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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit

Micro and Nanosystems 2025年第1期17卷 45-57页

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sadhu, Arindam Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College Kolkata India Department of Electronics & Communication Engineering Greater Kolkata College of Engineering and Management West Bengal India Department of Computer Science & Engineering JIS College of Engineering West Bengal India

Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit. Methods: This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit. Results: Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language. Conclusion: This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture. © 2025 Bentham Science Publishers.

关键词： Quantum computers

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FRANN: Fuzzy Rough Set Based Artificial Neural Network for Prediction of Rainfall Status 5

FRANN: Fuzzy Rough Set Based Artificial Neural Network for P...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Mallick, Bishnupriya Mantri, Jibendu Kumar Ray, Niranjan Kumar Mscb University Department of Computer Application India Deemed to be University School of Computer Engineering Bhubaneswar751024 India

ISBN: (纸本)9798331522100

Rainfall status prediction is a process of forecasting the future status or condition of rainfall for a particular region. Prediction of rainfall status is essential because heavy or low rainfall may have many adverse effects on agriculture, transportation, environment, living conditions and economic strength. So, accurate prediction of rainfall status is helpful in designing better action plans for water resource management, disaster risk reduction, sustainable development, emergency management and improving living conditions. Computational intelligence and approximate reasoning of conventional machine learning models are limited by incomplete or imperfect knowledge. However, the soft computing techniques have the capabilities to handle uncertainty, imprecision, partial truth and approximate reasoning to extract information in a number of ambiguous situations. In this aspect, a fuzzy rough set (FRST) based artificial neural network (ANN) model is proposed to predict the future rainfall status. Incorporating FRST method in ANN model can improve the prediction accuracy and reduce the overheads associated with both memory as well as computation time by selecting the dominant attributes and handling inconsistent data present in the information system. Rainfall status prediction accuracy of the proposed hybrid model is compared with two other hybrid models namely FRST based decision tree (DCST), FRST based support vector machine (SVM) and three other existing classification based prediction models namely DCST, SVM and ANN. The findings of the result suggest that the proposed model outperform its counterparts. © 2025 IEEE.

关键词： Risk management

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Predictive Analytics for Parkinson's Disease Detection Leveraging Deep Learning Algorithms

Predictive Analytics for Parkinson's Disease Detection Lever...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kalpana, T. Thamilselvan, R. Chitra, K. Priyanka, S. Jahaan, R. Reshmi Thenmalar, T. Kongu Engineering College Department of Computer Application Erode India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331543617

Parkinson disorder is a neurological disease that progresses gradually which is typified by the brain's dopamine-producing neurons becoming exhausted. This neuronal loss results by symptoms including tremors, muscle stiffness, bradykinesia, and balance difficulties. Early detection and accurate prediction of PD progression are especially crucial for improving management and intervention strategies. This study builds a prediction model with the use of MRI datasets. To detect Parkinson's disease from MRI data, this study uses deep learning algorithms, such as Inception, Xception, and VGG19, and evaluates how well they perform in terms of accuracy. © 2024 IEEE.

关键词： Neurons

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SVR: Feature Selection Based Support Vector Regression for Humidity Data Prediction 5

SVR: Feature Selection Based Support Vector Regression for H...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

ISBN: (纸本)9798331522100

Weather data like humidity is a key indicator in meteorology which is influenced by multiple climatic factors. Higher humidity levels become a threat to human health, cause storms and also lead to increase in rainfall, temperature and air borne pollutants. Therefore, prediction of humidity can be helpful in taking necessary action plans for protection of both human life and environment. Weather data can be predicted by multiple regression methods using explanatory variables. However, the effectiveness and prediction accuracy of the multiple regression methods are affected by the irrelevant or non-informative features and the uncertainties lie with the predictions. Feature selection (FS) methods can improve the effectiveness of prediction models and also reduce the uncertainties of predictions by reducing the dimensionality of the dataset by selecting more explanatory variables. On this basis, we have proposed feature selection-based support vector regression (SVR). The proposed hybrid model incorporates Random Forest (RF) model-based FS method in SVR to predict the future humidity data. Performance of RF model-based FS method is compared with two other feature selection methods like correlation-based method and recursive feature elimination (RFE) method. The prediction accuracy of the SVR model is compared with two other multiple regression models: artificial neural network (ANN) regression and decision tree regression (DTR). © 2025 IEEE.

关键词： Multiple linear regression

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Review on Predicting the Landslide Susceptibility using Machine Learning Techniques 2

Review on Predicting the Landslide Susceptibility using Mach...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sinha, Abhishek Giri, Shivam Kumar Yadav, Himanshu Prasad, Sanjeev Kumar Mishra, Priya School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Application ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challenges related to data quality, model coherence, and generalizability across diverse geographical and climatic regions. Additionally, while advanced machine learning (ML) and deep learning (DL) models offer high accuracy, they often require significant computational resources, which limits their practical application in real-time scenarios. In this review, we aim to highlight the strengths, limitations, and potential improvements of machine learning techniques in predicting landslide susceptibility, providing a comprehensive reference for researchers and practitioners in this domain. Furthermore, the role of Explainable AI is examined to address the critical need for interpretable and transparent models in real-world applications. Hybrid ML-DL models outperform traditional methods in handling complex spatial-temporal data, with dynamic factors improving accuracy and reliability. Explainable AI enhances interpretability, while advancements and collaborations address data and generaliz ability challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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Deep Learning-Based Detection of Foliar Diseases in Apple Plants Using an Assembled CNN Model 3

Deep Learning-Based Detection of Foliar Diseases in Apple Pl...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Sahu, Yatendra Bhargava, Arpita Thakur, Ghanshyam Singh Jain, Rishi IIIT Department of Computer Science and Engineering Bhopal India MANIT Department of Computer Application Bhopal India

ISBN: (纸本)9798350391565

The apple industry faces significant economic losses due to diseases and pests, contributing to low productivity levels. Detecting apple diseases promptly is essential for controlling their spread and improving overall productivity. However, early identification and diagnosis of these diseases are challenging due to various factors. In this work, we evaluated the performance of three distinct models: a custom CNN, Xception, and Resnet152V2. These models were tested on an expert-annotated dataset of apple diseases, comprising approximately 1821 high-quality RGB images across different categories of following types: C1: scab affected apple, C2: cedar apple affected by rust, C3: diseases of complex nature, and C4 samples of healthy apples. The custom CNN based proposed model trained for 50 epochs could yield an approximate accuracy of around 95%. © 2024 IEEE.

关键词： Plant diseases

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An optimized pair-wise comparison approach for automated feature weight assignment in content-based image retrieval system

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Multimedia Tools and applications 2024年 1-31页

作者： Rout, Narendra Kumar Ahirwal, Mitul Kumar Atulkar, Mithilesh Department of Computer Application NIT CG Raipur492010 India Department of Computer Science and Engineering MANIT M.P Bhopal462003 India

A content-based image retrieval system (CBIR) needs intensity/weighted importance for individual features of an image to find similar images in the database to achieve better results. Generally, these weights are assigned manually and are difficult to calculate as per the nature of the images. An experienced user may be able to retrieve images with higher accuracy by providing suitable weights for features in comparison to that of an inexperienced user. There is a need to develop a method that is independent of manual assignment of weights. To fulfil that need, an optimization-based pair-wise comparison approach has been developed for reducing the user intervention in the retrieval system and finding the optimal feature weight based on the nature of a benchmark COREL 10kimage database. The proposed work is implemented with different optimization techniques to optimize the pair-wise comparison method known as the Analytic hierarchy process (AHP). For optimization, algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Grey-wolf optimization (GWO), and Jaya algorithm (JAYA) have been used to get optimal weight for image feature. As per the results and observations, the AHP-PSO approach performs promising accuracy in terms of average precision and recall as compared to distinct optimized AHP approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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From Pixels to Pain: The Silent Health Risk Associated with Electronic Device Use 8

From Pixels to Pain: The Silent Health Risk Associated with ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Alamma, B.H. Smitha, G.V. Khaiyum, Samitha Sanchita Dayananda Sager College of Engineering Dept of Master of Computer Application Bangalore India

ISBN: (纸本)9798331505462

In the field of artificial intelligence (AI), machine learning is a subset that focuses on using data to learn and create prediction algorithms. Electronics controls the flow of electricity for processing, and health entails more than just being free from disease. On the other hand, overusing devices can increase health hazards. The inquiry consists of an empirical study that was carried out in Istanbul, Turkey, with 1147 students, as well as a systematic evaluation of ML applications in mental health within the human-computer interface (HCI) literature. The condition of emotional, psychological, and social well-being that affects a person's thoughts, feelings, and behaviour in day-to-day living is known as mental health. The study examines how teens' mental and physical health are affected by their excessive use of digital gadgets, especially cellphones. In this artical the integration of digital technologies and machine learning (ML) in relation to teenage wellbeing and mental health treatment is examined. The assessment also emphasises the necessity of humancentered and morally sound machine learning applications in mental health services. It also discusses how machine learning (ML) might improve mental health services by improving the identification, diagnosis, and treatment of mental health issues. The results show that teens' psychiatric problems, musculoskeletal discomfort, and sleep disruptions are linked to excessive digital device useThe study comes to the conclusion that while ML and digital technologies have a great deal of promise for enhancing mental health outcomes, there are also important socio-technical issues that require attention. These include worries about model bias, data privacy, and the generalisation of machine learning algorithms. Future studies should concentrate on creating rigorous assessment frameworks and transdisciplinary methods to guarantee the ethical and successful application of ML in mental health services. © 2024 IEEE.

关键词： Electronic health record

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Rough Set Based Recipe Clustering Approach 10

Rough Set Based Recipe Clustering Approach

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Das, Priyanka Mukherjee, Chandrani Gupta, Samiksha Roy, Sayan Shah, Khushi Singh, Shreya Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350384369

Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categorical data sets is a crucial task. Conventional clustering analysis algorithms frequently fail to address the uncertainty surrounding the exact value of features found in these categorical data sets. Thus to aid in decision-making, rough set based clustering is frequently used. This study aims to identify the optimal clustering feature from the recipe data that will result in the grouping of related food products by applying the rough purity notion. The recipe data has been regarded as a categorical valued information system in this instance, and information theoretic attribute purity is taken into account using the rough purity technique. © 2024 IEEE.

关键词： Food chemistry

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An Improved Verification Method Based on Asynchronous Verification 4

An Improved Verification Method Based on Asynchronous Verifi...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Wang, Yiqi Wang, Yulong Ye, Junyan Duan, Yubing Ye, Ke Yin, Mingyong Institute of Computer Application China Academy of Engineering Physics Mianyang621900 China

ISBN: (纸本)9798331541729

With the rapid development of Industrial Internet of Things (IIoT) technology, its applications have permeated various aspects of life and multiple industry sectors, significantly enhancing convenience. However, the large-scale deployment of IIoT devices has also triggered numerous security issues, especially in enterprise-level applications. Although some current methods have alleviated these problems to a certain extent, they still face many challenges in practical applications. This paper proposes an improved solution based on asynchronous verification methods, integrating Risk Assessment Based on Security Baseline, Business Security Mechanisms, and Global Access Control List into the algorithms of the Verification Server. The aim of this method is to enhance internal traffic security by rejecting access from high-risk hosts, ensuring minimal API access authorization, and enforcing security policies, thereby avoiding the rapid depletion of resources at the Verification Server due to excessive malicious failed requests. © 2024 IEEE.

关键词： data security industrial internet of things verification

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