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2024 IEEE International Conference on Big Data, BigData 2024

作者： Russo, Raffaele Russo, Diego Orlando, Gian Marco Romano, Antonio Riccio, Giuseppe Gatta, Valerio La Postiglione, Marco Moscato, Vincenzo University of Naples Federico II Department of Electrical Engineering and Information Technology via Claudio 21 80125 Naples Italy Northwestern University Department of Computer Science McCormick School of Engineering and Applied Science 2233 Tech Dr Evanston IL 60208 United States

ISBN: (纸本)9798350362480

Legal Artificial Intelligence has emerged as an essential field, focusing on AI technologies that facilitate various legal tasks and alleviate the workload of legal professionals. Despite advancements in Legal Artificial Intelligence, there remains a critical gap in systems that can provide both comprehensive and contextually accurate retrieval tailored to the intricate structure of EU legislation. We propose EuropeanLawAdvisor, an efficient and user-friendly legal information retrieval system designed to deliver tailored responses to legal queries. This system utilizes open-source Large Language Models within a Retrieval-Augmented Generation framework, facilitating precise and relevant information retrieval. The system employs a robust retrieval approach that integrates multi-match, k-nearest neighbors, hybrid methods, and TF-IDF search strategies across both complete documents and segmented text indexes, ensuring comprehensive retrieval for diverse query types. The implementation of the framework has demonstrated significant improvements in the accuracy and relevance of responses to EU legal queries, enhancing both the retrieval of relevant legal documents and the generation of precise responses. We show that EuropeanLawAdvisor, leveraging open-source models like Phi3-mini-3B and LLaMa-3-8B, achieves competitive Faithfulness and Relevance compared to GPT-4-Turbo. The performance gap narrows significantly in zero-shot scenarios, and our approach outperforms GPT-4-Turbo in the percentage of answered questions. We publicly release our code on GitHub: https://***/raffaele-russo/EuropeanLawAdvisor. © 2024 IEEE.

关键词： Nearest neighbor search

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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Wind Turbine Efficiency Under Altitude Consideration Using an Improved Particle Swarm Framework

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computers, Materials & Continua 2022年第12期73卷 4981-4994页

作者： Haykel Marouani Fahad Awjah Almehmadi Rihem Farkh Habib Dhahri Department of Applied Mechanical Engineering College of Applied EngineeringMuzahimiyah BranchKing Saud UniversityP.O.BOX 800Riyadh11421Saudi Arabia Department of Applied Electrical Engineering College of Applied EngineeringMuzahimiyah BranchKing Saud UniversityP.O.BOX 800Riyadh11421Saudi Arabia Department of Information Science College of Applied Computer SciencesMuzahimiyah BranchKing Saud UniversityP.O.BOX 800Riyadh11421Saudi Arabia

In this work,the concepts of particle swarm optimization-based method,named non-Gaussian improved particle swarm optimization for minimizing the cost of energy(COE)of wind turbines(WTs)on high-altitude sites are *** the COE depends on site specification constants and initialized parameters of wind turbine,the focus was on the design optimization of rotor radius,hub height and rated *** on literature,the COE is converted to the Saudi Arabia ***,the constrained wind turbine optimization problem is ***,non-Gaussian improved particle swarm optimization is provided and compared with the conventional particle swarm optimization for solving the optimization design in wind turbine efficiency under different altitudes ranging from 2500 to 4000 *** results show that as altitude rises,the optimal rotor radius grows,but the optimal hub height and rated power drop,resulting in an increase in ***,the non-Gaussian method display a faster convergence compared to the classical particle swarm *** findings will be useful as a reference for wind turbine design at high ***,it could be employed to optimize the initialized parameter of wind turbine for the planned and largest wind farm in Saudi Arabia in Dumat Al-Jandal selected site.

关键词： Wind turbine high altitude energy cost particle swarm optimization Levy distribution

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Machine Learning and its Application in Food Safety 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Rahul, Kumar Banyal, Rohitash Kumar Arora, Neeraj Department of Basic and Applied Sciences NIFTEM Kundli Sonepat131028 India Department of Computer Science and Engineering Rajasthan Technical University Kota324010 India School of Science and Technology Vardhman Mahaveer Open University Kota324010 India

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

Food safety is an important issue nowadays. Now, people become more aware of food safety and health concerns as their lifestyles have changed they follow more precautions for food consumption. The conventional technique of food safety is not capable of ensuring food safety very precisely and has many limitations so the food business operators are facing economic losses. To resolve these limitations, the researchers and scientists developed AI-based integrated machine learning technology. Machine learning is a computational science that enables learning, reasoning, and decision-making by interpreting and analyzing statistical patterns and subject data. ML tools like sensors, electric nose, and tongue, smart indicators, line-scan hyperspectral imaging systems, RT-PCR, ELISA, etc. are capable of overcoming these limitations of food safety practices by using the ML algorithms. The ML technique has great potential to overcome food safety issues and future applications in the food sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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Depth and Pitch Control of Autonomous Underwater Glider Using Model Predictive Control with ARX Model

Depth and Pitch Control of Autonomous Underwater Glider Usin...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Jeremy, Jason Hidayat, Egi Muhammad Idris Siregar, Simon Trilaksono, Bambang Riyanto Habibullah, Natsir School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Telkom University Faculty of Applied Science Department Computer Engineering Bandung Indonesia

ISBN: (纸本)9798350381290

Autonomous Underwater Gliders (AUGs) are extensively developed vehicles capable of prolonged exploration and observation in complex marine environments. Control of the AUG is challenging due to its slow response system and its constraints. In this research, a linear model representation of AutoRegressive eXogenous input (ARX) will be constructed using input and output data from the AUG system, built Model Predictive Control (MPC), analyzed the performance, comparing with traditional Proportional-Integral-Derivative (PID). The objective is to enhance setpoint tracking accuracy and minimize energy to extend exploration time while faced with constraints. Simulation results reveal MPC exhibits potential setpoint tracking, low overshoot as low as 0.6% up to 0.3m/s maximum depth rate, has relatively low input changes indicating good efficiency compared to PID. MPC approach effectively addresses slow response systems, managing momentum, and handling actuator constraints commonly encountered in AUG. © 2023 IEEE.

关键词： Model predictive control

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Energy efficient indoor localisation for narrowband internet of things

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1150-1163页

作者： Ismail Keshta Mukesh Soni Mohammed Wasim Bhatt Azeem Irshad Ali Rizwan Shakir Khan Renato RMaaliw III Arsalan Muhammad Soomar Mohammad Shabaz Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia Department of CSE University Centre for Research and Development Chandigarh UniversityMohaliPunjabIndia Department of Computer Science and Engineering National Institute of TechnologySrinagarIndia Asghar Mall College Rawalpindi Higher Education Department(HED)Govt.of the PunjabRawalpindiPakistan Department of Industrial Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Automation Electronics and Electrical EngineeringGdansk University of TechnologyGdańskPoland Arba Minch University Arba MinchEthiopia

There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops *** high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these *** maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a *** on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot *** a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is *** investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT *** current work primarily considers the NOMA Narrow Band IoT network under a single EH ***,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance *** simulation,the proposed approach is successfully *** improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.

关键词： artificial inteligence detection of moving objects internet of things

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A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

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computer Modeling in engineering & sciences 2024年第9期140卷 2131-2164页

作者： Madiha Hameed Aneela Zameer Muhammad Asif Zahoor Raja Department of Computer Sciences Pakistan Institute of Engineering and Applied SciencesNilore50700Islamabad Future Technology Research Center National Yunlin University of Science and TechnologyDouliou64002TaiwanChina Directorate of IT Services University of GujratGujrat50700Pakistan

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer *** datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis *** challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human *** cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left *** paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their *** notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion *** review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation *** explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset *** leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model ***,ViTs alleviate augmentation issues by effectively leveraging original *** a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and *** study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological ***,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic

关键词： Medical image skin cancer classification skin cancer segmentation international skin imaging collaboration convolutional neural network deep learning

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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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LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training

LPLgrad: Optimizing Active Learning Through Gradient Norm Sa...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Gul, Shreen Elmahallawy, Mohamed Madria, Sanjay Tripathy, Ardhendu Missouri University of Science and Technology Computer Science Department RollaMO65401 United States Washington State University School of Engineering & Applied Sciences RichlandWA99354 United States

ISBN: (纸本)9798350362480

Machine learning models are increasingly being utilized across various fields and tasks due to their outstanding performance and strong generalization capabilities. Nonetheless, their success hinges on the availability of large volumes of annotated data, the creation of which is often labor-intensive, time-consuming, and expensive. Many active learning (AL) approaches have been proposed to address these challenges, but they often fail to fully leverage the information from the core phases of AL, such as training on the labeled set and querying new unlabeled samples. To bridge this gap, we propose a novel AL approach, Loss Prediction Loss with Gradient Norm (LPLgrad), designed to quantify model uncertainty effectively and improve the accuracy of image classification tasks. LPLgrad operates in two distinct phases: (i) Training Phase aims to predict the loss for input features by jointly training a main model and an auxiliary model. Both models are trained on the labeled data to maximize the efficiency of the learning process - an aspect often overlooked in previous AL methods. This dual-model approach enhances the ability to extract complex input features and learn intrinsic patterns from the data effectively;(ii) Querying Phase that quantifies the uncertainty of the main model to guide sample selection. This is achieved by calculating the gradient norm of the entropy values for samples in the unlabeled dataset. Samples with the highest gradient norms are prioritized for labeling and subsequently added to the labeled set, improving the model's performance with minimal labeling effort. Extensive evaluations on real-world datasets demonstrate that the LPLgrad approach outperforms state-of-the-art methods by order of magnitude in terms of accuracy on a small number of labeled images, yet achieving comparable training and querying times in multiple image classification tasks. Our code is available at Github. © 2024 IEEE.

关键词： Active learning

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Efficient Models for Detecting Monkeypox using Skin Lesion Images 3

Efficient Models for Detecting Monkeypox using Skin Lesion I...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sunda, Neelam Sharma, Divya Battu, Sujith Banoth, Rewanth Nayak Rahul Monika Kanoria PG Mahila Mahavidyalaya Department of Computer Science Jaipur India Kanoria PG Mahila Mahavidhyalaya Department of Computer Science jaipur India Delhi Technological University Dept. of Software Engineering New Delhi India Delhi Technological University Department of Applied Physics New Delhi India University of Delhi Shaheed Rajguru College of Applied Sciences for Women Department of Computer Science India

ISBN: (纸本)9798350343632

Monkeypox is predicted to be the next pandemic and is spreading over African countries like water under a mat. The study basically investigates three deep learning imaging models for the identification of monkeypox from skin lesion photos from the MSID dataset. The motto is to define a computer-based technique to identify monkeypox from just its image, making it cheaper and faster with accurate results. The study achieved the highest accuracy of 99.37% as compared to previous works related to Resnet 50. The limits and complexity of the procedures in real-world situations are also clearly addressed. © 2023 IEEE.

关键词： Convolutional Neural Network Image preprocessing Monkeypox Performance Evaluation Metrics Transfer learning

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