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Adapting CRISP-DM to model enteric fermentation emission: farm level application

International Journal of Applied Decision Sciences 2025年第3期18卷 330-359页

作者： Guerrón, Philippe Belmont Hallo, María Luján-Mora, Sergio Faculty of Engineering Systems Escuela Politecnica Nacional Ladron de Guevara E11-253 Edif 20 Quito Ecuador Department of Software and Computing Systems University of Alicante Alicante03690 Spain

Enteric fermentation contributes substantially to greenhouse gas emissions (GGEs) in agriculture, but may be reversible in the short-term. To date, numerous attempts have been made to model the environmental impact of agriculture, but have failed to integrate multiple dimensions of production. The objective of this study is to adapt the cross industry standard process for data mining (CRISP-DM) at farm level, using the concept of life cycle assessment (LCA) and implemented a modified version of the global livestock environmental assessment model (GLEAM). Using local data collected over 20 years and secondary data, our results show that for dairy cattle, the methane emissions factor from cattle is lower among marginal farms 86 Kg CH4 head−1 year−1 compared to semi-intensive and intensive farms across time and geographical regions (107.4 and 113.5 respectively) and demonstrate that this type of application is relevant for developing countries and smallholder agriculture, where production data is often unavailable. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Farms

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On Bounding the Behavior of a Neuron 36th

On Bounding the Behavior of a Neuron

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36th International Florida Artificial Intelligence Research Society Conference, FLAIRS-36 2023

作者： Borowski, Richard Choi, Arthur Computer Science Department College of Computing and Software Engineering Kennesaw State University United States

A neuron with binary inputs and a binary output represents a Boolean function. Our goal is to extract this Boolean function into a tractable representation that will facilitate the explanation and formal verification of a neuron's behavior. Unfortunately, extracting a neuron's Boolean function is in general an NP-hard problem. However, it was recently shown that prime im-plicants of this Boolean function can be enumerated efficiently, with only polynomial time delay. Building on this result, we propose a best-first search algorithm that is able to incrementally tighten inner and outer bounds of a neuron's Boolean function. These bounds correspond to truncated prime-implicant covers of the Boolean function. We provide two case studies that highlight our ability to bound the behavior of a neuron. © 2023 by the authors. All rights reserved.

关键词： Boolean functions

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Parallel software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip

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Journal of Computer Science & Technology 2023年第2期38卷 405-421页

作者：张颖季鹏飞朱潘玮 Zebo Peng 李华伟江建慧 School of Software Engineering Tongji UniversityShanghai 200092China Department of Computer and Information Science Linkoping UniversityLinkoping 58183Sweden Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip(NoC)interconnection *** present a parallel software-based self-testing(SBST)solution that makes use of the bounded model checking(BMC)technique to generate test sequences and parallel *** this method,the parallel SBST with BMC derives the leading sequence for each router’s internal function and detects all functionally-testable faults related to the function.A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets,which guarantees the test quality and minimizes the test ***,a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel *** results show that the proposed method achieves a high fault coverage with a reduced test ***,by performing online testing in the functional mode with SBST,it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

关键词： software-based self-testing(SBST) parallel test kilo-core networks-on-chip(NoCs) online testing

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Enhancing software Cost Estimation Using Feature Selection and Machine Learning Techniques

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Computers, Materials & Continua 2024年第12期81卷 4603-4624页

作者： Fizza Mansoor Muhammad Affan Alim Muhammad Taha Jilani Muhammad Monsoor Alam Mazliham Mohd Su’ud Department of Software Engineering FAST-National University of Computer&Emerging SciencesKarachi75030Pakistan Faculty of Engineering Science and Technology IQRA UniversityKarachi72500Pakistan Department of Computer Science Bahria UnivesityKarachi74800Pakistan Faculty of Computing Riphah International UniversityIslamabad44600Pakistan Faculty of Computing and Informatics Multimedia University(MMU)Cyberjaya63100SelangorMalaysia

software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.

关键词： Machine learning software cost estimation PCA hyper parameter feature selection

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Sustainable Development Goal-17 and Nigeria Data Protection Regulation Adoption among Organizations in Nigeria

Sustainable Development Goal-17 and Nigeria Data Protection ...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Babangida-Sabo, Sumayya Avemaria Utulu, Samuel C. Ajibesin, Abel American University of Nigeria Department of Informaton Systems School of Informtaon Technology and Computing Yola Nigeria Amerucan University of Nigeria Department of Software Engineering School of Information Technology and Computing Yola Nigeria

ISBN: (纸本)9798350358155

Adoption of computerized systems by organizations in Nigeria poses some data security challenges to citizens and people from other nations. The challenges require organizations to deploy personal data security that are in line with global standards, hence, Nigeria Data Protection Regulation (NDPR) was introduced. NDPR is to promote ethical personal data security and Nigeria's attainment of the sustainable development goal-17. However, only small number of organizations in Nigeria are able to adopt the NDPR because its adoption is complex and necessitate the implementation of organizational change management (OCM) project. Experience shows that OCM projects usually fails because many OCM frameworks used for OCM projects do not align with both scientifically proven ideas and organizational realities. Our study addresses the benefits of collaborative practice research studies to the production of OCM frameworks that incorporates both theoretical ideas and organizational realities. Our study provides viable solutions to the problem of lack of practice oriented OCM frameworks that organizations in Nigeria can use to drive the adoption the NDPR. © 2024 IEEE.

关键词： Sustainable development goals

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Melanoma Moles Detection and Classification Using Cornernet Model 1

Melanoma Moles Detection and Classification Using Cornernet ...

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1st International Conference on Logistics, ICL 2024

作者： Nawaz, Marriam Nazir, Tahira Javed, Ali Masood, Momina Dawood, Hussain University of Engineering and Technology Department of Software Engineering Taxila Pakistan Riphah International University Department of Computing Islamabad Pakistan Skyline University College Sharjah United Arab Emirates

ISBN: (纸本)9798331518844

Melanoma is a malicious form of skin cancer and can develop wounds on the entire human body which can lead to the victim's demise at the advanced level. The timely and accurate detection of melanoma moles can prevent the victims from painful treatment and surgeries. However., immense similarity among the healthy and diseased areas and massive variations in the mass, contour., and shades of the melanoma moles complicate the recognition process. Here., we have employed a deep learning (DL) framework named the CornerNet model for the localization and categorization of skin lesions. The Hourglass-oriented CornerNet approach is used for deep features calculations which are later grouped by the one-step object recognizer network of the employed solution. The approach is tested on a standard data sample called the ISIC-2016 database and secured the average mAP and IOU values of 0.916., and 0.921 with an accuracy value of 90.08%. The model testing confirmed that our solution is vigorous for melanoma mole detection and cataloging. © 2024 IEEE.

关键词： Dermatology

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A Survey on Evolutionary Computation Based Drug Discovery

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

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Sinhala Sign Language Translation through Immersive 3D Avatars and Adaptive Learning 5

Sinhala Sign Language Translation through Immersive 3D Avata...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Gamage, B.G.J. Paranagama, R.P.S.D. Ranaweera, R.M.S.H. Dilshan, A.V.R. Weerathunga, Ishara Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Faculty of Computing Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research paper addresses the challenge of bridging the communication gap between sign language users and non-users through a comprehensive study on sign language translation. The focus is on Sinhala Sign Language (SSL), and the research problem encompasses various innovative components. These include the conversion of Sinhala Sign Language (SSL) into text, emotion recognition and representation, personalized sign language learning, and accurate identification and translation of sign language gestures. By leveraging machine learning, deep learning, 3D modeling, and image/video processing techniques, the system enhances accessibility, communication, and education for the deaf and hard-of-hearing community. The outcomes of this research encompass an accurate and visually appealing sign language translation system, personalized learning interfaces, and the integration of emotions into sign language communication. The research contributes by providing insights into SSL grammar and lexicon, developing a tailored sign language learning curriculum, implementing reinforcement learning algorithms for personalized feedback, utilizing 3D components for enhanced visual quality, and optimizing deployments and models for improved performance. The proposed system has significant implications for improved accessibility, education, and inclusivity, fostering effective communication and understanding between sign language users and non-users. © 2023 IEEE.

关键词： Emotion Recognition

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Data Augmentation for Emotion Detection in Small Imbalanced Text Data 22

Data Augmentation for Emotion Detection in Small Imbalanced ...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Koufakou, Anna Grisales, Diego De Jesus, Ragy Costa Fox, Oscar Florida Gulf Coast University Department of Computing and Software Engineering Fort MyersFL United States

ISBN: (纸本)9798350345346

Emotion recognition in text, the task of identifying emotions such as joy or anger, is a challenging problem in NLP with many applications. One of the challenges is the shortage of available datasets that have been annotated with emotions. Certain existing datasets are small, follow different emotion taxonomies and display imbalance in their emotion distribution. In this work, we studied the impact of data augmentation techniques precisely when applied to small imbalanced datasets, for which current state-of-the-art models (such as RoBERTa) under-perform. Specifically, we utilized four data augmentation methods (Easy Data Augmentation EDA, static and contextual Embedding-based, and ProtAugment) on three datasets that come from different sources and vary in size, emotion categories and distributions. Our experimental results show that using the augmented data when training the classifier model leads to significant improvements. Finally, we conducted two case studies: a) directly using the popular chat-GPT API to paraphrase text using different prompts, and b) using external data to augment the training set. Results show the promising potential of these methods. © 2023 IEEE.

关键词： affective computing data augmentation emotion detection emotion recognition NLP paraphrasing

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Performance Evaluation of Clustering Algorithms for Enhancing Test Case Prioritization in Regression Testing 4

Performance Evaluation of Clustering Algorithms for Enhancin...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Puviskar, S. Prabath Wasalthilaka, W.V.S.K. Kumara, B.T.G.S. Sabaragamuwa University of Sri Lanka Department of Computing & Information Systems Sri Lanka Sabaragamuwa University of Sri Lanka Department of Software Engineering Sri Lanka

ISBN: (纸本)9798350384864

Regression testing plays a crucial role in maintain software quality as applications evolving and getting more complex. Selection of a regression testing technique significantly influences overall software quality. In initial stages, this involves picking relevant test cases and remove unnecessary redundancies during minimization phase. Finally, prioritization of test cases phases most important ones is executed first which continue process iteratively or whole testing carried out which effect time and cost intensively. Test Case Prioritization method effective way to address the issue. In this study, we proposed approach to enhance Test Case Prioritization by integrating fault based methods and time of execution analysis. The study also evaluates the efficiency in proposed methodology through, employing APFD metric. The study aims to improve the precision and effectiveness of test case prioritization methods, advancing overall quality and reliability in dynamic software development environments. Notably, AHC outperforms in comparisons, showcasing its efficacy in improving outcomes. © 2024 IEEE.

关键词： software testing

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