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2024 IEEE International Conference on Vehicular technology and Transportation Systems, ICVTTS 2024

作者： Vanajakshi, J. Renuka, A. Adesh, N.D. Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal India Manipal Academy of Higher Education Manipal Institute of Technology Department of Information and Communication Technology Karnataka Manipal India

ISBN: (纸本)9798350355512

Modern vehicles heavily rely on various Electronic Control Units (ECUs) to ensure proper operation, safety and efficiency. In-Vehicle Network (IVN) facilitates communication and the flow of information between ECUs. Commonly used IVN protocols include Controller Area Network (CAN), Local Interconnect Network (LIN), FlexRay, Ethernet, Media Oriented System Transport (MOST), and CAN with flexible data transfer (CAN-FD). Understanding the effect of these protocols on data generation, transfer, and ECU behaviour is critical to improving communication and enhancing the safety and security features of automotive communication systems. This work focuses on using the Vector CANoe tool to simulate real-world scenarios and analyse the complexities of IVN protocols. The research aims to improve automotive communication systems by examining the performance of these protocols in virtual environments. The findings from this work offer valuable insights for enhancing in-vehicle communications through simulation and testing. © 2024 IEEE.

关键词： Network security

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Addressing Vaccine Misinformation on Social Media by leveraging Transformers and User Association Dynamics 2

Addressing Vaccine Misinformation on Social Media by leverag...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Rao, Chirag Prabhu, Gautham Manuru Kumar, Ajay Rajendra Gupta, Shourya Shetty, Nisha P. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Information Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India

Vaccine hesitancy is a growing concern in public health, with increasing numbers of individuals expressing skepticism or outright refusal to receive vaccines. This factor was significantly highlighted during the COVID-19 pandemic, with large populations refusing to take the vaccine and prolonging the pandemic. This paper presents and compares two transformer-based approaches i.e. XLNET and BERT to classify vaccine misinformation on Twitter using the standard COVID-19 ANTi-Vax dataset. Subsequently, an analysis of vaccine discourse on Reddit is carried out following a user association mapping algorithm. The resultant graph was subsequently analyzed. The XLNET model outperformed BERT by showing a high accuracy of 0.9484, with an F1 score of 0.9353. The methodology can be used in multiple other scenarios to address concerns with regard to the usage of social media by analyzing network interactions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Vaccines

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Software Defect Prediction Using Deep Semantic Feature Learning

Software Defect Prediction Using Deep Semantic Feature Learn...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Dwarakanath, B. Renuka, S. Brindha, T.V. Ebinezer, M.J.D. Srm Institute of Science and Technology Ramapuram Information Technology College of Engineering and Technology Chennai India Vasavi College of Engineering Information and Technology Hyderabad India Sri Venkateshwara College of Engineering and Technology Computer Science and Engineering Tiruvallur India Koneru Lakshmaiah Education Foundation Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350313413

The software defect prediction (SDP) methodology has the potential to increase software reliability by foreseeing any ominous problems in its source code. Nevertheless, as recent research has demonstrated, creating defective prediction models is a challenging issue. Several research methods for predicting source code defects have been proposed over time. However, the majority of previous research has concentrated on traditional feature extraction and modelling. Traditional methods frequently fail to locate the contextual information needed to build reliable prediction deep learning models in source code files. On the other hand, defect prediction semantic feature techniques have only lately changed and advanced. Using such techniques, it would be possible to forecast suspicious problems by automatically extracting contextual information from source code files. We suggest employing deep learning, a potent representation-learning approach, to fill the gap between defect prediction data and semantics. In order to automatically extract semantic properties from word vectors generated by program abstract syntax trees (AST) for file-level defect prediction models and source code updates for change-level defect prediction models, we use a Long-Short Term Memory (LSTM) model. Two tasks - one for defect prediction at the file level (within-project defect prediction and cross-project defect prediction) and one for defect prediction at the change level (within-project defect prediction and cross-project defect prediction) - were used to assess our methodology. Our findings demonstrate that LSTM-AST-based semantic features can greatly enhance fault identification tasks. The findings demonstrate that the deep learning model LSTM-AST is a practical choice for medium to low sparsity ratio datasets and that, in comparison to other machine models, it performs typically better in high and extremely high sparsity ratio datasets. © 2023 IEEE.

关键词： abstract syntax trees (AST) Deep Learning (DL) Long-Short Term Memory (LSTM) Software defect prediction (SDP) source code

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Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence technology 2024年第4期9卷 932-947页

作者： Syed Konain Abbas Muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif R.Aljohani Ibrahim A.Hameed Muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

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KSMOTEEN: A Cluster Based Hybrid Sampling Model for Imbalance Class Data 6th

KSMOTEEN: A Cluster Based Hybrid Sampling Model for Imbalanc...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Dhamal, Poonam Mehrotra, Shashi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Information Technology Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9789819940707

Classification accuracy for imbalance class data is a primary issue in machine learning. Most classification algorithms result in insignificant accuracy when used over class imbalance data. Class imbalance data exist in many sensitive domains such as medicine, finance, etc., where infrequent events such as rare disease diagnoses and fraud transactions are required to be identified. In these domains, correct classification is essential. The paper presents a hybrid sampling model called KSMOTEEN to address class imbalance data. The model uses a clustering approach, the K-means clustering algorithm, and combines the SMOTEEN technique. The experimental result shows, the KSMOTEEN outperforms some existing sampling methods, thus improving the performance of classifiers for class imbalance data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Diagnosis

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Hybrid Routing Techniques for Location Privacy in IoT-Enabled Wireless Sensor Healthcare Networks

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SN computer Science 2024年第8期5卷 1164页

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Ambedka Beedi Karnataka Bengaluru 560001 India Department of Information Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Bengaluru 560064 India

Ensuring patient privacy is critical for healthcare systems reliant on the Internet of Things. While prior work has addressed patient privacy extensively, a comprehensive end-to-end strategy protecting patients’ location and data privacy under the assumption that system entities are untrusted has yet to be developed. In response, this paper proposes a novel hybrid Deep Q-learning Neural Network (DQ-NN) technique tailored to Source Location Privacy (SLP) integrated with Blockchain technology for routing in Internet of Things (IoT)-enabled Wireless Sensor Networks (WSNs). To overcome the shortcomings of the existing systems, the study offers extremely random routing paths between the source and sink nodes. The plan uses strategically selected phantom nodes to transmit packets to the sink node randomly, ensuring the paths are incredibly confusing to the enemy. To attain maximum privacy and optimize energy consumption, the suggested method employs a hybrid DQNN model to select optimal paths among multiple paths generated. Blockchain integration adds an extra layer of security to the routing network. The results of the simulation show that the proposed scheme outperforms other schemes by offering a more extended safety period, optimized energy, and enhanced privacy for the healthcare environment. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain for privacy Hybrid phantom routing Location privacy Neural network solutions Wireless sensor network privacy

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Active terahertz beam steering based on mechanical deformation of liquid crystal elastomer metasurface

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Light(Science & Applications) 2023年第1期12卷 118-127页

作者： Xiaolin Zhuang Wei Zhang Kemeng Wang Yangfan Gu Youwen An Xueqian Zhang Jianqiang Gu Dan Luo Jiaguang Han Weili Zhang Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineering Key Laboratory of Optoelectronic Information TechnologyMinistry of EducationTianjin University300072 TianjinChina Department of Electrical and Electronic Engineering Southern University of Science and Technology518055 ShenzhenChina School of Electrical and Computer Engineering Oklahoma State UniversityStillwaterOK 74078USA

Active metasurfaces are emerging as the core of next-generation optical devices with their tunable optical responses and flat-compact *** for the terahertz band,active metasurfaces have been developed as fascinating devices for optical chopping and compressive sensing ***,performance regulation by changing the dielectric parameters of the integrated functional materials exhibits severe limitations and parasitic ***,we introduce a C-shape-split-ring-based phase discontinuity metasurface with liquid crystal elastomer as the substrate for infrared modulation of terahertz ***-focused infrared light is applied to manipulate the deflection of the liquid crystal elastomer substrate,enabling controllable and broadband wavefront steering with a maximum output angle change of 22°at *** as another control method is also investigated and compared with infrared *** further demonstrate the performance of liquid crystal elastomer metasurface as a beam steerer,frequency modulator,and tunable beam splitter,which are highly desired in terahertz wireless communication and imaging *** proposed scheme demonstrates the promising prospects of mechanically deformable metasurfaces,thereby paving the path for the development of reconfigurable metasurfaces.

关键词： tunable modulator beam

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Hyperautomation for Air Quality Evaluations: A Perspective of Evidential Three-way Decision-making

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Cognitive Computation 2024年第5期16卷 2437-2453页

作者： Ding, Juanjuan Zhang, Chao Li, Deyu Sangaiah, Arun Kumar School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Hyperautomation acts as a real digital transformation with the support of several cutting-edge cognitive computation methods that include robotic process automation, natural language processing, artificial intelligence, and other emerging ones, which is conducive to processing complex industrial processes via extending the range of various data-driven cognitive decision-making algorithms. The study of air quality evaluations (AQE) plays a significant role in ensuring healthy atmospheric environments. In view of the objective existence of uncertainties, AQE can be modeled and addressed by a typical data-driven automated decision-making problem, and hyperautomation can provide a reasonable solution via associating with a variety of techniques. This article explores hyperautomation for AQE via evidential three-way large-scale group decision-making (LSGDM) in an intuitionistic fuzzy (IF) setting. First, the notion of intuitionistic fuzzy sets (IFSs) is incorporated into the paradigm of multi-granularity (MG) three-way decisions (TWD), and the model of adjustable MG IF probabilistic rough sets (PRSs) is developed. Second, an IF clustering analysis with the improved technique for order preference by similarity to ideal solution (TOPSIS) method is conducted to affirm representative members within a decision group. Third, a novel IF LSGDM method is built via adjustable MG IF PRSs and the evidence reasoning (ER) method. Finally, a case study in the setting of AQE is studied by using the presented evidential three-way LSGDM method. Corresponding experimental analyses are carried out for illustrating the efficiency of hyperautomation for AQE. In general, the proposed method improves the performance of information fusion by virtue of adjustable MG IF PRSs, and the TOPSIS method avoids the influence of subjective factors on decision results. Meanwhile, the evaluation information of decision-makers (DMs) is fully analyzed by means of the ER method, which can provide more explaina

关键词： Decision making

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Evaluation and Enhancement of Standard Classifier Performance by Resolving Class Imbalance Issue Using Smote-Variants Over Multiple Medical Datasets

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SN computer Science 2025年第3期6卷 1-30页

作者： Kumar, Vinod Kumar, Ravi Kant Singh, Sunil Kumar School of Computer Applications and Information Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering SRM University AP Andhra Pradesh Guntur India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India karnataka Bengaluru India

In the era of machine learning we are solving the classification problems by training the labeled classes. But sometimes due to insufficient data in some of the training classes, the system training is inadequate for these minority classes. In this case the output for the classes obtained from the less amount of trained data are miserably inappropriate and biased towards the classes having more data. This problem is known as a class imbalance problem. In such cases, standard classifiers tend to be overpowered by the expansive classes and disregard the little ones. As a result, the performance of machine learning and the deep learning algorithms are also reducing and sometimes highly unacceptable too, mainly if it is related to crucial data like medical and health related. Though various researchers provided some methods to solve this problem but mostly they are problem specific and suitable with the specific classifier only. To find a generalized and effective solution to this problem, we have applied various smote variants for solving the imbalanced factors in dataset and finally improved the performance of the various machine learning and deep learning algorithms. We have experimented and analyzed the effects of SMOTE variants on various machine learning techniques over six standard medical datasets. We have found that SMOTE variants are very effective, and they improve the standard performance measures (Accuracy, Precision, Recall and F1-Score). Additionally, based on our research, it is feasible to determine which smote variation works best with machine learning methods and datasets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalanced Classifiers Machine learning Medical datasets Smote variants

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Interpretable Predictions for Crime Categories Using Log Loss Approach for Imbalanced Target Feature 2

Interpretable Predictions for Crime Categories Using Log Los...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Manasa, A. Sen, Snigdha Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383652

Crime indicates activities that infringe laws and regulations constituted by a governing authority. These wrongdoings can range from minor aberrations such as petty theft to more serious crimes like assault, robbery, or murder. Understanding crime patterns, causes, and trends is important for creating effective law enforcement strategies and enforcing policies that facilitate to a risk-free and stable society. This paper explores the interpretability of crime category predictions generated by XGBoost and Random Forest models on San Francisco crime data ranging 12 years. Notably, in comparison to other models, such as k-Nearest Neighbors (KNN), Stochastic Gradient Descent(SGD) and Naïve Bayes, our XGBoost and Random Forest models featured superior predictive excellence, Obtaining competitive log-loss scores of 2.277761 and 2.283956, respectively, to augment interpretability we have employed Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to clarify the decision-making processes of these models. This interpretability analysis would definitely add a deeper insight into the contributing factors of model's predictive behavior towards crime rate prediction. Additionally, we have dealt with imbalanced target feature as well. In comparison to existing research in the field, our work stands out by introducing advanced interpretability techniques, LIME and SHAP, enhancing transparency in crime prediction models. This innovative approach surpasses traditional methods, providing a more comprehensive understanding of crime dynamics and achieving superior predictive excellence. Innovation is introduced through the incorporation of advanced interpretability techniques, specifically Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). © 2024 IEEE.

关键词： Stochastic systems

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