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Feature Selection for Binary Dataset using Dragonfly Algorithm

Feature Selection for Binary Dataset using Dragonfly Algorit...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Zaid Tariq Raouf Dhafar Hamed Abd Informatics Institute for Postgraduate Student Baghdad Iraq College of Computer Science and Information Technology University of Anbar Ramadi Iraq

In contemporary times, the proliferation of data dimensionality has introduced many challenges within the realm of machine learning. Consequently, identifying and selecting pertinent features have assumed paramount importance. Consequently, a diverse array of techniques for feature selection has been proffered. Among them, Metaheuristic techniques are particularly significant within this milieu, with a focus on the dragonfly algorithm garnering notable attention. Metaheuristic algorithms are rooted in the emulation of swarm intelligence, drawing inspiration from the collective behaviors exhibited by insects. This scholarly inquiry aims to enhance the efficacy of classification outcomes by leveraging the dragonfly algorithm to select the most salient features. In this paper, the proposed approach is rigorously evaluated across three distinct datasets, namely, “Ansur,” “Predict 5-Year Career Longevity for NBA Rookies,” and “Chronic Kidney Disease,” all of which were procured from the Kaggle repository. The study used four machine learning classifiers, namely, XGBoosting, K-Nearest Neighbors (KNN), Decision Tree, and Gaussian Naive Bayes (Gaussian-NB), for classification and evaluation. The empirical findings unveiled a notable enhancement in classification performance. Notably, the accuracy metrics exhibit substantial improvements across all classifiers. For instance, within chronic kidney disease classification, both XGBoost and Decision Tree classifiers yielded a remarkable accuracy rate of 100%, thereby underscoring the efficacy of the proposed dragonfly algorithm-based feature selection technique in augmenting the predictive capabilities of machine learning models.

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CoDescribe: An Intelligent Code Analyst for Enhancing Productivity and Software Quality 10th

CoDescribe: An Intelligent Code Analyst for Enhancing Produ...

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10th International Symposium on Applied Computing for Software and Smart systems, ACSS 2023

作者： Das, Souvick Deb, Novarun Cortesi, Agostino Chaki, Nabendu Department of Environmental Science Informatics and Statistics Ca’ Foscari University Venezia30172 Italy Indian Institute of Information Technology Vadodara Gujrat Gandhinagar382028 India Department of Computer Science and Engineering University of Calcutta 67 Sukanta Park Kolkata700101 India

ISBN: (纸本)9789819977826

Automating the documentation, explanation, and modification of source code has been an area of intense research interest for several decades. This paper proposes a novel framework for analyzing source codes to generate natural language summaries, answer complex user queries by reasoning, and even support syntactic error checking and code conversion to other languages. The framework incorporates transformer-based models and knowledge graphs to understand the context of the code and the user’s queries. The contributions of this proposed approach represent a significant leap forward by overcoming the limitations of the most advanced ChatGPT model. The framework ensures the accuracy and reliability of the responses while handling multiple source code files efficiently, making it a valuable asset for software developers and stakeholders. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Automated Fire Hydrant Monitoring and Maintenance using IoT Data Analytics and Naive Bayes Classifier

Automated Fire Hydrant Monitoring and Maintenance using IoT ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sarasu, R. Nandagopal, V. Shibu, N.V. Lakshminarayanan, Suresh Raj, T Saju Rajmohan, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics Chennai India Mohan Babu University School of Engineering Department of Electrical and Electronics Engineering Tirupathi India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India University of Technology and Applied Sciences Department of Engineering & Electronics South Al Batina Oman Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350375442

This paper introduces a new method for managing fire hydrants that uses a monitoring system based on the Internet of Things (IoT) and a Naive Bayes Classifier (NBC) for predictive maintenance. Water pressure, flow rate, and the physical state of fire hydrants are among the essential factors for the proposed system continually collecting data using an IoT sensor network. The system analyzes real-time data using NBC. These predictive capabilities reduce the potential of hydrant breakdowns during emergencies through preventive maintenance potential. It introduces a reliable IoT infrastructure for fire hydrant monitoring, improving the acquired data's granularity and timeliness. NBC provides a powerful instrument for early problem detection, significantly improving maintenance effectiveness. Since the technology is compatible with existing fire department procedures, it can easily integrate into current operational processes. Better public safety, more efficient use of resources, and more dependable fire hydrants are potential outcomes of responsive and proactive data-driven maintenance techniques. The findings improve overall fire safety and operational efficiency, with a 25% decrease in maintenance expenditures and a 40% increase in issue detection accuracy. © 2024 IEEE.

关键词： Predictive maintenance

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Distributed Gradient Clustering: Convergence and the Effect of Initialization 58

Distributed Gradient Clustering: Convergence and the Effect ...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Armacki, Aleksandar Sharma, Himkant Bajović, Dragana Jakovetić, Dušan Chakraborty, Mrityunjoy Kar, Soummya Carnegie Mellon University PittsburghPA United States Indian Institute of Technology Kharagpur Kharagpur India University of Novi Sad Faculty of Technical Sciences Department of Power Electronics and Computer Engineering Novi Sad Serbia University of Novi Sad Faculty of Sciences Department of Mathematics and Informatics Novi Sad Serbia

ISBN: (纸本)9798350354058

We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains a local dataset and communicates only with its immediate neighbours, with the aim of finding a global clustering of the joint data. We perform extensive numerical experiments, evaluating the effects of center initialization on the performance of our family of methods, demonstrating that our methods are more resilient to the effects of initialization, compared to centralized gradient clustering [2]. Next, inspired by the K-means++ initialization [3], we propose a novel distributed center initialization scheme, which is shown to improve the performance of our methods, compared to the baseline random initialization. © 2024 IEEE.

关键词： K-means clustering

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A Hybrid Localization Algorithm of RSS and TOA Based on Adaptive Weight Errors

A Hybrid Localization Algorithm of RSS and TOA Based on Adap...

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Information technology, Applied Mathematics and Statistics (ICITAMS), International Conference on

作者： Suphian Mohammed Tariq Intisar Shadeed Al-Mejibli Computer Engineering Informatics Institute for Postgraduate Iraqi Commission for Computers & Informatics College Of Engineering Al-Iraqia University Baghdad Iraq Biomedical Informatics College University of Information Technology and Communications Baghdad Iraq

ISBN: (数字)9798350335118

ISBN: (纸本)9798350335125

Specifying the location of wireless sensor devices is vital in many applications such as target tracking, remote environmental monitoring, and more. Time of arrival (TOA), the received signal strength indicator (RSSI) and range information are applied to locate the wireless sensor network (WSN) node’s location. Still, they suffer inaccuracy because of environmental factors in the 2D region. A ranging method has been suggested, merging the TOA information domain with RSSI to accurately determine the distance between the unknown node and the anchor node. This approach relies on utilizing RSSI and TOA ranging techniques in the near and far endings. We combine received signal strength indication and arrival time in parallel style as major elements. The findings of the two methods are then combined with the error percentage to determine the final location using adaptive weight errors. The proposed range method is promoted in this work to enhance the accuracy of the positioning. The proposal has been implemented by MATLAB simulation and the results show improving positioning accuracy compared to the conventional TOA and RSSI ranging method. The mean square error in the proposed method is 0.5044, whereas it was 0.7032 and 0.8102 in RSSI, respectively. This indicates that the suggested approach is more efficient and performs better for localization than the other two methods.

关键词： Location awareness Wireless communication Wireless sensor networks Target tracking Simulation Merging Mean square error methods

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Harnessing Deep Learning for Effective Extractive Text Summarization: A Comparative Study 2

Harnessing Deep Learning for Effective Extractive Text Summa...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Alsekait, Deema Mohammed Almanaseer, Waref Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Baligh, Salma Alaa, Hana Hegazy, Jasmine Magdy, Julia Eldmrat, Alaa Tariq Abdelminaam, Diaa Salama Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information Technology Saudi Arabia Applied Science Private University Faculty of Information Technology Amman11931 Jordan Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Midocean University Faculty of Human Sciences United Arab Emirates Jadara University Jadara Research Center Irbid21110 Jordan Misr International University Egypt MEU Research Unit Middle East University Faculty of Computers Amman11831 Jordan

ISBN: (纸本)9798350362633

Text Summarization might seem like a trivial problem, but in reality, is essential for data acquisition and understanding by compressing large amounts of text into specific a nd to-the-point summaries. This research is conducted to help compare the proposed methods' applications and make a summary system accessible to applications such as information retrieval, legal documents summarization and much more. from this, we intend to start by acquiring the dataset, preprocessing and training, and testing the models. In this paper will explore the methods that can be applied on summarization. These methods are Machine learning models like the random forest, naive Bayes, SVM, logistic regression, Deep learning like a neural networks, Transformers-based models like T5, and performance metrics like ROUGE. By trying all these various methods and models on a news article dataset we are going to compare the results from each of them and get the best performance. Finally, the results would be a comparison between all outputs and found that Natural Language Processor. In conclusion, Text Summarization is best done using NLP, and challenges in this project would be due to the diversity of text and context. © 2024 IEEE.

关键词： Contrastive Learning

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Commonsense reasoning with argumentation for cognitive robotics 15

Commonsense reasoning with argumentation for cognitive robot...

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15th International Rule Challenge, 7th Industry Track, and 5th Doctoral Consortium @ RuleML+RR 2021, RuleML+RR-Companion 2021

作者： Vassiliades, Alexandros Bassiliades, Nick Patkos, Theodore Aristotle University School of Informatics Thessaloniki Greece Foundation for Research and Technology Institute of Computer Science Heraklion Greece

Commonsense Reasoning is a cognitive ability which is found only in humans, but the desire is to implement it in Artificial Intelligence when solving tasks. Moreover, the use of arguments can reveal how and why a human individual supports or rejects an opinion. In this thesis, our goal is to study theoretically the problem of commonsense reasoning and develop methods for enhancing the commonsense reasoning capabilities of a cognitive robotic system that acts in a household environment. The commonsense reasoning mechanism is implemented with the use of argumentation, by developing argumentation frameworks that can facilitate commonsense knowledge. Additionally, we use Semantic Web technologies to add commonsense knowledge in the commonsense reasoning mechanism, and we construct a framework that accommodates our methods. © 2021 for this paper by its authors.

关键词： Semantic Web

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RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification

RerrFact: Reduced Evidence Retrieval Representations for Sci...

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2022 Workshop on Scientific Document Understanding, SDU 2022

作者： Rana, Ashish Khanna, Deepanshu Ghosal, Tirthankar Singh, Muskaan Singh, Harpreet Rana, Prashant Singh School of Business Informatics and Mathematics University of Mannheim Germany Department of Electronics & Communication Engineering Thapar Institute of Engineering & Technology India Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics Charles University Czech Republic Department of Computer Science & Engineering Thapar Institute of Engineering & Technology India

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate supporting and refuting evidence from credible scientific sources. The SciFact dataset and corresponding task provide a benchmarking leaderboard to the community to develop automatic scientific claim verification systems via extracting and assimilating relevant evidence rationales from source abstracts. In this work, we propose a modular approach that sequentially carries out binary classification for every prediction subtask as in the SciFact leaderboard. Our simple classifier-based approach uses reduced abstract representations to retrieve relevant abstracts. These are further used to train the relevant rationale-selection model. Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim. Experimentally, our system RerrFact with no fine-tuning, simple design, and a fraction of model parameters fairs competitively on the leaderboard against large-scale, modular, and joint modeling approaches. © 2022 Copyright for this paper by its authors.

关键词： Abstracting

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Zero-Shot Sentiment Analysis Exploring BART Models

Zero-Shot Sentiment Analysis Exploring BART Models

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Big Data, Cloud Computing, Data Science & Engineering (BCD)

作者： Konstantinos Kyritsis Isidoros Perikos Michael Paraskevas Electrical and Computer Engineering Dept. Computer Technology Institute and Press "Diophantus" University of Peloponnese Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

The BART model is an advanced adaptation of transformers introduced by Facebook. It has incorporated elements from both BERT and GPT transformers, enabling significant advancements in language understanding and general speech processing. Utilizing both encoder and decoder components, BART proves versatile for various tasks, including translation, text completion, automatic sentence generation, entity recognition, sentiment analysis, and more. In this study, we focus on the study of pretrained models, BART and a modified version called distilbart, in the context of Zero-Shot Text Classification. In the experimental study we dive into the Zero-Shot technique applied to various pretrained Transformers. Our analysis demonstrates that, depending on the Model we utilize, we can achieve F1 scores of up to 88%, showcasing the model's effectiveness in discerning classes for this Sentiment Analysis problem using the Zero-Shot Text Classification technique.

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A Framework for Big Data Analytics with Wireless Communication of Network, Internet of Things and Cyber Security 2

A Framework for Big Data Analytics with Wireless Communicati...

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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Tedyyana, Agus Sivakumar, G. Kumar, Anuj Velayudham, A. Pavithra, M. Gangodkar, Durgaprasad Politeknik Negeri Bengkalis Department of Informatics Engineering Indonesia Electronics and Communication Engineering Ramco Institute of Technology Rajapalayam India Uit Uttaranchal University Dehradun India Jansons Institute of Technology Department of Computer Science and Engineering Karumathampatti Coimbatore India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

Nowadays, big data is being implemented in various fields due to its advantages in risk management, healthcare and other business fields. The conventional big data analytics system is not highly promising for implementation in every system. Therefore, the developers are integrating the Internet of Things (IoT), wireless communication and cyber security systems to strengthen the capability of big data analytics. The current research has explained the framework for big data analytics with IoT, wireless communication and cyber security. Initially, past literature was explained, and then, the research methodology has been described. Primary survey-based research has been conducted with 54 data analysis experts to understand the framework of big data, its applications and solutions for cyber security-based issues. Findings showed that a VPN system with a firewall is a great choice for preventing external attacks. Moreover, as the primary data suggested the use of encryption systems, the researchers used SHA-256 ('Secure Hashing Algorithm') for protecting the collected big data. Internet of Things (IoT) devices is used for collecting customer and operational data. After that, the data is collected via a wireless network and transferred to the respective access points. Users can access the data from access points and can generate meaningful information for executing various purposes. © 2022 IEEE.

关键词： Data Analytics

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