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HQF-CC: hybrid framework for automated respiratory disease detection based on quantum feature extractor and custom classifier model using chest X-rays

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International Journal of Information technology (Singapore) 2024年第2期16卷 1145-1153页

作者： Eswara Rao, G.V. Rajitha, B. Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad UP Prayagraj 211004 India

In recent years, humans have been affected by a variety of respiratory diseases, such as dry cough, fever, pneumonia, and COVID-19. Respiratory diseases may severely damage the respiratory system of humans. The early diagnosis of it can help enable appropriate treatment immediately to reduce the abnormalities in the healthcare monitoring systems. A chest X-ray could be used as one of the medical tools to assess the severity of respiratory diseases. In order to predict or classify respiratory diseases, many researchers have proposed different deep learning-based models. But they consume more computational resources. This article proposes a hybrid framework for respiratory disease detection based on a quantum feature extractor and custom classifier model (HQF-CC) from Chest X-rays. Here, we propose a quantum machine learning algorithm named MMS (Multi-Multi-Single) for feature extraction and a custom classifier for classification. The experiments were performed on the COVID-19 Radiography Dataset (CRD), which includes 15,153 Chest X-ray images of COVID-19, viral, and Normal diseases, respectively. The proposed model had the highest training and testing accuracy of 97.2% and 98.8% on the CRD dataset and a training and testing loss of 0.02, 0.01. Upon various experiments, the proposed model has proven to be more accurate, portable, and memory-efficient than other deep and machine learning models for respiratory disease detection. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Classification Deep learning model Feature extraction Quantum transfer learning Qubit Respiratory disease

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Review on Malware Classification and Malware Detection Using Transfer Learning Approach 5

Review on Malware Classification and Malware Detection Using...

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Priya, V. Sofia, A. Sathya Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9781665474672

The amount of malware infecting computers and other communication devices and migrating across the internet has greatly increased over the years. Numerous methods and procedures have been put forth up to this point to find and eliminate these hostile agents. However, a lot of malwares is still being developed, which can get past some cutting-edge malware detection algorithms, as new and automated malware production techniques emerge. Consequently, there is a need for the classification and detection of these antagonistic agents that have the potential to compromise the security of individuals, businesses, and a wide range of other digital assets. To develop a new improved approach for efficient zero-day malware detection, there is a compelling need to reduce bias and objectively assess these methods. This study focuses on investigating transfer learning strategies for malware picture classification, including AlexNet, VGG16, VGG19, GoogLeNet, and ResNet. Here, malware binaries are turned into grayscale images before being processed using models for classification based on transfer learning. As transfer learning uses pre-trained models, the primary objective is to save training time. In addition, an effective model for malware classification will be built in order to obtain performance that is unbiased. © 2023 IEEE.

关键词： Malware

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Optimal social welfare:A many-to-many data transaction mechanism based on double auctions

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Digital Communications and Networks 2023年第5期9卷 1230-1241页

作者： Jingyuan Duan Ling Tian Jianqiao Mao Jiaxin Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Shenzhen Institute of Information Technology China Department of Electronic and Electrical Engineering University College LondonGower StreetLondonUnited Kingdom Amazon(China)Investment Ltd China

With the development of Big Data and the Internet of Things(IoT),the data value is more significant in both academia and *** can achieve maximal data value and prepare data for smart city *** to data's unique characteristics,such as dispersion,heterogeneity and distributed storage,an unbiased platform is necessary for the data trading market with rational trading ***,there are multiple buyers and sellers in a practical data trading market,and this makes it challenging to maximize social *** solve these problems,this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism(SOMTM),which integrates three entities,a trading process and an algorithm named Many-to-Many Trading Algorithm(MMTA).Based on the market scale,market dominated-side and market fixed-side,simulations verify the convergency,economic properties and efficiency of SOMTM.

关键词： Data trading Double auction Social welfare Game theory Smart city

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An Organized Examination of Image Processing for Detection and Diagnosis of Plant Diseases 7

An Organized Examination of Image Processing for Detection a...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Naveenkumar, M. Akila, M. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

Agriculture is one of the most important and necessary sources of national wealth. Plant and crop diseases are one of the major factors that affect output quality and quantity. Lack of quality and quantity in food production creates a negative impact on the economy as well. Hence, it is crucial to identify plant diseases. Studies reveal a wide range of plant diseases occurring in different parts of the world. Various components of plants exhibit symptoms of plant diseases especially, the leaves. Extensive research is conducted across the globe and many horticulture specialists in liaison with technological experts apply computer vision and soft computing methods to automatically detect plant diseases from leaf photos. This article provides a summary of several aspects of such studies, along with their benefits and drawbacks. The authors addresses common infections as well as the state of such detection systems' research at various stages. The current feature extraction methods are examined in order to determine which one seems to function best across a variety of crop categories. This study will aid researchers in their understanding of the use of computer vision for identifying and categorizing plant diseases. © 2023 IEEE.

关键词： computer vision

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A Comprehensive Review on Family Budget Management 6th

A Comprehensive Review on Family Budget Management

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Akash, Rashik Shahriar Ullah, Mohammad Islam, Radiful Nahid, Sayed Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9783031501500

In the modern economic era, households have become one of the most significant corporate entities, and personal finances are essential management-related topics. Effective management of personal finances at a family level is really important to address all the financial related issues in an organized way. Any automated budget management system can help a family to utilize their incoming income in an efficient way. In this paper, we try to address the potential researches that were carried out considering the budget management related issues with a strong focus on family budget management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Finance

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Seeds Classification Using Deep Neural Network: A Review 6th

Seeds Classification Using Deep Neural Network: A Review

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Al Fahim, Hafiz Hasan, Md. Abid Bijoy, Md. Hasan Imam Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9783031503290

In the past few years, agricultural production studies have gained popularity, showing signs of rapid development. Using different kinds of computer technology, the newest thing to come along makes it easier for farmers to do their work. Many things affect agricultural output, but the efficiency of the seeds is the most important. Seed classification could give us more information about quality work, controlling seed quality, and finding impurities. Automated classification of seeds has been generally done based on factors like colour, texture, and size. Most of the time, specialists do this by looking at the samples, which is very much time-consuming. Adaptation of technologies for automated classification of seeds can be helpful in this regard. Fortunately, there are good number of research already carried out using Deep Neural Network (DNN) around the globe. In this paper, we provide a review of seed classification techniques with a strong focus on DNN. The goal of this research is to create a system for categorizing seeds based on visual and morphological traits. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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A Clustering Based Niching Method for Effectively Solving the 0-1 Knapsack Problem 1st

A Clustering Based Niching Method for Effectively Solving t...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Sarker, Md. Meheruzzaman Islam, Md. Jakirul Hossain, Md. Zakir Department of Computer Science and Engineering Dhaka University of Engineering and Technology Gazipur Bangladesh

ISBN: (纸本)9783031346217

The 0-1 knapsack problem (01-KP) is a NP-hard combinatorial optimization problems (COPs) with several applications. Because of its non-convexity, its search space contains several local and/or global optimum solutions. In this situation, both the classical and metaheuristic global optimization approaches often failed to locate the optimal solution to the 01-KP. Therefore, this research develops a clustering-based niching (CBN) method for maintaining and locating multiple solutions within an optimization run, increasing the possibility of locating the global optimal solution to the 01-KP. To do this, CBN method divides the population individuals into a number of clusters (similar to niches in a biology or ecology system) by measuring the Hamming distance between individuals. During the optimization, the individuals in the formed clusters independently explore different regions of the combinatorial search space in order to find promising solutions. For simplicity, the proposed CBN method is implemented using a modified binary PSO (lbest-BPSO) method. The numerical results show that the CBN method is superior than the well-known metaheuristic methods on the basis of solution quality, with a better success rate and average function evaluations, over widely used sets of 0-1 knapsack instance. This guarantees that CBN method is a suitable optimization method for determining the optimal solution to combinatorial optimization problems. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Combinatorial optimization

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On the use of the Total Constraint Violation as an Additional Objective in Evolutionary Multi-Objective Optimization

On the use of the Total Constraint Violation as an Additiona...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Nan, Yang Ishibuchi, Hisao Shu, Tianye Gong, Cheng City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9781665410205

In real-world applications, multi-objective optimization problems (MOPs) usually have multiple constraints. To solve constrained MOPs (CMOPs), various constraint handling techniques (CHTs) were proposed in the field of evolutionary multi-objective optimization (EMO). A simple CHT with high applicability is to use the total constraint violation as an additional objective. The total constraint violation-based CHT transforms a constrained (m-1)-objective MOP to an unconstrained m-objective MOP. This CHT was also used to create a real-world unconstrained multi-objective test suite called RE from real-world constrained problems. Recently, the RE test suite has been frequently used for evaluating EMO algorithms. Only when the additional objective value is zero (i.e., only when the total constraint violation is zero), solutions are feasible in the original constrained MOP. This means that feasible solutions of the original constrained MOP are located on the boundary of the Pareto front of the formulated unconstrained MOP. As a result, the final population of an EMO algorithm on the formulated unconstrained MOP includes many infeasible solutions of the original constrained MOP. This means that good solution sets for the unconstrained MOP are not always good solution sets for the original constrained MOP. In this paper, we propose an improved total constraint violation-based CHT. The core idea is to use not only positive constraint violations but also negative constraint violations. We apply the proposed CHT to real-world constrained MOPs. Experimental results show that the proposed modification improves the quality of feasible solutions obtained by the total constraint violation-based CHT. © 2024 IEEE.

关键词： Multiobjective optimization

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IoT cryptography with privacy-preserving ElGamal public key cryptosystem using oppositional artificial flora optimization algorithm

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Multimedia Tools and Applications 2025年第11期84卷 9287-9308页

作者： Paramasivam, Devi Sivaji, Sathyalakshmi Dayanandan, Venkata Subramanian Alqahtani, Abdullah Saleh Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India Department of CSE Hindustan Institute of Technology and Science Kelambakam Tamil Nadu Chennai603103 India Department of CSE Velammal Institute of Technology Chennai India Department of Self-Development Skills - Common First Year Deanship King Saud University Riyadh12373 Saudi Arabia

Privacy and security have received significant interest among the research communities Due to the increased exploitation of the Internet of Things (IoT). Since IoT devices gather most of the public data like identity, location, contact numbers, and energy utilization, security, and privacy become crucial. The lack of well-defined IoT-enabled security and privacy schemes will prevent the applicability of the IoT technologies from the client side. Therefore, this paper presents an ElGamal public key cryptosystem (EGPKC) for generating optimal keys utilizing an Oppositional Artificial Flora Optimization (EGPKC-OAFA) technique intended for preserving privacy in IoT. Additionally, the EGPKC approach with an optimum generation of authentication keys is applied on the medium access control (MAC) header for secure authentication. Besides, the optimal key selecting process for EGPKC occurs by employing the OAFA approach which inherits the oppositional-based learning (OBL) concept and classical artificial flora approach. To evaluate the effective analysis of the EGPKC-OAFA algorithm, huge simulations such as Known plaintext attack (KPA), chosen-plaintext attack (CPA), key sensitivity, and correlation coefficient are employed and the result is determined by different feature intervals. Accordingly, KPA and CPA reached the minimum value of 0.0987 and -0.0499 respectively. As a result, the proposed EGPKC-OAFA algorithm obtained superior performance compared to other methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

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An Enhanced Probabilistic Elastic Net Regression Model (EPERM) for Heart Disease Prediction

An Enhanced Probabilistic Elastic Net Regression Model (EPER...

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2024 International Conference on Future Technologies for Smart Society, ICFTSS 2024

作者： Selvi, Vennira Kumar, T.Ganesh Shajilin Loret, J.B. Kumar, K.Sampath Julian, Anitha Rishi, Priti Presidency University School of Cse and Information Science Bangalore India Galgotias University School of Computer Science and Engineering Greater Noida India Francis Xavier Engineering College Department of Information Technology Tirunelveli India Amet University Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Vadaplani Campus India

ISBN: (纸本)9798350373844

Heart attack prediction is a significant contributor to worldwide morbidity. Cardiovascular illness is a critical component of clinical data analysis prediction. A precise diagnosis of cardiac disease can save lives, yet an inaccurate diagnosis can lead to death. To predict heart disease prior, an Enhanced Probabilistic Elastic Net Regression model (EPERM) is presented, which is utilized to classify cardiac disease, and every feature selection is based on correlation with the goal value. The Attribute Ranking Method was used to select and organize highly positive associated attributes based on their weight. Finally, conditional probability is utilized to estimate the likelihood of cardiac disease in advance. This study makes use of one of the largest disease-related databases. Regarding projected illness progression, the suggested algorithm outperforms the most recent techniques. The findings also demonstrate how a patient's disease sequence may alter their risk management in the future. Our prediction model, based on Elastic Net Regression and data analysis, discovered several traits that clinical knowledge had previously overlooked. This article investigates clinical diagnostic records from the past decade to identify probable heart cancer symptoms and comorbidities. It also provides early disease prediction, allowing a greater number of patients to receive an early diagnosis. The suggested model accurately predicts evidence of a person's heart illness using Probability-based Elastic Net Regression. The results show that the technique is a reliable and consistent prediction model. © 2024 IEEE.

关键词： Prediction models

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