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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Kumar, M Prema Ashfaq Ahmed, K. Subash, K. Aeron, Anurag Department of Ece Andhra Pradesh Bhimavaram India Department of Emerging Technologies in Computer Science Andhra Pradesh Kurnool India Department of Data Science Trichy India Miet Meerut Department of Computer Science and Engineering Meerut India

ISBN: (纸本)9798350384277

Early detection dramatically increases the survival rate of oral cancer (OC). Artificial intelligence (AI) technology has garnered more attention in the field of diagnostic medicine in present periods. This study set out to assess the available data regarding AI's efficacy in OC diagnosis critically. Artificial intelligence diagnostic accuracy and capacity to detect early phases of OC were highlighted. In this project, performance indicators will be measured and oral cancer will be divided and classified using intelligent computing techniques. The role of oral cancer classification and detection to achieve a high recognition rate while leveraging the best theoretical components of oral cancer images, a newly established region-based Convolutional Neural Network (RCNN-COA) and the Chimp Optimization Algorithm were used to improve a Deep Learning Method. Then, using the recommended Chimp Optimization Algorithm (COA), a region-based convolutional neural network (R-CNN) classifier was trained using the acquired theoretical properties and the innovative image. A comparison of many deep learning and machine learning models' performances has been reported in a study. The findings imply that the deep learning model is capable of managing the problematic task of early oral malignant tumor detection. © 2024 IEEE.

关键词： Contrastive Learning

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Thermal-energy-efficient-secured-link reliable and delay-aware routing protocol (TESLDAR) for wireless body area networks

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International Journal of computers and Applications 2024年第9期46卷 715-727页

作者： Mohanty, Rasmita Kumari Sahoo, Satya Prakash Kabat, Manas Ranjan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla India

Wireless Body Area Networks (WBANs) have emerged as a transformative technology for health monitoring, leveraging sensor nodes placed on or within the human body. This paradigm offers real-time insights into physiological parameters, enabling remote healthcare applications and enhancing patient well-being. However, the WBANs bring challenges that demand innovative solutions to ensure their seamless integration into healthcare ecosystems. Secondly, trustworthiness becomes paramount in healthcare applications, where the sensitivity of health-related data necessitates secure and reliable communication. Lastly, congestion issues, arising from bandwidth constraints and concurrent data transmissions, affect the performance of WBANs, impacting data integrity, energy consumption, and quality of service. Therefore, this paper proposes a TESLDAR protocol that incorporates the proposed Jellyfish Updated Aquila Narrow ExploExpand (JUANE) method aims to address issues such as hotspot-induced thermal stress, trustworthiness concerns, and congestion challenges. The proposed algorithm incorporates Jellyfish Optimization (JFO), inspired by the adaptable movement of jellyfish, with characteristics inspired by the Aquila bird. The method's innovative integration of jellyfish and bird-inspired behaviors positions it as a promising solution for efficient route maintenance in WBANs addressing thermal, trust, and congestion challenges for enhanced reliability in healthcare monitoring applications. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Energy efficiency

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Revealing inputs causing web API performance latency using response-time-guided genetic algorithm fuzzing

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Artificial Life and Robotics 2024年第4期29卷 459-472页

作者： Huang, Ying-Tzu Lee, Shin-Jie Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

Web APIs are integral to modern web development, enabling service integration and automation. Ensuring their performance and functionality is critical, yet performance testing is less explored due to the difficulty in detecting performance bugs. This paper presents a response time-guided genetic algorithm (GA) fuzzing approach to uncover web API performance latency in a black-box setting. Unlike traditional random input generation, our method uses GA to refine inputs through crossover and mutation, guided by response time-based fitness. We propose two seed generation methods: pairwise combinatorial testing using Mircosoft’s Pairwise Independent Combinatorial Testing (PICT) and randomly paired combinations. We compared our method with classic random fuzzing. Experiments on five real-world web APIs show that our approach significantly outperforms classic random fuzzing, identifying inputs with response times 1.5 to 26.3 times longer. Additionally, PICT-generated seeds demonstrated superior performance compared to randomly-paired combinations in 2 out of 5 APIs. Our findings highlight the potential of GA-based fuzzing to reveal web API performance latency, advocating for further research in this area. © International Society of Artificial Life and Robotics (ISAROB) 2024.

关键词： Genetic algorithms

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Minimum spanning tree cycle intersection problem on outerplanar graphs

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Discrete Applied Mathematics 2024年 343卷 328-339页

作者： Su, Tai-Yu Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan

We study the minimum spanning tree cycle intersection (MSTCI) problem on outerplanar graphs in this paper. Consider a connected simple graph G=(V,E) and any spanning tree T=(V,ET) of G, it is well-known that each non-tree edge e∈E∖ET induces a fundamental cycle in T∪{e}. We say that two distinct fundamental cycles intersect if they share at least one edge. The MSTCI problem seeks a spanning tree such that the number of pairwise cycle intersections is minimized. We provide a linear-time algorithm to solve the problem on outerplanar graphs by exploiting the cycle-cut duality on plane graphs. Besides, we show that the minimum cycle intersection number is bounded by O(n) for outerplanar graphs with n vertices. © 2023 Elsevier B.V.

关键词： Graphic methods

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Improved optimizer with deep learning model for emotion detection and classification

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Mathematical Biosciences and engineering 2024年第7期21卷 6631-6657页

作者： Joseph, Willson C. Kathrine, G. Jaspher Willsie Shanmuganathan, Vimal Sumathi, S. Pelusi, Danilo Valencia, Xiomara Patricia Blanco Verdú, Elena Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Adi Shankara Institute of Engineering and Technology Kerala India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Sri Eshwar College of Engineering Coimbatore India Communication Sciences University of Teramo Coste Sant’agostino Campus Teramo Italy Universidad Internacional de La Rioja La Rioja Logroño Spain

Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Nonetheless, existing methods are mostly unable to offer correct predictions with a minimum error rate. In this paper, an innovative facial emotion recognition framework, termed extended walrus-based deep learning with Botox feature selection network (EWDL-BFSN), was designed to accurately detect facial emotions. The main goals of the EWDL-BFSN are to identify facial emotions automatically and effectively by choosing the optimal features and adjusting the hyperparameters of the classifier. The gradient wavelet anisotropic filter (GWAF) can be used for image pre-processing in the EWDL-BFSN model. Additionally, SqueezeNet is used to extract significant features. The improved Botox optimization algorithm (IBoA) is then used to choose the best features. Lastly, FER and classification are accomplished through the use of an enhanced optimization-based kernel residual 50 (EK-ResNet50) network. Meanwhile, a nature-inspired metaheuristic, walrus optimization algorithm (WOA) is utilized to pick the hyperparameters of EK-ResNet50 network model. The EWDL-BFSN model was trained and tested with publicly available CK+ and FER-2013 datasets. The Python platform was applied for implementation, and various performance metrics such as accuracy, sensitivity, specificity, and F1-score were analyzed with state-of-the-art methods. The proposed EWDL-BFSN model acquired an overall accuracy of 99.37 and 99.25% for both CK+ and FER-2013 datasets and proved its superiority in predicting facial emotions over state-of-the-art methods. ©2024 the Author(s), licensee AIMS Press.

关键词： Emotion Recognition

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Enhancing E-Commerce Text Classification: A GRU-Based Approach for Improved Product Understanding 5th

Enhancing E-Commerce Text Classification: A GRU-Based Approa...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Nazmul Abdal, Md. Shaown, Shahanaz Islam Islam, Khairum Aman, Rutba Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Uttara University Dhaka Bangladesh Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9789819791118

In the burgeoning landscape of e-commerce, the ability to accurately classify product texts is paramount for enhancing user experience and driving business success. Traditional approaches to text classification often struggle with the nuances and complexities inherent in e-commerce product descriptions. In this paper, we propose a novel approach utilizing Gated Recurrent Unit (GRU) to address these challenges and improve product understanding in e-commerce text classification tasks. Our model leverages the inherent sequential nature of product descriptions, effectively capturing long-range dependencies and semantic relationships within the text. We use a standard dataset in extended trials to demonstrate the superiority of our GRU-based approach over conventional methods in terms of classification accuracy and robustness across diverse product categories. Furthermore, we conduct comprehensive analyses to gain insights into the inner workings of our model and its ability to learn meaningful representations of e-commerce text data. The performance of the model is compared using several cutting-edge techniques, including Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) in order to show that our model is superior at correctly classifying e-commerce texts. The experimental findings show that the suggested model performs competitively in classifying e-commerce texts, surpassing other approaches with an accuracy of 98.35%. Our findings underscore the potential of GRU-based approaches for advancing the state-of-the-art in e-commerce text classification, offering promising avenues for future research and practical applications in the domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Marketplaces

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Artificial Intelligence-Driven Approach for Predicting Maternal Health Risk Factors 9

Artificial Intelligence-Driven Approach for Predicting Mater...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Hosaain, Mohammad Mobarak Kashem, Mohammod Abdul Nayan, Nasim Mahmud Department of Computer Science and Engineering Gazipur1707 Bangladesh Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350342482

The development of machine learning has the potential to significantly improve the identification and treatment of pregnancy-related risks in maternal health. This work uses an extensive dataset to create reliable models that can predict possible health issues with accuracy and reliability. Our models are highly predictive and have transparency and interpretability in their results because of our creative feature engineering, such as featurewiz and chi-square test, and meticulous data processing. The models underwent extensive validation procedures, and in three different categories, each proved better evaluation metrics results alongside accuracy levels above 99% in each class. Ensuring the durability and stability of the models, this remarkable degree of accuracy is further enhanced by ensemble techniques and hyperparameter tuning. Incorporating Explainable AI (XAI) methods, such as LIME and SHAP, has proven crucial in elucidating the models' decision-making process, fostering trust, and enabling their possible use in healthcare environments. The results highlight the potential of ML in predicting maternal health risks and establishing solid foundations for future integration with Medical Cyber-Physical Systems (MCPS), which has the potential to transform the field of predictive analytics in prenatal care completely. © 2024 IEEE.

关键词： Adversarial machine learning

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On building automation system security

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High-Confidence Computing 2024年第3期4卷 103-122页

作者： Christopher Morales-Gonzalez Matthew Harper Michael Cash Lan Luo Zhen Ling Qun Z.Sun Xinwen Fu Department of Computer Science University of Massachusetts LowellLowell 01854USA Department of Electrical and Computer Engineering University of Central FloridaOrlando 32816USA School of Computer Science and Technology Southeast UniversityMa’anshan 243032China School of Computer Science and Engineering Anhui University of TechnologyNanjing 211189China

Building Automation Systems(BASs)are seeing increased usage in modern society due to the plethora of benefits they provide such as automation for climate control,HVAC systems,entry systems,and lighting *** BASs in use are outdated and suffer from numerous vulnerabilities that stem from the design of the underlying BAS *** this paper,we provide a comprehensive,up-to-date survey on BASs and attacks against seven BAS protocols including BACnet,EnOcean,KNX,LonWorks,Modbus,ZigBee,and *** studies of secure BAS protocols are also presented,covering BACnet Secure Connect,KNX data Secure,KNX/IP Secure,ModBus/TCP Security,EnOcean High Security and Z-Wave *** and ZigBee do not have security *** point out how these security protocols improve the security of the BAS and what issues remain.A case study is provided which describes a real-world BAS and showcases its vulnerabilities as well as recommendations for improving the security of *** seek to raise awareness to those in academia and industry as well as highlight open problems within BAS security.

关键词： Building automation system BAS protocols Security Attack

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Traffic Rerouting Optimization Using Scheduling Algorithms

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SN computer science 2024年第6期5卷 762页

作者： Garg, Tanya Kaur, Gurjinder Rana, Prashant Singh Department of Computer Science & amp Engineering Sant Longowal Institute of Engineering & amp Technology Computer Science & amp Engineering Department Thapar Institute of Engineering & amp Technology

Traffic rerouting is a technique used to optimize traffic flow and reduce congestion by redirecting vehicles to alternate routes. The work done in this research focuses on a specific case scenario covering a 25 km distance with initially consisting of 25 red lights and the impact of increased number of red lights on average waiting time with the variable number of vehicles over different number of iterations using K-Fold cross-validation and variable speeds of the vehicles have been analyzed. The study compares the results for the average waiting time and shortest path length experienced by vehicles without any scheduling and after scheduling. The distance to be traveled successfully minimized, with values ranging from 734.36 min to 33.68 min for vehicles travelling with fixed speed of 40 km/h and 30.43 min from 500.34 min for variable speed vehicles in range of 30 km/h to 60 km/h using Adaptive Scheduling. In minimizing the distance traveled, the QoS Algorithm performed best by providing the shortest alternate route for both the fixed and variable speed of vehicles. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Average waiting time Delay QoS (Quality of Service) Scheduling algorithms Shortest distance Traffic lights Traffic rerouting

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A Comparative Study of C Compilers: Evaluating Operator Precedence and Associativity in Compilers 5

A Comparative Study of C Compilers: Evaluating Operator Prec...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kumar, Lalit Sharma, Isha Department of Computer Science and Engineering Ambala Haryana Mullana133207 India CodeQuotient Department of Computer Science and Engineering

ISBN: (纸本)9798350379945

Operator precedence and associativity are fundamental concepts in the C programming language that govern the order of evaluation of expressions. Understanding these principles is essential for writing correct and efficient C code. The paper also discusses potential pitfalls and best practices for leveraging these concepts effectively. This research paper aims to compare three popular C++ compilers, namely Turbo compiler, Dev C Compiler, and online compilers (programiz, onlineGDB), with respect to their handling of operator precedence and associativity in C. Through a series of experiments and analyses, we will examine how these compilers interpret complex expressions and their impact on code execution. This study will provide insights into potential differences and implications of using different compilers, thus aiding developers in making informed decisions when choosing a C++ compiler for their projects. © 2024 IEEE.

关键词： Program compilers

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