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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Ramakrishnan Raman Rejuwan Shamim Shaik Vaseem Akram Lalit Thakur Biju G. Pillai R. Ponnusamy Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering with Data Science Maharishi University of Information Technology Noida Uttaranchal Institute of Technology Uttaranchal University Department of CIVIL Parul University Department of IT & Business Analytics BIIB & Dean - Faculty of Management Sri Balaji University Pune (SBUP) Department of Computer Science & Engineering Chennai Institute of Technology

Supervised Classification (SML) is the pursuit of systems that reasoning from externally given instances to generate broad hypotheses, which subsequently generate predictions for future instances. One of the jobs performed by intelligent systems most commonly is supervised classification. Based on the data set, number of occurrences and variables in this study, the most efficient classification algorithm is selected. It is comprehensively described and contrasted with various supervised learning methods. Seven distinct machine learning algorithms were taken into consideration utilizing the Waikato Environment for data Analysis (WEDA) machine learning tool. For the identification process, the data set was used with 780 instances, eight independent variables (quality), and one dependent variable (variable). According to the data, SVM was the technique with the highest degree of precision and accuracy. Accordingly, the next accurate classification algorithms after SVM were determined to be Naive Bayes and Random Forest. The study demonstrates that precision (accuracy) and model construction time are two factors, whereas kappa statistics and mean error percentage (MAE) are two other factors. Therefore, controlled predictive machine learning requires precision, accuracy, and minimal error in ML algorithms.

关键词： Support vector machines Machine learning algorithms Supervised learning Training data Prediction algorithms Time measurement Classification algorithms

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A residual attention-based privacy-preserving biometrics model of transcriptome prediction from genome

A residual attention-based privacy-preserving biometrics mod...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Cheng Tian Song Liu Jinbao Li Guangchen Wang Luyue Kong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Medicine Shandong Medical College Jinan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Transcriptome prediction from genetic variation data is an important task in the privacy-preserving and biometrics field, which can better protect genomic data and achieve biometric recognition through transcriptome. Many transcriptome prediction methods have achieved good accuracy from genetic variation data. However, these traditional transcriptome prediction methods have the problems of linear assumption, overfitting, expose personal privacy, and extensive manual optimization. To solve these shortcomings, we propose an attention-based transcriptome prediction model from genetic variation named RATPM that improves the accuracy of transcriptome prediction and protects participant genomic data. In RATPM, we introduce and improve the deep learning model with multi-head self-attention into the transcriptome prediction stage of Predixcan, which uncovers the non-linear relationship between genetic variation and transcriptome. Moreover, we introduce a residual attention module to generate attention-aware features and extract more accurate features at different levels from genetic variation. Furthermore, we introduce the BERT pre-training module to encode genetic variation fully utilizing their contextual information. Our research enables scientific institutions to publish only predicted transcriptomic data for biometric purposes, thus protecting the genomic information of the subjects. Finally, we evaluated our model on the 1000 Genomes and Geuvadis projects datasets to compare with other baseline models.

关键词： Privacy Biometrics (access control) Biological system modeling Genomics Predictive models Feature extraction Gene expression

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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TransHFC: Joints Hypergraph Filtering Convolution and Transformer Framework for Temporal Forgery Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Huang, Jiahao Yuan, Xiaochen Lam, Chan-Tong Im, Sio-Kei Lei, Fangyuan Bi, Xiuli Macao Polytechnic University Faculty of Applied Sciences Macao999078 China Macao Polytechnic University Macau999078 China Guangdong Polytechnic Normal University Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou510665 China Chongqing University of Posts and Telecommunications Department of Computer Science and Technology Chongqing400065 China

The authenticity of audio-visual content is being challenged by advanced multimedia editing technologies inspired by Artificial Intelligence-Generated Content (AIGC). Temporal forgery localization aims to detect suspicious contents by locating forged segments. So far, most of the existing methods are based on Convolutional Neural Networks (CNNs) or Transformers, yet neither of them has fully considered the complex relationships within forged audio-visual content. To address this issue, in this paper, we propose a novel method, named TransHFC, which innovatively introduces hypergraphs to model group relationships among segments while considering point-to-point relationships through Transformers. Through its dual hypergraph filtering convolution branch, TransHFC captures both temporal and spatial level group relationships, enhancing the representation of forged segment features. Furthermore, we propose a new hypergraph filtering convolution Auto-Encoder that uses a multi-frequency filter bank for adaptive signal capture. This design compensates for the limitation of a single hypergraph filter. Our extensive experiments on Lav-DF, TVIL, Psynd, and HAD datasets demonstrate that TransHFC achieves state-of-the-art performance. © 1991-2012 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Machine Learning Techniques for Heart Disease Prediction

A Comparative Analysis of Machine Learning Techniques for He...

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Student Conference on Research and Development (SCOReD)

作者： Alawi Alqushaibi Mohammed Gamal Ragab Ebrahim Hamid Sumiea Safwan Mahmood Al-Selwi Mohd Hilmi Hasan Said Jadid Abdulkadir Hitham Alhussian Mahmoud Mustafa Al-Asbahi Computer & Information Sciences Universiti Teknologi PETRONAS Perak Malaysia Centre for Research in Data Science Universiti Teknologi PETRONAS Perak Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

Detection of heart disease is critical, as it is a common and serious health condition. Predicting its occurrence is vital in the healthcare domain, and machine learning techniques are increasingly being utilized to accurately identify patients at risk. To evaluate how well machine learning models can effectively predict heart disease, we used a pre-existing dataset from the UCI repository’s Cleveland database. Though the dataset originally contained 303 records and 76 attributes, we followed the literature by narrowing the focus to 14 relevant attributes in order to improve the precision of various algorithmic approaches. Our study employed five machine learning classifiers, namely KNearest Neighbors (KNN), CatBoost Classifier (CBC), Decision Tree Classifier (DT), XGBClassifier (XGBC), and Support Vector Classifier (SVM). We evaluated their performance using different metrics like accuracy, precision, AUC, and F1 score on the heart disease dataset. We found that the CatBoost Classifier had the best performance, achieving high accuracy and AUC with 0.88% and 0.94% values, respectively. This suggests that machine learning algorithms could aid in identifying heart disease at an early stage and have significant implications for improving patient outcomes in healthcare.

关键词： Heart Accuracy Machine learning algorithms Machine learning Predictive models Prediction algorithms Vectors

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Semantic segmentation of surgical hyperspectral images under geometric domain shifts

arXiv

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arXiv 2023年

作者： Sellner, Jan Seidlitz, Silvia Studier-Fischer, Alexander Motta, Alessandro Özdemir, Berkin Müller-Stich, Beat Peter Nickel, Felix Maier-Hein, Lena Heidelberg Germany Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Medical Faculty Heidelberg University Heidelberg Germany

Robust semantic segmentation of intraoperative image data could pave the way for automatic surgical scene understanding and autonomous robotic surgery. Geometric domain shifts, however – although common in real-world open surgeries due to variations in surgical procedures or situs occlusions – remain a topic largely unaddressed in the field. To address this gap in the literature, we (1) present the first analysis of state-of-the-art (SOA) semantic segmentation networks in the presence of geometric out-of-distribution (OOD) data, and (2) address generalizability with a dedicated augmentation technique termed’Organ Transplantation’ that we adapted from the general computer vision community. According to a comprehensive validation on six different OOD data sets comprising 600 RGB and hyperspectral imaging (HSI) cubes from 33 pigs semantically annotated with 19 classes, we demonstrate a large performance drop of SOA organ segmentation networks applied to geometric OOD data. Surprisingly, this holds true not only for conventional RGB data (drop of Dice similarity coefficient (DSC) by 46 %) but also for HSI data (drop by 45 %), despite the latter’s rich information content per pixel. Using our augmentation scheme improves on the SOA DSC by up to 67 % (RGB) and 90 % (HSI) and renders performance on par with in-distribution performance on real OOD test data. The simplicity and effectiveness of our augmentation scheme makes it a valuable network-independent tool for addressing geometric domain shifts in semantic scene segmentation of intraoperative data. Our code and pre-trained models are available at https://***/IMSY-DKFZ/htc. © 2023, CC BY.

关键词： Semantics

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Multi-Granular and Multi-Criteria Large-Scale Group Decision-Making Method Using the Aggressive Ordered Aggregation Operator Created Using Sentiment Analysis

SSRN

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SSRN 2023年

作者： Trillo, José Ramón Aguilar-Luzón, María del Carmen Morente-Molinera, Juan Antonio García-Cabello, Julia Cabrerizo, Francsico Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Social Psychology Faculty of Psychology University of Granada Granada18071 Spain Department of Applied Mathematics University of Granada Granada18071 Spain

Currently, human beings make different decisions daily, from choosing where to invest their money to deciding how they can improve their business. To help make the decision, Decision-Making processes can be used, in which several experts can participate. Nevertheless, when you want to make a Decision-Making process with a large number of experts, Large-Scale Group Decision-Making methods are used, but several problems arise when using them. The first problem is how to manage all the information that is generated in the Group Decision-Making process, as the number of participants is larger than in a previous Group Decision-Making process. The second problem is knowing how to control the aggressiveness that the debate can generate when experts give their information. The third problem is the management of relationships in the debate, as differences can generate controversy and arguments. To solve these problems, this paper proposes a novel Large-Scale Group Decision-Making process applied to a multi-criteria environment and has a multi-granular fuzzy linguistic modelling that uses sentiment analysis to aggregate the criteria and optimises the consensus value. With this method, the experts can express themselves as they wish and it also increases the relevance of the debate, as extra information is extracted and used during the process. © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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Implementation Analysis of KEBI Application on Non-Standard Words Detection in Student Essay Tasks on Language Variety

Implementation Analysis of KEBI Application on Non-Standard ...

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Information Technology International Seminar (ITIS)

作者： Ilmatus Sa' diyah Tresna Maulana Fahrudin Latinah Ibnu Zahy Atha Illah Cagiva Chaedar Bey Lima Burhan Syarif Acarya Department of Data Science Faculty of Computer Science Universitas Pembangunan Nasional &#x201C Veteran&#x201D Jawa Timur Surabaya Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Narotama Surabaya Indonesia

Non-standard word errors in writing sometimes escape the attention of writers or editors due to several factors, including unintentional or limited time in editing. Non-standard word is one of the Indonesian spelling error. Therefore, the application for detecting non-standard words in writing, KEBI, was created to minimize the height of non-standard words in writing. In this study, KEBI was analyzed in terms of application implementation to five various language essay documents as student assignments for Indonesian language courses. There are three things that are analyzed, namely the interface page, processing time, and the level of accuracy of detection and correction using the precision and recall method (confusion matrix). The overall research method includes literature study, data collection, testing, measurement of display, time, and accuracy. Based on the results of the analysis, the interface page display shows good quality. The processing time required by KEBI to detect non-standard words is in the range of 0.02-0.06 seconds. Meanwhile, KEBI's non-standard word detection accuracy rate is 86%, while KEBI's non-standard word correction accuracy rate is 90%.

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th International Conference on Advances in Computing and data sciences, ICACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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Is GPT-4 fair? An empirical analysis in automatic short answer grading

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Rodrigues, Luiz Xavier, Cleon Costa, Newarney Gasevic, Dragan Mello, Rafael Ferreira Computer Engineering Coordination Federal Technological University of Paraná Apucarana Brazil Instituto Federal Goiano – IF Goiano Iporá Brazil CESAR School Centro de Estudos e Sistemas Avançados do Recife Recife Brazil Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Melbourne Australia

Short open-ended questions represent a central resource in formative and summative assessments both face-to-face and online settings, ranging from elementary to higher education. However, grading these questions remains challenging for instructors, raising attention to the field of Automatic Short Answer Grading (ASAG). While ASAG has yielded valuable contributions to learning analytics, it often faces generalizability issues. Accordingly, the rapid advancement in Large Language Models (LLMs) has motivated their adoption to empower ASAG systems. Despite that, previous research has not investigated whether LLMs are fair graders in the context of ASAG. Therefore, this paper presents an empirical analysis aimed to understand LLMs' fairness in ASAG by using human grades as a baseline, comparing them to GPT-4's answers, and investigating whether the LLM's grades are equivalent in grading answers from varied groups of humans. Our results demonstrated that, while GPT-4 tended to be more lenient in its grading, it maintained consistent evaluation standards when assessing responses from different groups of students. GPT-4 remained consistent for questions of different subjects and levels of Bloom's taxonomy and for people with different demographics. These findings suggest GPT-4 is a fair grader, supporting its potential to empower educators and developers in using and designing ASAG systems. Nevertheless, we recommend further research to investigate these findings and understand how to optimize GPT-4's grades. © 2025 The Authors

关键词： Natural language processing systems

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