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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Ensuring Data Integrity: A Strategic Approach to Reconciliation in Data Migration Projects 15

Ensuring Data Integrity: A Strategic Approach to Reconciliat...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Raju, Mudunuru Krishnamurty Rajesh, Remala Kumar, Thummapudi Jithendra Guravaiah, Koppala Independent Researcher United States Indian Institute of Information Technology Kottayam Department of Computer Science & Engineering India

ISBN: (纸本)9798350370249

In today's corporate landscape, mergers are increasingly prevalent, driven by various factors such as strategic expansion, cost efficiency, and technological innovation. However, post-merger, the migration of data from disparate systems becomes imperative to consolidate information and establish an efficient operational model. Despite the prominence of data migration projects, reconciliation often takes a backseat to ingestion and loading processes. This article emphasizes the critical importance of upfront consideration for reconciliation strategies, drawing from practical experience and research. Neglecting this phase can lead to chaotic testing processes and unnecessary iterations, hindering the migration process. Through this article, we present a comprehensive strategy for designing and implementing reconciliation processes, from proactive planning to meticulous execution. By prioritizing reconciliation upfront, organizations can mitigate risks and streamline their data migration endeavors for enhanced success. Our approach includes unmasking encrypted Personally Identifiable Information (PII), reconciling it with source data, and implementing a metadata-driven audit framework enriched with extensive log information. This ensures thorough oversight and accountability, safeguarding data integrity throughout the migration journey. © 2024 IEEE.

关键词： Data integrity

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A Harmonic Approach to Handwriting Style Synthesis Using Deep Learning

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computers, Materials & Continua 2024年第6期79卷 4063-4080页

作者： Mahatir Ahmed Tusher Saket Choudary Kongara Sagar Dhanraj Pande Seong Ki Kim Salil Bharany School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra Pradesh522237India School of Engineering&Technology Pimpri Chinchwad UniversityPuneMaharashtra412106India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea Independent Researcher AmritsarPunjab143001India

The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human *** majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting *** is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly *** resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting *** network excels in generating conditional text by extracting style vectors from a series of style *** model performs admirably on a range of handwriting synthesis tasks,including the production of text that is *** works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting *** fields include digital forensics,creative writing,and document security.

关键词： Recurrent neural network generative adversarial network style encoder fréchet inception distance geometric score character error rate mixture density network word error rate

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Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

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computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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Two Ways to Scare a Gruffalo 1

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10th Indian Conference on Logic and Its Applications, ICLA 2023

作者： Singh, Shikha Lodaya, Kamal Khemani, Deepak Department of Computer Science and Engineering IIT Madras Chennai600036 India Independent Researcher Bengaluru560064 India

ISBN: (数字)9783031266898

ISBN: (纸本)9783031266881

This paper applies and extends the results from [22] on agent-update frames and their logic. Several interesting examples of actions for forgery and deception, agent-upgrade and agent-downgrade are considered. Going on from the earlier paper, a second interesting children’s story is modelled using these ideas. A dynamic epistemic logic is defined with all these actions and provided with a complete axiomatization. Decision procedures for satisfiability and model checking follow. A planning-oriented approach is also discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Model checking

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Observation of Heart Attack Patients Utilizing Machine Learning with Monarch Butterfly Optimization and IoT 26

Observation of Heart Attack Patients Utilizing Machine Learn...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Rahman, Wahidur Abul Ala Walid, Md. Saklain Galib, S.M. Rokhsana, Kaniz Abdul Hai, Talha Bin Mohammad Azad, Mir Ali Moni, Mohammad Uttara University Department of Computer Science and Engineering Uttara Dhaka Bangladesh Department of Computer Science and Engineering Khulna9203 Bangladesh Jahangirnagar University Department of Computer Science and Engineering Dhaka Savar1342 Bangladesh Independent Researcher Department of Computer Science and Engineering Dhaka Bangladesh Charles Stuart University Artificial Intelligence and Cyber Futures Institute BathurstNSW2795 Australia

ISBN: (纸本)9798350359015

Heart attack is a significant reason of human's death and disability and required emergency medical help which makes it a major global health issue. Previously, various models have been developed to accurately predict the heart attack utilizing non-invasive and portable technologies but very few of these works have used feature optimization method. Thus, the present work proposed a novel method by employing cutting-edge techniques like ML and IoT to predict the heart attack possibility accurately by analyzing vital signs of a human. This work has two stages (ML heart attack stage and an IoT monitoring framework) for predicting the heart attack possibility. Many steps build the ML prediction stage, including building a dataset, employing feature optimization algorithm to the dataset, prediction of heart attack through ML algorithms. The proposed work focused on human vital signs to diagnose and predict heart attack accurately. The proposed framework utilized the Monarch Butterfly Optimization algorithm to discover the best features from the dataset. The suggested framework acquired 100% accuracy by using the performance analysis of the models in predicting the possibility of occurring heart attack. In addition, an IoT-based monitoring system integrated with a smartphone app is proposed to collect the user's physiological data. The monitoring system will be connected to the cloud for data processing and prediction. The system can also be beneficial to getting help in emergency critical situations after happening a heart attack. © 2023 IEEE.

关键词： and Feature Extraction Heart Attack Internet of Things (IoT) Machine Learning Classifiers Monarch Butterfly Optimization

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Advancing Total Communication in SIBI: A Proposed Conceptual Framework for Sign Language Translation

Advancing Total Communication in SIBI: A Proposed Conceptual...

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2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Darmawan, I Dewa M. Bayu A. Linawati, Linawati Sukadarmika, Gede Wirastuti, Ni M. Ary Esta Dewi Pulungan, Reza Mulyanto Hariyanti, Ni Kadek Dessy Udayana University Doctoral Engineering Department Denpasar Indonesia Udayana University Electrical Engineering Department Jimbaran Indonesia Universitas Gadjah Mada Computer Science and Electronics Department Yogyakarta Indonesia National Taiwan University of Science and Technology Doctoral Engineering of Electronic and Computer Engineering Department Taipei Taiwan Politeknik Negeri Bali Business Administration Department Badung Indonesia

ISBN: (纸本)9798350382822

Total communication in sign language involves hand gestures as manual signs and facial expressions as non-manual signs. Both types of signs have an important role in communication. Research related to sign language translators focuses more on manual signs. Ignoring the non-manual components causes the translation of spoken language to be less precise because of the loss of information from the signer's facial emotions. Indonesian Sign Language (SIBI) is different from other sign languages. SIBI follows the rules of the Indonesian language, which consist of structure and syntax. This paper proposes a framework for sign language translation composed of manual and non-manual components. Two propositions will be tested and contribute to this research. The first proposition is related to the use of sign gesture detection and segmentation methods using optical flow-based methods, and the second proposition is associated with the use of STMC, Facial Expression Recognition, and Transformer methods at the stage of recognition and translation of sign language. The proposed work arrangement is expected to improve performance measured by WER, BLEU, and METEOR and reduce computational load as measured by processing time. © 2023 IEEE.

关键词： computer vision

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Exploring the Frontier of Technological Progress in Plant Biotechnology 6

Exploring the Frontier of Technological Progress in Plant Bi...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Aggarwal, Aarohi Mehta, Ankit Saraswat, Manish Shekhar Independent Researcher Haryana Panchkula India Lloyd Institute of Engineering & Technology Greater Department of Computer Science Uttar Pradesh Noida India

ISBN: (纸本)9798350304480

Innovative solutions to major issues in agriculture and sustainability are now available because of the fusion of bio-inspired computing and plant biotechnology. This study examines how genetic algorithms and neural networks, two bio-inspired technologies, can be used to improve crop qualities, forecast plant responses, and manage crops more effectively. We demonstrate their potential influence on developing plant biotechnology by discussing their benefits and applications to conventional approaches. We showcase effective implementations, ranging from better planting schedules to creating disease-resistant crops. Its capabilities are further enhanced by combining omics and phenotyping technologies with bio-inspired computing. We discuss issues including data integration difficulties and moral concerns with regard to genetically modified creatures. computer scientists and biotechnologists will be able to work together thanks to this multidisciplinary approach. In summary, we can conclude that combining bio-inspired computing with plant biotechnology has the potential to advance sustainable agriculture. This combination offers a crucial step toward resilient food systems and environmental stewardship as we move toward precision farming. © 2023 IEEE.

关键词： Bio-inspired Computing Biofortification Bioinformatics and Analytics CRISPR-Cas Genome Editing Nanotechnology Plant-Machine Interfaces Synthetic Biology Techniques

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Optimal Location to Use Solar Energy in an Urban Situation

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computers, Materials & Continua 2023年第4期75卷 815-829页

作者： Ngakan Ketut Acwin Dwijendra Indrajit Patra N.Bharath Kumar Iskandar Muda Elsayed M.Tag El Din Faculty of Engineering Udayana UniversityBali80361Indonesia An Independent Researcher NIT DurgapurWest Bengal700027India Department of Electrical and Electronics Engineering Vignan’s Foundation for Science Technology and ResearchGuntur522001India Department of Doctoral Program Faculty Economic and BusinessUniversitas Sumatera UtaraMedan20222Indonesia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

This study conducted in Lima, Peru, a combination of spatial decisionmaking system and machine learning was utilized to identify potentialsolar power plant construction sites within the city. Sundial measurementsof solar radiation, precipitation, temperature, and altitude were collectedfor the study. Gene Expression Programming (GEP), which is based on theevolution of intelligent models, and Artificial Neural Networks (ANN) wereboth utilized in this investigation, and the results obtained from each werecompared. Eighty percent of the data was utilized during the training phase,while the remaining twenty percent was utilized during the testing phase. Onthe basis of the findings, it was determined that the GEP is the most suitablenetwork for predicting the location. The test state’s Nash-Sutcliffe efficiency(NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Followingthe generation of the final map based on the results of the GEP model, itwas determined that 9.2% of the province’s study area is suitable for theconstruction of photovoltaic solar power plants, while 53.5% is acceptable and37.3% is unsuitable. The ANN model reveals that only 1.7% of the study areais suitable for the construction of photovoltaic solar power plants, while 66.8%is acceptable and 31.5% is unsuitable.

关键词： Solar energy renewable energy machine learning

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