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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Gupta, Kapil Raj, Aditya Gupta, Keshav Department of Computer Science and Engineering Sharda School of Engineering & Technology Sharda University Greater Noida India

ISBN: (纸本)9798350374629

Because of the negative effects fuel consumption has on the environment and the economy, car owners and operators are quite concerned about it. By anticipating fuel usage and understanding the variables that influence it, machine learning (ML) algorithms can beused to maximize fuel economy. This paper examines the optimization of fuel efficiency through the application of machine learning methods such as Random-Forest, Decision-trees and linear regression. An algorithm to manage the efficiency of fuel used by a vehicle by using multiple machine learning model such as Linear Regression, Decision-Tree, Random-Forest, XG-Boost for predicting the fuel consumption of the vehicles based on thegiven set of input values and predicting the output value which is the fuel consumption in Litre/100 km. For example, this paper used linear regression model, random forest regression model, decision tree regression model, and xg-boost model for predicting the fuel consumption of the Light Motor Vehicles (LMV) with an accuracy of 90.5%,99.97%, 99.96%, and 99.96% respectively. All things considered, ML algorithms might drastically increase fuel economy. To create and test different machine learning models for optimizing fuel efficiency in various car types and operating environments, further study is nonetheless required. ©2024 IEEE.

关键词： Decision trees

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A Practical Predefined-Time Stability Criterion and Its Application to Uncertain Nonlinear Systems

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Journal of Systems science & Complexity 2024年第6期37卷 2556-2578页

作者： XU Hao YU Dengxiu SUI Shuai XU Bowen CHEN C.L.Philip Unmanned System Research Institute Northwestern Polytechnical UniversityXi'an 710072China College of Science Liaoning University of TechnologyJinzhou 121001China School of Artificial Intelligence Optics and ElectronicsNorthwestern Polytechnical UniversityXi'an 710072China School of Computer Science and Engineering South China University of Technology and Pazhou LabGuangzhou 510641China

In this paper,the singularity-free predefined-time fuzzy adaptive tracking control problem is studied for non-strict feedback(NSF)nonlinear systems considering mismatched external *** innovative practical predefined-time stability(PPTS)criterion is proposed to provide the theoretical basis for subsequent control *** to the existing predefined-time stability(PTS)criterion,this criterion has a broader application range and can solve the control design issues of nonlinear systems with system *** logic systems(FLSs)are employed to identify unknown nonlinear *** on the backstepping control technology,a singularity-free predefined-time control(PTC)design method is proposed,in which the hyperbolic tangent function is utilized to avoid the singular problem,and the nature of fuzzy basis function is adopted to resolve the algebraic loop *** PPTS of the closed-loop NSF nonlinear system is proven with the Lyapunov theory and the proposed PPTS ***,the efficiency of the presented PTC design method is verified by several sets of simulations on a single link manipulator system.

关键词： Fuzzy adaptive control nonlinear systems practical predefined-time stability predefinedtime control singularity-free control

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Robust multi-domain descriptive text classification leveraging conventional and hybrid deep learning models

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International Journal of Information technology (Singapore) 2024年第5期16卷 3219-3231页

作者： Bhowmik, Shovan Sultana, Sharmin Sajid, Ahmed Arian Reno, Saha Manjrekar, Aakash Department of Computer Science and Engineering Bangladesh Army International University of Science and Technology Syedpur Bangladesh Department of Computer and Information Sciences University of Delaware Newark United States Department of Computer Science University of Massachusetts Lowell Lowell United States Vidyalankar Institute of Technology University of Mumbai Mumbai India

Since an unprecedented amount of online information in the form of unstructured texts is generated daily, researchers have started to focus on the development of robust mechanisms with the help of Natural Language Processing (NLP) and Artificial Intelligence (AI) to predict sentiments. Reliable sentiment classification models can not only avoid the spreading of misinformation but also help people to sieve genuine data by creating fact-checking tools on the user end. The main focus of this work is to investigate the performance of six conventional Deep Learning (DL) models followed by one hybrid DL model in three domains (fake news identification, spam filtering, and cyberbullying detection) of opinion mining tasks by employing efficient deep architectures capable of predicting sentiments even if long sentences are provided. Our empirical results show the rigid performance of all models on a corpus with maximum accuracy of 99.55%, 97.57%, and 93.00% for fake news, spam email, and cyberbullying domains accomplished by Convolutional Neural Network (CNN), Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) hybrid, and CNN models accordingly. Our work also exhibits a comparison of compilation times for every model to speed up the training time in this state-of-the-art. Contrary to the previous studies, we have taken long descriptive paragraphs for training purposes with a maximum length of 3336 words in a single sentence. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Artificial Neural Network Convolutional Neural Network Cyberbullying Fake News Sentiment Analysis

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Examining the Efficiency of Various Classification Algorithms in Spam Detection Datasets 15

Examining the Efficiency of Various Classification Algorithm...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Umar, Mohd. Gupta, Mahima Didwania, Rishabh Verma, Rajat Dhanda, Namrata Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India Amity University Department of Computer Science and Engineering Uttar Pradesh Lucknow India

ISBN: (纸本)9798350370249

This study investigates the relationship between the accuracy of Machine Learning (ML) models used to differentiate between Spam and Normal Messages for different dataset sizes. Efficiency evaluation of four classification models (Decision Tree, Passive Aggressive, Random Forest, and Regression) using 1500, 3000, and 5500 samples from three distinct dataset sizes is performed in this paper. The primary objective is to determine how variations in dataset size impact these models' accuracy. This work not only quantifies performance shifts but also offers insights into how responsive these models are to changes in data amount through careful experimentation and analysis. These findings enhance understanding of ML model behaviour in identifying unwanted messages and provide practical guidance for selecting and configuring models based on available dataset sizes. They also help to improve the design of trustworthy and accurate categorization systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Link Budget and Enhanced Communication Distance for Ambient Internet of Things

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ZTE Communications 2024年第1期22卷 16-23页

作者： YANG Yibing LIU Ming XU Rongtao WANG Gongpu GONG Wei School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China

Backscatter communications will play an important role in connecting everything for beyond 5G(B5G)and 6G *** open challenge for backscatter communications is that the signals suffer a round-trip path loss so that the communication distance is *** this paper,we first calculate the communication distance upper bounds for both uplink and downlink by measuring the tag sensitivity and reflection *** is found that the activation voltage of the envelope detection diode of the downlink tag is the main factor limiting the back-scatter communication *** on this analysis,we then propose to implement a low-noise amplifier(LNA)module before the envelope detection at the tag to enhance the incident signal *** experimental results on the hardware platform show that our method can increase the downlink communication range by nearly 20 m.

关键词： ambient IoT(AIoT) B5G backscatter communication link budget low-noise amplifier(LNA) Release 19 tag chip sensitivity upper bounds

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Nature-inspired whale optimization technique for efficient information exchange in vehicular networks 12

Nature-inspired whale optimization technique for efficient i...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Tandon, Righa Verma, Ajay Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Jaypee University of Information Technology Department of Computer Science and Engineering Solan India

ISBN: (纸本)9781665462617

Vehicle Ad-Hoc Networks (VANETs) offer roadside connectivity to facilitate user information exchange while driving. It connects other vehicles and hierarchical infrastructure components for applications including real-time traffic and vehicle control. Concord information sharing is needed for application answers because of the increase in vehicle and information density. In this paper, a secure information exchange model has been proposed using nature-inspired whale optimisation algorithm as a way to provide faster data transmission speeds and security. The proposed model makes use of a social behavior model of humpback whales to explore and exploit the search space for finding the optimal solution. Successful information selection and traffic information retrieval depend on the prior and present judgements. This keeps extra data from clogging up the traffic and vehicle management application while the driver is using it. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Arabic Topic Classification Corpus of the Nakba Short Stories 1

Arabic Topic Classification Corpus of the Nakba Short Storie...

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1st International Workshop on Nakba Narratives as Language Resources, Nakba-NLP 2025

作者： Hamed, Osama Zaidkilani, Nadeem Department of Computer Systems Engineering Palestine Technical University-Kadoorie Palestine Department of Computer Engineering and Security Mathematics University Rovira i Virgili Spain Department of Engineering and Technology Al-Zaytona University of Science and Technology Palestine

ISBN: (纸本)9798891762169

In this paper, we enrich Arabic Natural Language Processing (NLP) resources by introducing the "Nakba Topic Classification Corpus (NTCC)," a novel annotated Arabic corpus derived from narratives about the Nakba. The NTCC comprises approximately 470 sentences extracted from eight short stories and captures the thematic depth of the Nakba narratives, providing insights into both historical and personal dimensions. The corpus was annotated in a two-step process. One third of the dataset was manually annotated, achieving an IAA of 87% (later resolved to 100%), while the rest was annotated using a rule-based system based on thematic patterns. This approach ensures consistency and reproducibility, enhancing the corpus’s reliability for NLP research. The NTCC contributes to the preservation of the Palestinian cultural heritage while addressing key challenges in Arabic NLP, such as data scarcity and linguistic complexity. By like topic modeling and classification tasks, the NTCC offers a valuable resource for advancing Arabic NLP research and fostering a deeper understanding of the Nakba narratives. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Fabrication and Statics Performance of Pyramidal Lattice Stitched Foam Sandwich Composites

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computer Modeling in engineering & sciences 2021年第3期126卷 1251-1274页

作者： Yun Zhao Shi Yan Jiale Jia Department of Engineering Mechanics Harbin University of Science and TechnologyHarbinChina

In this study,the pyramidal lattice stitched foam sandwich composite materials were manufactured by integrating top and bottom panels with pyramidal lattice core to overcome the weak interface between the core and the skins of the sandwich *** influence of the reinforcing core rods on the mechanical properties including compressive,shear,and three-point bending performances of the foam sandwich compositematerialswere revealed through theoretical analysis and comparative *** theoretical predictions were consistent with the experimental *** test,shear test and three-point bending test were *** experimental results show that the core rods can significantly improve the compressive performance and energy absorption efficiency of the pyramidal lattice stitched foam sandwich *** effect is related to the diameter of the core *** core rod with large diameter has better *** with the foamsandwich structure,the pyramidal lattice reinforcing foam composites have stronger shear and bending *** failure modes and failure mechanisms of the pyramidal lattice stitched foam sandwich structure under the shear load are *** failure modes and failure mechanisms of the pyramidal lattice stitched foam sandwich structure under the three-point bending load are also *** study concludes that compared with the foam sandwich structure,the overall mechanical properties of the lattice stitched foam sandwich structure composites are significantly improved.

关键词： Pyramidal lattice compressive performance shear three-point bending failure modes

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Bug Classification Depend Upon Refactoring Area of Code

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Journal of The Institution of Engineers (India): Series B 2023年第1期104卷 61-77页

作者： Singh, Satwinder Jalal, Maddassar Kaur, Sharanpreet Department of Computer Science and Technology Central University of Punjab Punjab Bathinda India Department of Computer Science and Engineering Akal University Punjab Talwandi Sabo India Department of Computer Science Mata Gujri College Punjab Sri Fatehgarh Sahib India

Due to rapid development in the software industry, various software is being developed, which compromises the software quality. As long as time passes, the software which compromises with the quality starts showing bugs which adversely affect the working of the software system. Sometimes software undergo repeated addition of functionality, so various classes and functions in software systems become bulky and cause smells in code. Code smells also degrade the software quality, increasing the software system's maintenance cost. So these bad smells should be appropriately detected and eliminated from the software system well on time. With the identification of smells in code, one could easily map the refactoring area of code to improve the code. Improving the code will help in preventive maintenance to identify the bugs quickly. To solve this problem, the current study will focus on the five types of code smells: Data Class, God Class, Feature Envy, Refuse Parent Request and Brain method for identification of bugs in the code. The data set used for this study includes the smell extraction and identification from two versions of Eclipse, i.e. Eclipse 3.6 and Eclipse 3.7, which are renowned open soured industry size software systems. Later on, supervised machine learning classifiers j48, Random Forest, and Naive Bayes are used to identify the bugs in code at the class level. The results show that j48 performed well and provides high accuracy for the identification of bugs with the help of bad smells. © 2023, The Institution of Engineers (India).

关键词： Program debugging

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Malaria Detection Using Deep Learning Techniques 4

Malaria Detection Using Deep Learning Techniques

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Singh, Bhumi Dhanda, Namrata Verma, Rajat Amity University Uttar Pradesh Department of Computer Science and Engineering Lucknow India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350364729

Malaria, a deadly illness caused by Plasmodium parasites is usually diagnosed by a microscopic blood test. However, this manual process consumes an extended period and is liable to mistakes made by people. To address these challenges, models are trained on a comprehensive dataset containing either malaria-infected or uninfected blood samples. This study examines the effectiveness of various models of deep learning, particularly Convolutional Neural Networks (CNN), ResNet50, VGGNet19, and Inception V3, for the detection of malaria from blood smear images. The dataset is carefully curated to ensure a balanced representation of both classes, improving the ability of the Models can generalise successfully to new, previously unknown data. The ResNet50 is recognised for its deep residual learning framework, which has an accuracy of 94%, which has the highest accuracy. VGGNet19, known for its simplicity and depth, and Inception V3, known for its efficient multi-scale feature extraction, were all compared to a standard CNN. The results showed that these advanced architectures significantly outperformed traditional methods, providing a robust and reliable approach to malaria detection. This study mainly focuses on deep learning models to revolutionize the diagnostic process, making it faster, more accurate, and easier to use, especially in resource-constrained settings. © 2024 IEEE.

关键词： Convolutional neural networks

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