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Feature engineering Methods for Analyzing Blood Samples for Early Diagnosis of Hepatitis Using Machine Learning Approaches

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computer Modeling in engineering & sciences 2025年第3期142卷 3229-3254页

作者： Mohamed A.G.Hazber Ebrahim Mohammed Senan Hezam Saud Alrashidi Department of Information and Computer Science College of Computer Science and EngineeringUniversity of Ha’ilHail81481Saudi Arabia Department of Computer Science College of Applied SciencesHajjah UniversityHajjah9677Yemen Department of Artificial Intelligence Faculty of Computer Science and Information TechnologyAl-Razi UniversitySana’a9671Yemen

Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions,and it has many types,from normal to *** is diagnosed through many blood tests and factors;Artificial Intelligence(AI)techniques have played an important role in early diagnosis and help physicians make *** study evaluated the performance of Machine Learning(ML)algorithms on the hepatitis data *** dataset contains missing values that have been processed and outliers *** dataset was counterbalanced by the Synthetic Minority Over-sampling Technique(SMOTE).The features of the data set were processed in two ways:first,the application of the Recursive Feature Elimination(RFE)algorithm to arrange the percentage of contribution of each feature to the diagnosis of hepatitis,then selection of important features using the t-distributed Stochastic Neighbor Embedding(t-SNE)and Principal Component Analysis(PCA)***,the SelectKBest function was applied to give scores for each attribute,followed by the t-SNE and PCA ***,the classification algorithms K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Artificial Neural Network(ANN),Decision Tree(DT),and Random Forest(RF)were fed by the dataset after processing the features in different methods are RFE with t-SNE and PCA and SelectKBest with t-SNE and PCA).All algorithms yielded promising results for diagnosing hepatitis data *** RF with RFE and PCA methods achieved accuracy,Precision,Recall,and AUC of 97.18%,96.72%,97.29%,and 94.2%,respectively,during the training *** the testing phase,it reached accuracy,Precision,Recall,and AUC by 96.31%,95.23%,97.11%,and 92.67%,respectively.

关键词： Hepatitis machine learning PCA RFE SelectKBest t-SNE

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Developing a Secure Framework Using Feature Selection and Attack Detection Technique

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computers, Materials & Continua 2023年第2期74卷 4183-4201页

作者： Mahima Dahiya Nitin Nitin Department of Electrical Engineering and Computer Science College of Engineering and Applied ScienceUniversity of Cincinnati2600 Clifton AveCincinnatiOH 45221USA

Intrusion detection is critical to guaranteeing the safety of the data in the *** though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion *** challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,*** to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection *** data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as ***,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack *** on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the ***,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced *** selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter ***,the selected features are trained and tested for detecting attacks using *** Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the

关键词： Cyber security data mining intrusion detection system(DataMIDS) marginal likelihood fisher information matrix(MLFIM) absolute median deviation based robust scalar(AMD-RS) functional perturbation(FP) inverse chi square based flamingo search optimization(ICS-FSO) hyperparameter tuned threshold based decision tree(HpTT-DT) Xavier normal distribution based relief(XavND-relief) and Bengio Nesterov momentum-based tuned generative adversarial network(BNM-tGAN)

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Optimized neuro fuzzy convolutional networks in gene expression analysis in microscopic image feature extraction and classification by deep learning architectures

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Neural Computing and Applications 2025年第6期37卷 5005-5017页

作者： Qamar, Shamimul Computer Science and Engineering Applied College Dharan Al Janoub Campus King Khalid University Abha Saudi Arabia

With the use of gene expression microarrays, also referred to as "gene chips," scientists can monitor the pace at which hundreds of genes in each cell or tissue are expressed and converted into proteins. These gene expression pictures of biological activity can help with disease diagnosis, prognosis, and treatment planning. They can also reveal new targets for drug discovery and infer regulatory circuits in cells. The popularity of deep learning methodologies has grown recently because of their wide range of applications in several sectors for inference and prediction. This research proposes novel techniques in gene expression analysis in microscopic image data feature extraction with classification. Input is collected as microscopic image which is processed for noise removal and image smoothening. Then, these image features were extracted using neuro fuzzy convolutional networks. The extracted features have been classified using a self-learning-based genetic algorithm to obtain input images' gene expression. Using these organized gene expression immunity system and abnormality of the body function have been analyzed. It is proposed that existing approaches be improved. I developed a multivariate and hybrid feature selection strategy to achieve good classification performance for high-dimension classification issues. Experimental analysis is carried out for various microscopic image datasets in terms of accuracy, recall, precision, F-Score, RMSE, and MAP. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fuzzy neural networks

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IoT-Based Indoor Air Quality Management System for Intelligent Education Environments

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IEEE Internet of Things Journal 2025年第11期12卷 18031-18041页

作者： Rosa-Bilbao, Jesus Butt, Fatima Sajid Merkl, David Wagner, Matthias F. Schafer, Jorg Boubeta-Puig, Juan University of Cadiz Department of Computer Science and Engineering Cádiz11519 Spain University of Cadiz School of Engineering Cádiz11519 Spain Frankfurt University of Applied Sciences FB2 Computer Science and Engineering Frankfurt60318 Germany

One of the leading causes of early health detriment is the increasing levels of air pollution in major cities and eventually in indoor spaces. Monitoring the air quality effectively in closed spaces like educational institutes and hospitals can improve both the health and the life quality of the occupants. In this article, we propose an efficient indoor air quality (IAQ) monitoring and management system, which uses a combination of cutting-edge technologies to monitor and predict major air pollutants like CO2, PM2.5, TVOCs, and other factors like temperature and humidity. The aim is to create an intelligent environment for IAQ. The data is captured and monitored using an Internet of Things network of sensors, manufactured by ourselves, in different lecture rooms at the university. The obtained data is then processed and correlated in real time using a complex event processing engine and analyzed by machine/deep learning algorithms. A long short-term memory neural network is proposed to forecast IAQ. Then a decision tree regressor is used to identify the relationships between temperature, humidity and different pollutants like CO2 and PM2.5. © 2014 IEEE.

关键词： Indoor air pollution

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Nonlinearity in Large-Cycle Cellular Automata

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Complex Systems 2025年第1期34卷 129-160页

作者： Mukherjee, Sukanya Adak, Sumit Department of Computer Science and Engineering Institute of Engineering and Management Kolkata University of Engineering and Management West Bengal Kolkata700091 India Department of Applied Mathematics and Computer Science DTU Compute Technical University of Denmark Kgs. Lyngby2800 Denmark

The class of maximal-length cellular automata (CAs) has gained significant attention over the last few years due to the fact that it can generate cycles with the longest possible lengths. For every l of the form l = 2n-1 where n ∈ , a cycle of length l can be generated by a cellular automaton (CA) of size ⌊log2 l⌋ + 1;this maximal-length CA is in fact the optimal (minimum-sized) CA to generate a cycle of length l. However, maximal-length CAs are, in general, linear and have some drawbacks. Moreover, it is nontrivial to address this problem for any l ∈ , that is, removing the constraint that l is of the form 2n-1. In this paper we therefore employ nonlinearity in the rules and aim to generate the near-optimal CA for any given cycle length l, meaning that the generated CAs will be of size n = (⌊log2 l⌋ + 1) + d, where the displacement d is as small as possible. We use a generic operator to concatenate a collection of component CAs of sizes n1, n2, …, nk by employing an evolutionary strategy. Here, n = n1 + n2 + ⋯ + nk and l ≥ lcm(1, 2, …, k ), where i (1 ≤ i ≤ k) is the maximum cycle length of a CA of size ni . An influencing factor in the design of component CAs is to achieve the near-optimal CA for a given cycle length. Here we consider nonlinear reversible large-cycle CAs as component CAs. We also measure the effectiveness of nonlinearity in large-cycle generation. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： CAs cellular automata evolution large-cycle CA neighborhood dependent nonlinearity reversible CAs

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Image Guidance Encoder-Decoder Model in Image Captioning and Its Application

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第9期51卷 1385-1392页

作者： Yang, Zhen Zhou, Ziwei Wang, Chaoyang Xu, Liang School of Applied Technology University of Science and Technology Liaoning Anshan China School of Computer and Software Engineering University of Science and Technology Liaoning Anshan China School of Computer and Software Engineering University of Science and Technology Liaoning Anshan China

This paper introduces a new network model - the Image Guidance Encoder-Decoder Model (IG-ED), designed to enhance the efficiency of image captioning and improve predictive accuracy. IG-ED, a fusion of the convolutional network VGGNet-16 and the long short-term memory network (LSTM), is designed based on the encoder-decoder structure. The image captioning performance sees significant enhancements when leveraging the IG-ED network model. The network training process unfolds in a series of steps. Initially, the input image undergoes convolution via the VGGNet-16 network, producing a 512-dimensional vector. Concurrently, each word in the image's caption is encoded to generate a corresponding 512-dimensional vector consistent with the image feature dimension. These two vectors form the input for the decoding process. Subsequently, the vectors are fed into the redesigned fusion LSTM (F-LSTM) network at different time steps to gradually train the parameters of the IG-ED framework. The training process is completed by utilizing a loss function for determining convergence. Evaluation of the IG-ED model's performance is conducted using CIDEr and seven other evaluation metrics on the MSCOCO 2014 dataset. The results exhibit substantial improvements over the "Adaptive Attention Mode" network and "Neural Talk" network. Additionally, the parameter count of the IG-ED architecture is significantly reduced compared to the "Adaptive Attention Mode" network, leading to decreased computational resource requirements and enabling edge computing on the neural network. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Long short-term memory

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Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete EVent system specification

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International Journal of Vehicle Systems Modelling and Testing 2024年第1期18卷 33-61页

作者： Maatoug, Abdelfettah Kharoubi, Sahraoui Maaskri, Moustafa Belalem, Ghalem Mahmoudi, Said Faculty of Applied Sciences Science and Technology Department University of Tiaret Tiaret14000 Algeria Faculty of Mathematics and Computer Science Computer Science Department University of Tiaret Tiaret14000 Algeria Faculty of Applied Sciences Electrical Engineering Department University of Tiaret Tiaret14000 Algeria Faculty of Exact and Applied Sciences Computer Science Department University of Oran 1 Ahmed Ben Bella Oran31000 Algeria Faculty of Engineering Computer Science Department University of Mons Mons7000 Belgium

This work provides a basis for studying energy management optimisation in power-split hybrid electric vehicles (PSHEVs) to reduce fuel consumption and increase powertrain efficiency by enforcing a strategy related to battery level and vehicle speed. Three modes were used to operate the vehicle to meet different situations related to available energy and desired speed. These are particularly abundance mode, ascetic mode and optimal mode. For this, a system was designed based on several sub-models written in the discrete EVent system (DEVS) specification. The vehicle's power train and control strategy models were created in the DEVSimPy environment, and a joint simulation was carried out from the composite sub models until the overall model is reached. Simulation results under several driving cycles showed that the proposed system improves the energy consumption of the vehicle in different operating modes of the engines and gives more opportunities to adopt electric motors in vehicle transmission. © 2024 Inderscience Enterprises Ltd.

关键词： Energy management

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MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

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computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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Analysis on Improved Gaussian-Wiener filtering technique and GLCM based Feature Extraction for Breast Cancer Diagnosis 2

Analysis on Improved Gaussian-Wiener filtering technique and...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ranjitha, K.V. Pushphavathi, T.P. Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bangalore India Department of Computer Science and Engineering Faculty of Engineering and Technology Ramaiah University of Applied Sciences Bangalore India

Breast cancer is become the most prevailing and fastest growing disease. In medical imaging, the use of machine learning and deep learning algorithms is essential. Classification of the tumor to predict the chemotherapy response for survival is trivial. In this paper, an innovative Gaussian-Wiener filter combination is used for de-noising the MRI images. These pre-processed images with good image quality are selected for tumor detection. On the basis of these pre-processed outputs, important features are extracted to determine the spatial relationship between the image pixels which results in better texture analysis for the tumor images. Analysis is made on the ISPY-2 trial breast MRI database. Results are analyzed which gives better image quality performance for MRI images. The filters and feature extraction method analyzed is used further in the segmentation and optimization process for breast detection and diagnosis to get the best accuracy of nearly 100%. The results also show better texture analysis for extracting features using GLCM based method. Furthermore, the MRI images for these methods used are explained for better performance in the process of breast cancer detection and diagnosis. © 2024 Elsevier B.V.. All rights reserved.

关键词： Feature extraction

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