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Reduction of the dataset of Baitul Mal aid recipients using the principal component analysis (PCA) method to improve C5.0 algorithm classification results

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AIP Conference Proceedings 2024年第1期3222卷

作者： Evi Susanti Sawaluddin Poltak Sihombing Master of Informatics Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

This research raises problems among Baitul Mal aid recipients regarding aid to micro businesses. The process of assisting is an important activity to develop micro-businesses so that they can continue to operate and develop. Selection is also needed to prevent aid from being distributed incorrectly. Therefore, the proposed solution is to utilize data mining techniques, especially classification methods (clustering) to identify potential aid recipients who truly deserve to receive aid. This research also uses the Principal Component Analysis (PCA) method to reduce attribute dimensions in the dataset to increase the accuracy of the classification process. By optimizing the classification process through appropriate attribute reduction, this research is expected to make a positive contribution to increasing the efficiency and accuracy of credit risk assessment in the banking sector. The results of this research are: accuracy after the dataset has been reduced by 2 (two) attributes has a higher value when compared to the dataset that has not been reduced, this happens because the attribute reduction process is carried out to reduce attributes that are not or less relevant. So it can be said that the reduction process influences the accuracy of the C5.0 algorithm classification results.

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SRGAN with Total Variation Loss in Face Super-Resolution 7

SRGAN with Total Variation Loss in Face Super-Resolution

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7th NAFOSTED Conference on information and computer Science, NICS 2020

作者： Nguyen-Truong, Hai Nguyen, Khoa N. A. Cao, San Advanced Program in Computer Science Faculty of Information Technology Vietnam National University Ho Chi Minh city Viet Nam

ISBN: (纸本)9780738105536

Facial image super-resolution is a crucial preprocessing for facial image analysis, face recognition, and image-based 3D face reconstruction. Convolutional neural networks were earlier used to produce high-resolution images that train quicker and shown excellent performance by learning mapping relation using pairs of low-resolution and high-resolution images. However, in some cases, they are incapable of recovering finer details and often generate blurry images. In this paper, we evaluate a method of applying Generative adversarial networks in generating realistic super-resolution images from low-resolution ones by using three typical losses for super-resolution: Content Loss, Adversarial Loss, Perceptual Loss, and proposed to use Total Variation Loss. We try different pre-trained famous Convolutional neural networks models (VGG19, FaceNet, and EfficientNet) in Perceptual Loss to have a general view with different backbones. Our network gains 32.67 of Peak signal-to-noise ratio (PSNR) and 0.89 of Structural similarity index (SSIM) in 100 random samples from the Flickr-Faces-HQ dataset. © 2020 IEEE.

关键词： Face recognition

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Towards Emotionally Intelligent Software Engineers: Understanding Students' Self-Perceptions After a Cooperative Learning Experience

Towards Emotionally Intelligent Software Engineers: Understa...

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ICSE Workshop on Cooperative and Human Aspects on Software Engineering, CHASE

作者： Allysson Allex Araújo Marcos Kalinowski Matheus Paixao Daniel Graziotin Center for Science and Technology Federal University of Cariri Juazeiro do Norte Brazil Department of Informatics Pontifical Catholic University of Rio de Janeiro Rio de Janeiro Brazil Graduate Program in Computer Science State University of Ceará Fortaleza Brazil Department of Information Systems and Digital Technologies University of Hohenheim Stuttgart Germany

ISBN: (数字)9798331538712

ISBN: (纸本)9798331502539

Background] Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. SE workers face increasing pressure to develop both technical and interpersonal skills, as modern software development emphasizes collaborative work and complex team interactions. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. [Objective] This paper analyzes SE students' self-perceptions of their EI after a twomonth cooperative learning project, using Mayer and Salovey's four-ability model to examine how students handle emotions in collaborative development. [Method] We conducted a case study with 29 SE students organized into four squads within a projectbased learning course, collecting data through questionnaires and focus groups that included brainwriting and sharing circles, then analyzing the data using descriptive statistics and open coding. [Results] Students demonstrated stronger abilities in managing their own emotions compared to interpreting others' emotional states. Despite limited formal EI training, they developed informal strategies for emotional management, including structured planning and peer support networks, which they connected to improved productivity and conflict resolution. [Conclusion] This study shows how SE students perceive EI in a collaborative learning context and provides evidence-based insights into the important role of emotional competencies in SE education.

关键词： Training Productivity Federated learning Software Encoding Data models Planning Faces Software engineering Software development management

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UNIFORM CYCLIC GROUP FACTORIZATIONS OF FINITE GROUPS

arXiv

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arXiv 2023年

作者： Kanai, Kazuki Miyamoto, Kengo Nuida, Koji Shinagawa, Kazumasa General Education Program National Institute of Technology Kure College Hiroshima737-8506 Japan Department of Computer and Information Science Ibaraki University Ibaraki316-8511 Japan Kyushu University Fukuoka819-0395 Japan Tokyo135-0064 Japan

In this paper, we introduce a kind of decomposition of a finite group called a uniform group factorization, as a generalization of exact factorizations of a finite group. A group G is said to admit a uniform group factorization if there exist subgroups H1, H2, . . ., Hk such that G = H1H2 · · · Hk and the number of ways to represent any element g ∈ G as g = h1h2 · · · hk (hi ∈ Hi) does not depend on the choice of g. Moreover, a uniform group factorization consisting of cyclic subgroups is called a uniform cyclic group factorization. First, we show that any finite solvable group admits a uniform cyclic group factorization. Second, we show that whether all finite groups admit uniform cyclic group factorizations or not is equivalent to whether all finite simple groups admit uniform group factorizations or not. Lastly, we give some concrete examples of such *** Codes 20D06, 20D08, 20D10, 20D20, 20D40, 94A60 Copyright © 2023, The Authors. All rights reserved.

关键词： Factorization

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

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International Conference on Informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

Optimized Security Mechanism for publicly Secret Key Sharing...

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2024 International Symposium on Securing Next Generation Systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

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High Level Learning Approach for High Level Collision Avoidance Using Sensors 1

High Level Learning Approach for High Level Collision Avoida...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Naibaho, Lamhot Razauddin Vertivendan, L. Karunakaran, V. Oberoi, Ashish Vishwath, N.C. Ajay Christian University of Indonesia English Language Education Study Program Faculty of Letters and Languages Indonesia Gulf Colleges Saudia Arabia Department of Computer and Information Sciences Saudi Arabia Galgotias University Department of CSE Uttar Pradesh Greater Noida India Faculty of Engineering and Technology Department of CSE Bangalore India RIMT University Department of CSE Godindgarh Mandi India Parul Institute of Engineering and Technology Parul University Aeronautical Department India

ISBN: (纸本)9781665456876

Motion planning for many unmanned aircraft is challenging because they have a larger configuration space than self-driving automobile development (Automated guided vehicles). Additionally, there are more significant uncertainties and disruptions in UAV environments, which makes inter automatic navigation more difficult. In this letter, I proposed a 2 recurrent neural networks (RL) oriented multi-UAV collision avoidance technique by modeling the unpredictability or noise in the environment. Our objective is to create a strategy that can construct a path without clashing with anything using local noisy data. But unlike supervised algorithms, RL lacks a steady training data set with floor labels, thus its collision avoidance strategies often show significant fluctuation and are difficult to reproduce. To solve these issues, we created a two-stage training method for RL-based collision avoidance. We first optimize the policy, and then we utilize a supervised learning methodology with a loss function that encourages the agents to adopt the well-known reciprocity obstacle detection mechanism. In the second step, we use transmission to fine-tune the policy. The complete computer simulation findings demonstrate that this approach can handle noisy local views with erratic sound levels and can design moment & accident paths under inadequate sensing. We review the impact of our policies in a variety of ways. © 2022 IEEE.

关键词： Recurrent neural networks

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Hybrid descriptor definition for content based image classification using fusion of handcrafted features to convolutional neural network features

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International Journal of information technology (Singapore) 2021年第4期13卷 1365-1374页

作者： Das, Rik Kumari, Khusbu De, Sourav Manjhi, P.K. Thepade, Sudeep Department of Post Graduate Program in Information Technology Xavier Institute of Social Service Ranchi Jharkhand India Department of Computer Applications Yogoda Satsanga Mahavidyalaya Ranchi Jharkhand India Department of Computer Science & Engineering Cooch Behar Government Engineering College Cooch Behar India Department of Mathematics Vinoba Bhave University Hazaribag Jharkhand India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India

The efficacy of content-based image classification is dependent on the richness of the feature vectors extracted from the image data. Traditional feature extraction techniques highlight single low level image characteristics like shape, size, texture, color etc. for feature generation. The process often fails to extract meaningful descriptors since considering a single image characteristic will ignore other rich properties of image contents. Mass adoption of Convolutional Neural Network (CNN) has significantly improved classification performances for content-based image data. Recent literature has documented high level of precision for image classification by carrying out transfer learning with open source pre trained Convolutional Neural Network (CNN). However, the concept of transfer learning experiences major setback in case of limited training data due to overfitting of training instances during finetuning. This work has identified this challenge and has attempted to capture probability distribution of input images to a pre trained Convolution Neural Network by utilizing it as a fixed weight feature extractor. The proposed approach has addressed the overfitting problem by removing the finetuning step involved in transfer learning. Further investigation on robust descriptor definition is carried out by concatenating the pre-trained CNN features to handcrafted features. The hybrid architecture has encouraging outcomes that have outclassed the classification accuracies of state-of-the-art handcrafted techniques. © 2021, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Augmented feature learning Feature fusion Handcrafted features Image classification Pre trained Convolutional Neural Network (CNN) Representation learning

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Automatic Text Summaration of COVID-19 Scientific Research Topics Using Pre-trained Model from HuggingFace®

TechRxiv

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TechRxiv 2021年

作者： Ontoum, Sakdipat Chan, Jonathan H. Computer Science Program School of Information Technology King Mongkut’s University of Technology Thonburi Bangmod Thung-Khru Bangkok Thailand

By identifying and extracting relevant information from articles, automated text summarizing helps the scientific and medical sectors. Automatic text summarization is a way of compressing text documents so that users may find important information in the original text in less time. We will first review some new works in the field of summarizing that use deep learning approaches, and then we will explain the "COVID-19" summarization research papers. The ease with which a reader can grasp written text is referred to as the readability test. The substance of text determines its readability in natural language processing. We constructed word clouds using the abstract’s most commonly used text. By looking at those three measurements, we can determine the mean of "ROUGE-1", "ROUGE-2", and "ROUGE-L". As a consequence, "Distilbart-mnli-12-6" and "GPT2-large" are outperform than other. © 2021, CC BY.

关键词： COVID-19

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Improved accuracy of long short term memory (LSTM) algorithm in chili price prediction

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AIP Conference Proceedings 2024年第1期3222卷

作者： Rizki Ramadhansyah Benny Benyamin Nasution Ade Candra Informatics Engineering Master Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Computer Engineering Program Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia

The unstable price of chili is still a serious problem in society. Many previous studies have created systems that can predict chili prices. However, there are no chili price prediction results that are considered quite accurate and consistent. So the problem is overcome by processing chili price data using interpolation and dropna on missing values in order to maintain data quality and using the Long Short Term Memory (LSTM) algorithm development in predicting chili prices. This LSTM development was carried out to improve the inconsistent chili price prediction results due to the high error value. LSTM development is carried out in the cell state (ct) and hidden state (ht) with the aim of obtaining better prediction results. Then the accuracy of the error value obtained from the LSTM development in Labuhanbatu district is MAE = 2.589, RMSE = 3.419, and MSE = 11.695.900. This value is lower than using the original LSTM and only using dropna in processing missing value data, namely MAE = 5.517, RMSE = 7.930, and MSE = 62.900.289. Then the difference in percentage error value from the comparison is MAE = 53.07%, RMSE = 56.88%, and MSE = 81.41%. Therefore, it is expected that the low error value results from the development of LSTM can be an indicator of increasing the accuracy of chili price prediction results and making the results more consistent.

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