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UML and NFR-framework based method for the analysis of the requirements of an information system

International Journal of Information technology (Singapore) 2023年第1期15卷 411-422页

作者： Arif, Mohd. Mohammad, Chaudhary Wali Sadiq, Mohd. Computer Science and Technology Research Group Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi 110025 India Software Engineering Lab Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi 110025 India

Software requirements are the expectation of stakeholders which are identified and modeled by various requirements elicitation and modeling techniques like traditional methods, goal oriented methods, and unified modeling language (UML), etc. The identified software requirements are broadly classified into functional requirements (FRs) and non-functional requirements (NFRs). Based on our literature review, we found that most of the focus in the area of software engineering is on the analysis of FRs as compared to NFRs. For the successful development of any information system both FRs and NFRs should be considered equally during the elicitation and modeling process;and ignoring NFRs may lead to failure of software. Therefore, to address this issue, we developed a method for the analysis of both FRs and NFRs so that a successful software product can be developed by a software company. In the proposed method, FRs are modeled by UML use-case diagram, class-diagram, and activity diagram. The NFRs are analyzed using NFR-framework;and NFR propagation rules have been derived from the requirements of an information system. In this framework, fuzzy based approach has been used to analyze the contribution links of softgoal interdependency graph. The applicability of the proposed method is discussed by using FRs and NFRs of library information system. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Functional requirements Library information system Non-functional requirements Software requirements UML

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Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data 19

Cox-ResNet: A Survival Analysis Model Based on Residual Neur...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Yin, Qingyan Chen, Wangwang Wu, Ruiping Wei, Zhi School of Science Xi'an University of Architecture and Technology Xi'an China New Jersey Institute of Technology Department of Computer Science Newark United States

ISBN: (纸本)9781665472432

Survival analysis with genomics data provides a deep understanding of biological processes related to prognosis and disease progression at the molecular level. However, high-dimensional small sample genome data causes computational challenges in survival analysis. To address this problem of overfitting and poor interpretation of existing models, we applied the deep learning technology to genome data and proposed a survival analysis model based on an image-based residual neural network model, called Cox-ResNet. High-dimensional gene expression data was embedded into 2D images according to gene positions on chromosomes, and then a residual network model based on Cox proportional hazards was introduced to perform survival analysis. We demonstrated the performance of Cox-ResNet on five datasets of different cancer types from TCGA, comparing it with the cutting-edge survival analysis methods. The Cox-ResNet model not only shows better performance in prediction accuracy, but also biologically interpretable, by generating heat-maps and prognostic genes for high-risk groups with the guided Grad-Cam visualization method. By performing protein-protein interaction network analysis, we examined hub genes and their biological functions for the bladder cancer. These findings confirm that Cox-ResNet model provides a new solution for discovering the driver genes of poor cancer prognosis. © 2022 IEEE.

关键词： Deep learning

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Plant Disease Detection and Classification Using Transfer Learning

Plant Disease Detection and Classification Using Transfer Le...

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

作者： Karthigaiveni, S. Madhubala, C. Avinash, G. Saranya, E. Rohini, P. Yogapriya, G. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Avadi Tamilnadu Chennai India School of Environment and Architecture Design Srm Institute of Science and Technology Tamilnadu Chennai India Sri Eshwar College of Engineering Department of Computer Science Engineering Chennai India Saranathan College of Engineering Department of Computer Science and Engineering Tamilnadu Trichy India Chettinad School of Architecture Chettinad Academy of Research and Education Tamilnadu Chennai India

ISBN: (纸本)9798331543617

Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. The illnesses impede the development of their respective species. Timely and accurate identification and categorization of illnesses may mitigate the risk of further harm to the plants. The identification and categorization of these disorders have emerged as significant challenges. The conventional methods used by farmers to forecast and identify plant leaf diseases may be tedious and inaccurate. Challenges may occur while endeavoring to manually forecast illness kinds. The failure to promptly identify and categorize plant diseases may lead to the devastation of crops, causing a substantial reduction in yield. Farmers using automated image processing techniques in their fields may mitigate losses and enhance productivity. This study proposes using a deep learning framework utilizing the latest convolutional neural network model EfficientNet to identify tomato illnesses based on 18,161 plain and segmented photos of tomato leaves. The efficacy of two segmentation theories, namely U-net and Modified U-net, for leaf segmentation is shown. The proposed methodology for plant disease identification is straightforward and computationally efficient, necessitating less time for predictions compared to previous deep learning methodologies. This document presents the computed accuracies for several plant and leaf diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Mapping Quantum Circuits to 2-Dimensional Quantum architectures

Mapping Quantum Circuits to 2-Dimensional Quantum Architectu...

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2022 Informatik in den Naturwissenschaften, INFORMATIK 2022 - 2022 computer science in the Natural sciences, INFORMATIK 2022

作者： Datta, Kamalika Kole, Abhoy Sengupta, Indranil Drechsler, Rolf Cyber Physical System Bremen Germany JIS University Department of Computer Science and Engineering Kolkata India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering India University of Bremen Group of Computer Architecture Bremen Germany

ISBN: (纸本)9783885797203

We have been witnessing a rapid growth in quantum computing research over the years, with the emergence of demonstrable quantum computers of moderate size. The major issues that are faced to run a quantum algorithm reliably on these systems are: (i) lower qubit coherence period, (ii) noisy primitive gate operations, (iii) limited number of available physical qubits, and (iv) support of restricted set of 2-qubit operations. Overcoming these issues mandates physical resources that exceeds the capabilities of these noisy intermediate scale quantum (NISQ) systems. On the other hand, computation using bare qubits get further disturbed due to the inclusion of additional gates to mitigate the nearest neighbor constraints. In the present work, the 2-dimensional square, heavy-hex and fully hexagonal qubit coupling lattices are considered for mapping quantum circuits. The benefits are assessed in terms of minimal additional gates needed to satisfy the nearest neighbor (NN) constraint and the compilation complexity of mapping circuits on these architectures. From the experiments by mapping benchmark circuits on 16-qubit square and 65-qubit heavy-hex architectures from IBM as well as on a 64-qubit fully hexagonal architecture, it is observed that none of the square or heavy-hex lattice architecture provides uniform compilation advantage compared to the fully hexagonal architecture. It is expected that beyond the NISQ era, strongly connected lattices like hexagonal will become practically feasible. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Qubits

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Combination of Fuzzy-Weighted Zero-Inconsistency and Fuzzy Decision by Opinion Score Methods in Pythagorean m -Polar Fuzzy Environment: A Case Study of Sign Language Recognition Systems

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International Journal of Information technology and Decision Making 2023年第4期22卷 1341-1369页

作者： Albahri, O.S. Alsattar, H.A. Garfan, Salem Qahtan, Sarah Zaidan, A.A. Ahmaro, Ibraheem Y. Y. Alamoodi, A.H. Zaidan, B.B. Albahri, A.S. Al-Samarraay, Mohammed S. Jasim, Ali Najm Baqer, M.J. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim 35900 Malaysia Department of Computer Center Middle Technical University's Baghdad Iraq Faculty of Engineering & It British University in Dubia Dubai United Arab Emirates Computer Science Department College of Information Technology Hebron University Hebron Palestine Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Baghdad Iraq Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq

In the fuzzy multicriteria decision-making approach, a committee of decision-makers is usually involved in the assessment of the suitability of different alternatives based on the evaluation criteria by using linguistic terms and their equivalent fuzzy numbers. In this context, researchers have developed the Pythagorean fuzzy set (PFS) to overcome the limitation of intuitionistic fuzzy set in the description of decision-maker information such as imposing restrictions on the representation of membership and nonmembership grades. On the one hand, PFS still does not have sufficient ability and flexibility to deal with such issues. On the other hand, multipolar technology is used to operate large-scale systems in real-life situations, especially in dealing with dissatisfaction and indeterminacy grades for the alternatives of the reference set. Thus, m-polar fuzzy set is utilized and applied with other fuzzy sets because of its remarkable ability as a tool for depicting fuzziness and uncertainty under multipolar information in many circumstances. With the practical features of m-polar fuzzy set in combination with PFS, this paper employs it to extend two considerable MCDM methods, namely, fuzzy decision by opinion score method and fuzzy-weighted zero inconsistency. Such extensions, called Pythagorean m-polar fuzzy-weighted zero-inconsistency (Pm-PFWZIC) method and Pythagorean m-polar fuzzy decision by opinion score method (Pm-PFDOSM), are formulated to weight the evaluation criteria followed by alternative ranking progressively. The research methodology is presented as follows. Firstly, the mechanisms of Pm-PFWZIC and Pm-PFDOSM are formulated and integrated into the development phase. Secondly, the description of the real-world case study of the evaluation and benchmarking of the sign language recognition systems is adapted and presented. The result of Pm-PFWZIC shows that the criterion of 'finger movements' has the highest weight amongst the rest of the criteria, wherea

关键词： Decision making

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A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

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computer Systems science & Engineering 2023年第8期46卷 1789-1809页

作者： Hicham Moujahid Bouchaib Cherradi Oussama El Gannour Wamda Nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan EEIS Laboratory ENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE Team CRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Department of Computer Science University of Sheba RegionMarib14400Yemen DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information Technology College of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关键词： Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network

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Impurity explicit optimal Machine learning and statistical perfect construction for air quality prediction 2

Impurity explicit optimal Machine learning and statistical p...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Nareshkumar, R. Shalinikumari, R. Kowsalya, S. Sri Nandhini Vijay Anand, P. Mohamed, Ajmal Gayathri, S. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India Bharath Institute of Higher Education and Research Selaiyur Department of Architecture Tamilnadu Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamilnadu Chennai India CMR Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350365269

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy. © 2024 IEEE.

关键词： Machine learning

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Intelligent Caching Strategies for 5G Edge Networks using Machine Learning 4

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Guduri, Naga Venkata Ramakrishna Sethu, S. Shalinirajan, R. Reena, R. Karthikeyan, M. Uthayakumar, G.S. University of Technology and Applied Sciences-Al Musannah Electrical and Electronics Section Engineering Department Oman Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Computer Science and Business System Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%. © 2023 IEEE.

关键词： 5G mobile communication systems

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Opposition-based multi-objective ant colony optimization framework for sustainable retrofitting: time–cost–energy–risk trade-offs

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Asian Journal of Civil Engineering 2025年第5期26卷 2223-2239页

作者： Pokkuluri, Kiran Sree Chauhan, Trupti Ravindra Macedo, Víctor Daniel Jiménez Manjunath, T.C. Bharadwaj, Manish Sethi, Krushna Chandra Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Design Manipal School of Architecture and Planning Manipal Academic of Higher Education Manipal Karnataka Udupi India Mechanical Engineering Faculty Michoacan University of Saint Nicholas of Hidalgo Morelia Mexico Department of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru India Department of Management Studies Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India Civil Engineering Department Centurion University of Technology and Management Gajapati India

The increasing emphasis on sustainable retrofitting of buildings has brought attention to the need for optimizing trade-offs among critical objectives, such as time, cost, energy consumption, and risk. This study proposes an opposition-based multi-objective ant colony optimization (OB-MOACO) framework to address these challenges effectively. The framework incorporates opposition-based learning to enhance solution exploration and convergence in multi-objective optimization problems, specifically tailored for sustainable retrofitting projects. The study develops a time–cost–energy–risk trade-off (TCERT) model with four objectives: minimizing retrofitting time, cost, energy consumption, and risks. The model integrates constraints related to budget, project timelines, and energy efficiency to ensure feasibility and sustainability. A detailed case study of retrofitting a mixed-use building is presented, encompassing 11 aspects such as structural reinforcement, energy efficiency enhancement, and accessibility improvements. Results demonstrate the superiority of OB-MOACO over conventional methods like NSGA-III, MODE, and MOPSO in achieving Pareto-optimal solutions. Key performance metrics, including hypervolume (HV) and inverted generational distance (IGD), highlight the model's efficiency in balancing competing objectives. The study contributes to advancing sustainable retrofitting practices by providing an innovative, cost-effective, and energy-efficient decision-making framework. Implications for practitioners and policymakers are discussed, alongside recommendations for future research. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Multi-objective optimization Opposition-based ant colony optimization Pareto optimization Sustainable retrofitting Time–cost–energy–risk trade-off

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Qualitative Analysis of the Impact of Data-Driven Image Processing on Animation 8th

Qualitative Analysis of the Impact of Data-Driven Image Proc...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Dubey, Shish Kumar Raghvendran, U. George, Emmanuel Shukla, Vinod Kumar Department of Computer Science and Engineering Jaipur National University Rajasthan Jaipur India Sam Higginbottom University of Agriculture Technology and Sciences Prayagraj India Department of Engineering and Architecture Amity University Dubai Dubai United Arab Emirates

ISBN: (纸本)9789819786657

Integrating data-driven image processing into animation production is revolutionizing animated content creation. Our qualitative study explores its impact on visual quality, storytelling, and workflow efficiency. We assess enhancements in visual quality, such as sharper imagery and enhanced colour fidelity, improving viewer engagement. Data-driven approaches positively influence storytelling, enhancing narrative cohesion and emotional engagement. We compare data-driven and traditional methods, noting workflow advantages and artistic trade-offs. Ethical considerations underscore responsible data use. Our research offers valuable insights for creators, studios, and researchers, informing decision-making and promoting accountable integration of data-driven techniques. This study propels creative innovation and enriches viewer experiences in animated storytelling as the industry progresses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Animation production Data-Driven image processing Visual enhancement

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