期28卷 195-217页

作者： Bhargava, Raja Arivazhagan, N. Babu, Kunchala Suresh Research Scholar Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Potti Sriramulu Chaluvadi Mallikarjuna Rao college of Engineering and Technology Andhra Pradesh Vijayawada India

ASR is an effectual approach, which converts human speech into computer actions or text format. It involves extracting and determining the noise feature, the audio model, and the language model. The extraction and determination of the noise feature is a crucial aspect of speech recognition, serving as both a process of information compression and signal deconvolution. ASR schemes are mostly employed in smart homes, smart appliances, and biometric schemes. Yet, traditional approaches offer very low performance because of a noisy environment. Moreover, local differences and accents negatively influence the ASR scheme execution during the conversion of the speech signals. This paper introduces a hybrid RMDL-CNN method to address these challenges. At first, the input of unclear speech is carried out by the dataset. Then, signal pre-processing is done by employing a Gaussian filter. After that, voice enhancement is accomplished by employing nonlinear spectral subtraction. Later, the speech word is segmented from the enhanced output based on the Attentional Encoder-Decoder approach and finally, the speech is recognized using the proposed RMDL-CNN. The RMDL-CNN method is devised by the combination of RMDL and CNN. Furthermore, the established RMDL-CNN is accessed for its efficiency based on several values of k-group value, as well as learning data. In addition, the introduced RMDL-CNN approach for speech recognition achieved better accuracy, PPV, as well as NPV of 0.909, 0.947, and 0.917 for dataset 1. Moreover, the RMDL-CNN has achieved the highest accuracy of 0.909, PPV of 0.926 and NPV of 0.888 for dataset 2. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

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SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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Taste-centered deep matrix factorization model for food recommendations

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Multimedia Tools and Applications 2025年 1-28页

作者： Singh, Reetu Dwivedi, Pragya Kant, Vibhor Computer Science and Engineering Department MNNIT Allahabad Teliarganj Uttar Pradesh Prayagraj211004 India Computer Science Department Rajiv Gandhi South Campus BHU Uttar Pradesh Barkachhakalan231001 India

Food recommendation systems (FRSs) provide personalized food recommendations to users based on their taste preferences. In FRSs, users’ unique taste preferences are influenced by personal history, specific dietary needs, and the impact of social connection recommendations. These preferences are not captured efficiently in existing traditional FRSs because these preferences are quite complex. Therefore, deep neural networks (DNNs) came into existence to deal with this limitation. Deep neural networks dominate traditional recommendation techniques due to their ability to capture complex interactions effectively. This paper presents a novel approach known as "taste-centered deep matrix factorization model" to understand users’ food preferences through ratings. It hybridizes DNNs with matrix factorization for prediction and recommendation. In this paper, firstly, deep matrix factorization (DeepMF) utilizes a multi-layer perceptron (MLP) to handle the interactions between user and item along with their latent features. This entails feeding the suitable data into the MLP layers to learn intricate patterns and relationships. Secondly, the learned representations from the MLP are employed to generate appropriate food recommendations to users. This model assists users in developing good eating habits that align with their food choices and requirements. Experimental analysis on two standard datasets demonstrates that our proposed FRSs model outperforms several state-of-the-art models in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Food ingredients

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