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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Ali, Hassan Imtiaz, Hussain Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Alshammeri, Menwa Mir, Azka Kumar, Himanshu University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait College of CIS Al Jouf University Department of Computer Science Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Alzheimer's disease is a neurodegenerative disorder in which central nervous cells gradually die that poses a significant health challenge globally, especially in older people. As life expectancy increases which suggests a large amount of society will be affected. This increasing number of Alzheimer's cases demands the urgency to develop predictive models for early intervention as the real effect of Alzheimer's shows very late. Although there are no treatments capable of reversing the natural pathological changes, we can delay the development of Alzheimer's. Our goal is to, detect this neurodegenerative disease before it becomes more rooted, we can help the patient identify the disease at an early age so they can adapt to their new condition and perform treatments to help manage symptoms. We used the OASIS dataset which consists of multiple features collection of subjects whose age varies from 18 to 96. This research focuses on employing supervised machine learning, we have used various classifiers such as Decision trees, Naïve Bayes, k-NN, random forest, and logistic regression, and evaluated each classification's effectiveness using several well-known performance indicators to forecast Alzheimer's disease status. We used Rapid Miner to test different models and found that the Naïve Bayes classifier achieves the highest accuracy at 97.5% this model has a superior predictive capability and outperforms alternative classifiers. The insights gained from this research contribute to advancing early diagnosis and intervention for Alzheimer's disease. © 2024 IEEE.

关键词： Alzheimer's disease Classification Health care Machine Learning OASIS Prediction

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The Sophisticated Prognostication of Migraine Aura Using Machine Learning 4

The Sophisticated Prognostication of Migraine Aura Using Mac...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Samiullah Rehman, Abdul Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Mohammad Mir, Azka Nirsanametla, Yadaiah University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait Lovely Professional University Division of Research and Development Punjab Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Migraine is one of the most disabling diseases in the world and impacting more than one billion individuals. The symptoms such as intensity, Nausea, Vomit, Phonophobia, Photophobia, Visual, Dysphasia, Dysarthria, Vertigo, Sensory, intense to sound, occurs before the migraine. Migraine drains the quality of persons' life. Furthermore, the study of this research was to train machine learning model on migraine aura dataset using different modern approaches that could assist medical patients before occurring migraine and could also give the indications related to migraine. Likewise, by applying random forest algorithm, we got 99.5 percent accuracy using google colab which will be enough to deploy on application for future Project. Moreover, we leveraged traditional framework to complete the research incorporating, Data collection (Online free Kaggle), Preprocessing, Classification, Model training and Comparison of models. As a result, our peak algorithm precision was including, Naive Bayes 94.52%, decision tree 98.3%, K-nearest neighbours 98.6%, random forest 99.5%, Similarly, Rapid miner and Google Colab software are used for the comparison of algorithms and best one is chosen. © 2024 IEEE.

关键词： Random forests

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AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs 37

AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Ve...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Cobb, Adam D. Roy, Anirban Elenius, Daniel Heim, F. Michael Swenson, Brian Whittington, Sydney Walker, James D. Bapty, Theodore Hite, Joseph Ramani, Karthik McComb, Christopher Jha, Susmit Computer Science Laboratory SRI International United States Mechanical Engineering Division Southwest Research Institute United States Intelligent Systems Division Southwest Research Institute United States Institute of Software Integrated Systems Vanderbilt University United States School of Mechanical Engineering Purdue University United States Department of Mechanical Engineering Carnegie Mellon University United States

ISBN: (纸本)9781713899921

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27, 714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details;a STandard for the Exchange of Product (STEP) model data;a 3D CAD design using a stereolithography (STL) file format;a 3D point cloud for the shape of the design;and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446, baseline models and code at https://***/SRI-CSL/AircraftVerse, and the dataset description at https://***/. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： computer aided logic design

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A Real-Time Optimization Model for Production Planning in Quarry Sites 8

A Real-Time Optimization Model for Production Planning in Qu...

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8th IEEE International Conference on Industrial engineering and Applications, ICIEA 2021

作者： Fattouh, Anas Bohlin, Markus Sundmark, Daniel Mälardalen University Division of Computer Science and Software Engineering Västerås Sweden

ISBN: (纸本)9781665428958

A quarry site is used to produce gravel with different granularities based on the customer's needs. The production process consists of different stages that need to be planned in order to achieve high productivity and low cost. During the operation of the quarry site, the operating parameters change, and the site productivity could be affected, therefore, a replanning of the production is needed. This paper presents a real-time optimization model for production planning in a quarry site. The optimization model consists of three modules, a productivity monitoring module, a simulation model, and an optimization algorithm. The productivity monitoring module produces the site productivity based on the current operating parameters of the quarry site. Once the site productivity reaches a specific threshold, the optimization algorithm is run on an updated simulation model to find the optimal values of the site parameters. The proposed real-time optimization model is applied to a real quarry site simulated on a high-fidelity simulator. The case study shows that the proposed optimization model can effectively improve the productivity of the quarry site and hence reduces the wastes and improve environmental sustainability. © 2021 IEEE.

关键词： Productivity

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Towards Automated Support for the Co-Evolution of Meta-Models and Grammars

arXiv

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arXiv 2023年

作者： Zhang, Weixing Department of Computer Science & Engineering Division of Interaction Design & Software Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden

Blended modeling is an emerging paradigm involving seamless interaction between multiple notations for the same underlying modeling language. We focus on a model-driven engineering (MDE) approach based on meta-models to develop textual languages to improve the blended modeling capabilities of modeling tools. In this thesis, we propose an approach that can support the co-evolution of meta-models and grammars as language engineers develop textual languages in a meta-model-based MDE setting. Firstly, we comprehensively report on the challenges and limitations of modeling tools that support blended modeling, as well as opportunities to improve them. Second, we demonstrate how language engineers can extend Xtext’s generator capabilities according to their needs. Third, we propose a semi-automatic method to transform a language with a generated grammar into a Python-style language. Finally, we provide a solution (i.e., GrammarOptimizer) that can support rapid prototyping of languages in different styles and the co-evolution of meta-models and grammars of evolving languages. © 2023, CC BY-NC-ND.

关键词： Modeling languages

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An Intelligent Technique for the Effective Prediction of Parkinson Disease 4

An Intelligent Technique for the Effective Prediction of Par...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Tariq, Sawera Qadeer, Madiha Rehman, Attique Ur Javed, Sabeen Ali, Tahir Muhammad Mir, Azka Singh, Suresh University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait Lovely Professional University Division of Research and Development Phagwara144411 India KKR KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Parkinson is a condition of the nervous system that is linked to breakdown of basal ganglia of the brain. This disorder is responsible for affecting the neurological system and other body parts that are nerve-controlled. Its symptoms vary from person to person, however common symptoms include Tremor, Slow Movement and Stiffness in Muscles, Impaired Posture, Speech Changes and Loss of Automatic Movements. The main cause of this disease is not known, that is why it is not curable and has no proven prevention yet. This research concerns the application of machine learning classifiers including KNN, Logistic Regression, Decision Tree, Naive Bayes and Random Forest for the detection of Parkinson's disease. We are using a Parkinson disease dataset using a machine learning algorithm for detection to determine the performance in terms of accuracy. In a nut shell, the Random Forest classifier, without using SMOTE, provided the highest accuracy among the models considered. However, this result and existing results as well are based on imbalanced class. This research is contributing valuably for the convergence of machine learning and healthcare for effective predictions of this disease using Smote for balancing out same dataset classes with accuracy of 98.7%. The results hold significance for future modifications in medical diagnostics for more accurate and effective detection methodologies. © 2024 IEEE.

关键词： Random forests

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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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Optimization Performance Analysis of Different Initialization Methods in Single-Objective and Multi-Objective Chaotic Evolution

Optimization Performance Analysis of Different Initializatio...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Xiaoyi Zhang Yan Pei Huiyan Jiang Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Japan Computer Science Division University of Aizu Aizuwakamatsu Japan College of Software Northeastern University Shenyang China

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Chaotic Evolution (CE) is a significantly faster and more robust method for solving single-objective and multi-objective optimization problems. However, there are various factors that can impact the performance of CE, with population initialization being one of them. A well-executed initialization method can contribute to the discovery of better solutions and enhance both convergence rate and accuracy. This paper conducts a comparative experiment on CE algorithms using six different initialization methods. The performance of the CE algorithms utilizing these various initialization methods is analyzed and discussed. To assess convergence rate and accuracy, eight single-objective optimization benchmark functions and five multi-objective optimization benchmark functions are employed in our analysis. The evaluation results reveal significant disparities among the six initialization methods, indicating that different approaches to initialization have a substantial influence on the optimization performance of the CE algorithm.

关键词： Accuracy Benchmark testing Performance analysis Optimization Convergence

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A Twin Dual-Arm Robot Testbed to Collect the Human Demonstration Data for Imitation Learning*

A Twin Dual-Arm Robot Testbed to Collect the Human Demonstra...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Juhyung Kim Sungwon Heo Songwoo Kim Ho-Jin Jung Han Ul Yoon Division of Computer and Telecomm. Engineering Yonsei University Mirae Campus Wonju Korea Department of Computer Science Graduate School Yonsei University Wonju Korea Division of Software Yonsei University Mirae Campus Wonju Korea

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Collecting consistent human demonstrations has been a problematic issue in the field of a learning from demonstration. This paper presents a twin dual-arm robot testbed to collect human demonstration data while performing manual tasks in the activities of daily livings such as opening/closing a window, opening-up/closing-down a sauce bottle cap, etc. The testbed consists of the two sets of a dual-arm robot; for each robot, one-side arm has 6-DoF with a crab-gripper as its hand. Two dual-arm robots are mounted at the front and back of a table-type station. A human operator performs actions to complete a given task with the one at the back; the human operator's actions, meanwhile, are duplicated by the other robot at the front simultaneously. The experimental results show the testbed's potential to gather consistent human demonstration data for training a machine learning agent to manipulate the dual-arm robot under an imitation learning framework.

关键词： Training Imitation learning Manuals Grasping Manipulators 6-DOF Robots Sorting

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Delegating Responsibilities to Intelligent Autonomous Systems: Challenges and Benefits

arXiv

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arXiv 2024年

作者： Dodig-Crnkovic, Gordana Basti, Gianfranco Holstein, Tobias Division of Computer Science and Software Engineering Mälardalen University Västerås Sweden Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Pontifical Lateran University Vatican City Italy

MSC Codes K. Computing Milieux K.4 computerS AND SOCIETY K.4.1 Public Policy Issues. EthicsAs AI systems increasingly operate with autonomy and adaptability, the traditional boundaries of moral responsibility in techno-social systems are being challenged. This paper explores the evolving discourse on the delegation of responsibilities to intelligent autonomous agents and the ethical implications of such practices. Synthesizing recent developments in AI ethics, including concepts of distributed responsibility and ethical AI by design, the paper proposes a functionalist perspective as a framework. This perspective views moral responsibility not as an individual trait but as a role within a socio-technical system, distributed among human and artificial agents. As an example of 'AI ethical by design,' we present Basti and Vitiello's implementation. They suggest that AI can act as artificial moral agents by learning ethical guidelines and using Deontic Higher-Order Logic to assess decisions ethically. Motivated by the possible speed and scale beyond human supervision and ethical implications, the paper argues for 'AI ethical by design', while acknowledging the distributed, shared, and dynamic nature of responsibility. This functionalist approach offers a practical framework for navigating the complexities of AI ethics in a rapidly evolving technological landscape. © 2024, CC BY-NC-ND.

关键词： Public policy

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