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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Satyarthi, Shailendra Sharma, Sanjiv Department of Computer Science and Engineering Madhav Institute of Technology and Science Gwalior India Department of Information Technology Madhav Institute of Technology and Science Gwalior India

ISBN: (纸本)9789819700363

Social networking-based platforms (e.g., Twitter, Facebook, Amazon) where users feel free to express their opinion/sentiment about any product, service or event, in the form of reviews, tweets, blogs, etc., in the last decade with the enhanced use of social media and social networking, result in huge amount of data in unstructured and unorganized form, and the need of sentiment analysis of user’s opinionated text arises for improvement in the product or service policymaking decisions. Therefore, sentiments in user’s text play crucial role, and classifying the sentiment (positive, negative or neutral) at review/document level or at sentence level is not sufficient for achieving the real solution for improvement in product, service or an event;therefore, there is a need of deeper level and fine-grained analysis at aspect level known as aspect-based sentiment classification (ABSC) which attract researchers. This paper emphasizes on various existing approaches for ABSC specifically deep learning methods and their comparative analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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OFDM with Generalized Cascade Index Modulation for Secure Communications

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China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

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Algorithmic Proficiency in Spark Configuration Tuning: An Empirical Study using Execution Time Metrics across Varied Workloads 2

Algorithmic Proficiency in Spark Configuration Tuning: An Em...

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

作者： Sewal, Piyush Singh, Hari Computer Science & Engineering Department Jaypee University of Information Technology Solan173234 India

In the realm of big data, where datasets of immense scale pose processing challenges, distributed processing platforms like open-source Apache Spark have emerged to address these issues. Spark's internal configuration parameters exert varying impacts on execution times based on job characteristics, making manual optimization daunting. The core focus of this study lies in optimizing Spark's internal configurations, with specific attention directed towards three types of workloads: Iterative-intensive, Memory-intensive, and CPU-intensive. Employing Grid Search, Random Search, and Evolutionary Optimization algorithms yields substantial execution time reductions: 23.24% with Grid Search, 19.71% with Random Search, and 23.06% with Evolutionary Optimization. Notably, Evolutionary Optimization achieves optimal configurations approximately 29% faster than Grid Search. While Random Search and Evolutionary Optimization share similar time requirements, Random Search's execution time reduction for a given Spark workload is relatively lower. This research sheds light on algorithmic configuration tuning intricacies and its influence on Spark workload execution times, contributing to the exploration of optimizing big data processing platforms. © 2024 Elsevier B.V.. All rights reserved.

关键词： Iterative methods

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A Hybrid Regularized Multilayer Perceptron for Input Noise Immunity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 115-126页

作者： Mondal, Rahul Pal, Tandra Dey, Prasenjit National Institute of Technology Durgapur Department of Computer Science and Engineering Durgapur713209 India Cooch Behar Government Engineering College Department of Computer Science and Engineering Cooch Behar736170 India

The immunity of multilayer perceptron (MLP) is less effective toward input noise. In this article, we have focused on the robustness of MLP with respect to input noise where noise can be additive or multiplicative. Here, we have proposed a DropConnect-based regularized MLP to reduce the coadaptation among the neurons of the hidden layer. At first, we have empirically and statistically shown that by reducing coadaptation among the hidden neurons, an MLP can achieve better noise immunity. We have also empirically shown that an MLP with input noise injection and $l_{2}$ regularizer is an effective approach to improve its noise immunity. However, the results indicate that it does not adjust the coadaptation among the hidden neurons. Therefore, for further improvement, we have proposed a hybrid regularized MLP (HRMLP), where DropConnect is combined with the noise injection and $l_{2}$ regularizer. In addition to input noise, we have also verified the robustness of HRMLP with respect to $\mathbf {20\%}$ outliers in the dataset. To justify the effectiveness of the proposed HRMLP, we have compared it with MLP, MLP with noise injection, MLP with $l_{2}$ regularizer, MLP with noise injection and $l_{2}$ regularizer, and MLP with DropConnect along with two state-of-the-art works based on $\mathbf {20}$ standard datasets. The experimental results for both noisy inputs and outliers confirm that the performance of HRMLP is significant compared to other methods. © 2020 IEEE.

关键词： Neurons

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Automated License Plate Recognition for Non-Helmeted Motor Riders Using YOLO and OCR

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Journal of Mobile Multimedia 2024年第2期20卷 239-266页

作者： Anilkumar, Chunduru Rani, Meesala Shobha Venkatesh, B. Rao, G. Srinivasa Department of Information Technology GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Science and Engineering REVA University Karnataka Bangalore India Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Information Technology RVR & JC College of Engineering Andhra Pradesh Guntur India

One of the leading causes of serious injuries in motorcycle accidents is the failure to wear a helmet, pressing the need for effective measures to encourage riders to use helmets. To stop these frequent violations of business regulations, regular observation is necessary. The proposed system is for detecting traffic violations in India related to riding motorcycles without helmets. The system utilizes deep learning-based object detection using YOLO and OCR techniques to automatically detect non-helmet riders and extract license plate numbers. The proposed system aims to improve efficiency and accuracy in detecting violations by automating the process and reducing the need for manpower. The system involves three levels of object detection: person and motorcycle/moped, helmet, and license plate. OCR is used to extract the license plate number, and all techniques are subject to predefined conditions and constraints. The system is designed to operate on video input to ensure high-speed execution, and it intends to offer a complete solution for both detection of helmet and extracting the license plate number. © 2024 River Publishers.

关键词： helmet detection license plate number Object detection optical character recognition YOLO

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IoT-Based Pesticide Detection in Fruits and Vegetables Using Hyperspectral Imaging and Deep Learning 4th

IoT-Based Pesticide Detection in Fruits and Vegetables Usi...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Augustin, Anju Kiliroor, Cinu C. Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam India

ISBN: (纸本)9783031488870

Fruits and vegetables contain rich nutrients and vitamins. So that they are part of our daily diet. For proper cell growth and health, we need these nutrients. Today in most crops during their growth and post-harvesting preservation different kinds of pesticides were used. Normal usage of such pesticides not that much affects health. But the actual situation is beyond our control. From soil preparation to the post-harvesting stage, pesticides are being added at alarming rates. It affects our health in a harmful way and leads to major health issues. Various studies exist to detect the pesticide levels in fruits and vegetables. This article analyses different existing methods of pesticide detection and examines their features and problems. Through this study, it is understood that Hyperspectral Imaging (HSI) is a very good method, and with it, more accurate results can be obtained by Transfer Learning (few-shot learning). This paper proposes an architecture and algorithm based on HSI and few-shot learning. Future studies are needed in this area to convert an RGB image to a spectral image because the HSI device is very expensive. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Nutrients

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Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, and YOLOv9: A Study on Performance and Explainability

Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, a...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Subair, Rubiya Benedict, Shajulin Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798331540821

Elephant emotion detection has marked a crucial component owing to the increased casualties due to human-elephant conflicts. The difficulty has increased due to the lack of algorithms that quickly identify emotions with explainable features. This paper proposes a holistic architecture that detects the emotions of elephants using YOLOv5, YOLOv8, and YOLOv9 models, The study aims to integrate explainable AI features such as Eigen-CAM to manifest the training and testing capabilities of models. In addition, we evaluated the capability of the proposed system using labeled datasets that distinguish elephant emotions to angry, happy, and sad. Our experimental results, conducted at the IoT Cloud Research laboratory, revealed training accuracy of 79.3% for YOLOv5, 79.5% for YOLOv8, and 81.1 % for YOLOv9 and test accuracy of 93%, 95%, and 95% for YOLOv5, YOLOv8, and YOLOv9. Additionally, we recorded the impact of resolutions while performing model evaluations. The observations learned from the experiments will benefit AI-assisted machines to detect elephant emotions that can be utilized by mahouts or people living near forest locations. © 2024 IEEE.

关键词： Elephant Emotion Explainable AI Yolo V5 Yolo V8 Yolo V9

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PERFORMANCE TEST OF V2X VEHICLE COLLISION WARNING ALGORITHM BASED ON COMBINATION OF VIRTUAL REALITY FUSION

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2023年第2期85卷 111-124页

作者： Han, Baojian Zhang, Yu Liu, Yunxiang Zhu, Jianlin Zhang, Qing School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

Proposing a performance testing methodology for vehicle collision warning algorithm at intersections, this approach addresses the issues of the key V2X technology being challenging to simulate with real-scene data in virtual simulations, alongside the high-risk factor, elevated testing cost and limited space during the real vehicle verification process. The proposed method involves constructing a simulation environment based on PreScan and Matlab/Simulink, to verify the algorithm's performance. The sensory data obtained from an on-board unit (OBU) installed in a test vehicle on actual roads is then collected and imported into two indoor OBUs to evaluate the algorithm's efficiency further. The test outcomes reveal that combining real and virtual data with simulated data substantially enhances the efficiency of the algorithm testing and ensures security while further validating its effectiveness in genuine scenarios. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Virtual reality

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Halo reduction multi-exposure image fusion technique

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Multimedia Tools and Applications 2025年第13期84卷 12347-12370页

作者： Sharif, Rizwan Amin, Benish Sukhia, Komal Nain Department of Electrical Engineering Institute of Space Technology Islamabad Pakistan Department of Computer Science Institute of Space Technology Islamabad Pakistan

Multi-exposure image fusion (MEF) involves combining images captured at different exposure levels to create a single, well-exposed fused image. MEF has a wide range of applications, including low light, low contrast, night photography, medical imaging, and remote sensing. However, MEF methods often face issues like artifacts, halos around edges, color inconsistencies, noise amplification, and difficulty in preserving fine details. Moreover, assessing the quality of fused images objectively is complex due to the subjective nature of human perception. Solving the challenges is essential to developing efficient MEF techniques that produce high-quality results across various scenarios. The proposed technique introduces an approach to handling halo artifacts and implementing MEF. The Dense Scale-Invariant Feature Transform (DSIFT) is used to capture vital information about image brightness, texture, and edges from source images. Three weight maps are computed from the local mean, signal strength, and the global gradient for initial weight estimation. The local mean represents the brightness of specific image areas, signal strength preserves essential details like textures and edges while reducing image noise, and global gradient helps identify regions with significant pixel value shifts across multiple exposure images. The weight maps are then combined using a weighted average and a Gaussian smoothing filter is applied to reduce inherent noise and discontinuities in the original weights. Subsequently, pyramid decomposition is performed to generate a fused image. The efficiency of the proposed method is extensively tested on challenging multi-exposure image sequences. The results of the proposed approach demonstrate its superiority in both subjective evaluation and objective metrics like the MEF-Structural Similarity Index (MEF-SSIM), Natural Image Quality Evaluator (NIQE) and Gradient based performance measure (QAB/f). © The Author(s), under exclusive licence to Springer

关键词： Image fusion

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Hybrid RNN Based Text Classification Model for Unstructured Data

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SN computer science 2024年第6期5卷 726页

作者： Sunagar, Pramod Sowmya, B.J. Pruthviraja, Dayananda Supreeth, S. Mathew, Jimpson Rohith, S. Shruthi, G. Cloudthat Technologies Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education School of Computer Science and Engineering REVA University Department of Computer Science and Engineering Indian Institute of Technology Patna Department of ECE Nagarjuna College of Engineering and Technology

The volume of social media posts is on the rise as the number of social media users expands. It is imperative that these data be analyzed using cutting-edge algorithms. This goal is handled by the many techniques used in text categorization. There are a variety of text categorization techniques available, ranging from machine learning to deep learning. Numerical crunching has become easier with less processing time since the emergence of high-end computer facilities. This has led to the development of sophisticated network architectures that can be trained to achieve higher precision and recall. The performance of neural network models which was evaluated by the F1 score is affected by cumulative performance in precision and recall. The current study intends to analyze and compare the performance of the neural network proposed, A Hybrid RNN model that has two layers of BiLSTM and two layers of GRU to that of previous hybrid models. GloVE dataset is used to train the models and their accuracy, precision, recall, and F1 score are used to assess performance. Except for the RNN + GRU model, the RNN + BILSTM + GRU model has a precision of 0.767, a recall of 0.759, and an F1-score of 0.7585. This hybrid model outperforms the others. © The Author(s) 2024.

关键词： Bidirectional long-short-term memory (BiLSTM) Gated recurrent units (GRU) Recurrent neural networks (RNN) Region-based convolutional neural networks (RCNN) Text classification

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