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Fast Routing With Rule Caching in Lossy Mobile SDN

Journal of Institute of Control, Robotics and Systems 2023年第11期29卷 854-858页

作者： Kim, Janghan Park, Hyung-Seok Park, Kyung-Joon Department of Electrical Engineering and Computer Science DGIST Korea Republic of

Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have been actively employed for tasks that are challenging for humans, such as scenarios like battlefield and disaster. Swarm unmanned systems, comprising UAVs and UGVs, experience dynamic additions or removals of unmanned aircraft. In this paper, we propose a rule caching-based recovery technique for achieving rapid forwarding rule establishment for additional UAVs deployed for recovery in wireless Software-defined Networking (SDN) based swarm unmanned systems. To validate the proposed algorithm, we conducted experiments by setting up a testbed using ODROID and a controller. Our results demonstrate that the proposed algorithm improves forwarding rule establishment performance by up to 50.3%. © ICROS 2023.

关键词： Ground vehicles

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Exploiting Retina Biometric Fused with Encoded Hash for Designing Watermarked Convolutional Hardware IP Against Piracy

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SN computer science 2024年第8期5卷 1-17页

作者： Chaurasia, Rahul Sengupta, Anirban Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal India Department of Computer Science and Engineering Indian Institute of Technology Indore India

The convolution layer in a convolutional neural network (CNN) is highly computationally intensive. It is crucial to design reusable low-cost hardware IP for convolutional layer for enabling hardware-based feature extraction. However, the involvement of fake IP vendor/untrustworthy broker in the integrated circuit (IC) supply chain, makes these IPs susceptible to the threat of piracy. The proposed approach presents high- level synthesis (HLS) driven watermarking methodology for designing low-cost and secure convolutional hardware IP. The presented watermarking approach employs complier-driven high-level transformation and exploits retinal signature fused with the encoded hash for piracy detective countermeasure. The proposed approach, therefore, firstly performs compiler-driven high-level transformation in order to optimize the design latency, followed by embedding the watermark of an authentic IP vendor. The generated watermark in the form of encoded hardware watermarking constraints (digital evidence) is covertly embedded into the resulting optimized design during the register allocation module of HLS. The proposed approach achieves the following: (i) optimized and secure design for convolutional hardware IP, (ii) robust detection of pirated IP at zero design cost overhead, (iii) significantly lower probability of coincidence (in the range of 1.3E−06 to 1.2E−09) indicating stronger digital evidence and higher tamper tolerance (in the range of 2.64E+460 to 9.60E+698) than recent approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional layer Encoded hash Hardware security HLS IP design Retina biometrics

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Improved Set Algebra-Based Heuristic Technique for Training Multiplicative Functional Link Artificial Neural Networks for Financial Time Series Forecasting

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SN computer science 2024年第5期5卷 1-34页

作者： Behera, Sudersan Kumar, AVS Pavan Nayak, Sarat Chandra Department of Computer Science and Engineering School of Engineering & amp Technology GIET University Gunupur India Department of Computer Science and Engineering School of Technology GITAM University Hyderabad India

The current study is defined by two main aims. An effective strategy for improving local search is to combine the Set Algebra-Based Heuristic Algorithm (SAHA) algorithm with the Nelder-Mead simplex method. The approach outlined above, referred to as the Improved SAHA (ISAHA), and has the ability to produce superior outcomes. The multiplicative functional link artificial neural network (MFLANN) is an improved version of the functional link artificial neural network that promotes exploration by replacing the summing unit in the output layer with a multiplication unit. Moreover, the combination of ISAHA and MFLANN results in the development of ISAHA-MFLANN, a sophisticated hybrid forecasting model. The main assessment of the hybrid model rests on its ability to forecast complex and dynamic financial time series. It's possible to get around the problems that come with traditional learning-based MFLANN techniques by using MFLANN's advanced approximation features and ISAHA's resilient global search features together. Experimental verification using two stock market datasets and three currency exchange rates demonstrates the validity of the idea. The results show that the suggested improved SAHA hybrid learning works well at improving six standard benchmark functions. The ISAHA-MFLANN model is also statistically significant at accurately capturing the volatility that is inherent in the financial time series. In addition, it surpasses other models such as SAHA-MFLANN, Monarch Butterfly Optimization-MFLANN, Particle Swarm Optimization-MFLANN, Genetic Algorithm-MFLANN, Gradient Descent-MFLANN, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Auto Regressive Integrated Moving Average (ARIMA). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Exchange rate forecasting Improved Set Algebra-Based Heuristic Algorithm Multiplicative functional link artificial neural network Stock market forecasting

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Two Stage Video Classification Approach Using Convolution Neural Network 2

Two Stage Video Classification Approach Using Convolution Ne...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Veena, H.N. Rajani Gowda, Naveen Chandra Ramani, D Roja SJB Institute of Technology Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India Newhorizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

This research study focuses on video categorization, which is a crucial area of computer vision with uses in entertainment, education, and surveillance. Convolutional Neural Networks (CNNs) are used in a two-stage approach in the suggested methodology. The video is first broken down into frames, which are subsequently supplied into a CNN model. Another CNN model that predicts the video class uses the output from this model as input. A publicly accessible dataset is used to assess the efficacy of this approach, demonstrating its competitive accuracy performance in comparison to the most advanced techniques. The outcomes indicate that the technique can effectively classify videos and has the potential to be used in a variety of real-world scenarios. © 2024 IEEE.

关键词： 3D CNN Autoencoder Convolution Neural Networks (CNN) Long-Term Recurrent Convolutional Network (LRCN) Recurrent Neural Network(RNN)

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Enhancing Bangla Language Next Word Prediction and Sentence Completion through Extended RNN with Bi-LSTM Model On N-gram Language 3

Enhancing Bangla Language Next Word Prediction and Sentence ...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Islam, Md Robiul Amin, Al Zereen, Aniqua Nusrat Uttara University Department of Computer Science and Engineering Dhaka Bangladesh BRAC University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

Texting stands out as the most prominent form of communication worldwide. Individual spend significant amount of time writing whole texts to send emails or write something on social media, which is time consuming in this modern era. Word prediction and sentence completion will be suitable and appropriate in the Bangla language to make textual information easier and more convenient. This paper expands the scope of Bangla language processing by introducing a Bi-LSTM model that effectively handles Bangla next-word prediction and Bangla sentence generation, demonstrating its versatility and potential impact. We proposed a new Bi-LSTM model to predict a following word and complete a sentence. We constructed a corpus dataset from various news portals, including bdnews24, BBC News Bangla, and Prothom Alo. The proposed approach achieved superior results in word prediction, reaching 99% accuracy for both 4-gram and 5-gram word predictions. Moreover, it demonstrated significant improvement over existing methods, achieving 35%, 75%, and 95% accuracy for uni-gram, bi-gram, and tri-gram word prediction, respectively. © 2024 IEEE.

关键词： Forecasting

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Analyzing the performance of geometric mean optimization-based artificial neural networks for cryptocurrency forecasting

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International Journal of Information Technology (Singapore) 2024年 1-13页

作者： Behera, Sudersan Kumar, A V S Pavan Nayak, Sarat Chandra Department of Computer Science and Engineering School of Engineering and Technology GIET University Odisha Gunupur India Department of Computer Science and Engineering School of Technology GITAM University Hyderabad India

In this study, we utilize a recently proposed non-parametric metaheuristic algorithm known as geometric mean optimization (GMO) to adjust the hidden layer input weights and bias of six ANN variants, namely PSNN, SPNN, JPSNN, FLANN, RBFN, and MLP, thereby creating six hybrid models. Later on, we engage all these hybrid models to predict the closing price of four widely used cryptocurrencies. For comparison purposes, we also engage the widely popular traditional GD algorithm to fix the hidden layer input weights and biases of the same set of ANNs, creating six alternate models and employing those models for the same prediction task. In order to assess the effectiveness of each model, we employ the mean squared error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) statistics. The experimental result shows the superiority of GMO-based PSNN over others. We then compare the GMO-based PSNN's outcomes with some of the existing hybrid models in the literature. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ANN Cryptocurrency forecasting Financial time series forecasting GMO Higher order neural networks

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Real-Time Facial Expression Recognition Using Convolutional Neural Networks for Adaptive User Interfaces 5

Real-Time Facial Expression Recognition Using Convolutional ...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Sundi, Jerry Jatan Kumar, Hitesh Bharti Bedi, Rajnish Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Chandigarh University Mohali India

ISBN: (纸本)9798350361155

Facial expression recognition plays a crucial role in enabling natural and intuitive human-computer interaction. However, existing approaches often struggle with robustness and accuracy, particularly in unconstrained environments with varying illumination, occlusions, and pose variations. This research proposes a novel real-time facial expression recognition system based on convolutional neural networks (CNNs) to address these challenges. The proposed system leverages state-of-the-art deep learning architectures and techniques to accurately recognize a wide range of facial expressions in real-time. Through extensive data preprocessing, augmentation, and a carefully designed CNN architecture, the system aims to achieve high accuracy and generalization capabilities across diverse scenarios. The recognized facial expressions are then utilized to enable adaptive user interfaces that can dynamically adapt to the user's emotional state, enhancing the overall user experience. Comprehensive experiments and user studies are conducted to evaluate the system's performance, robustness, and effectiveness in improving human-computer interaction. The proposed approach has the potential to significantly impact various domains, including gaming, healthcare, education, and beyond, by enabling emotion-aware computing and personalized interfaces. © 2024 IEEE.

关键词： Real time systems

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Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

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computer Systems science & engineering 2023年第4期45卷 715-736页

作者： Kandasamy Sellamuthu Vishnu Kumar Kaliappan Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407Tamil NaduIndia

Pesticides have become more necessary in modern agricultural ***,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the *** to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to *** residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly *** residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in *** is an obligation to provide a warning about this amount of pesticide use in *** technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential *** analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and ***,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.

关键词： Pesticide contamination complex event processing recurrent neural network Q learning multi residual level and contamination level

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Single Sample Face and Ear Recognition using Transfer Learning and Sample Expansion

Single Sample Face and Ear Recognition using Transfer Learni...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Tomar, Vivek Kumar, Nitin Deshmukh, Maroti Singh, Maheep National Institute of Technology Computer Science and Engineering Department Garhwal Uttarakhand Srinagar India Computer Science and Engineering Department Chandigarh India Doon University Computer Science Department Dehradun India

ISBN: (纸本)9798350388534

Single-sample face and ear recognition is a challenging problem in biometrics due to the fact that only a single face or ear image sample is available for the training. However, during the recognition, test images may have possible variations like different poses, occlusion, brightness, etc, which increase the difficulty of the problem. Deep learning approaches have attracted researchers working in this area. However, the selection of proper methods for feature extraction and populating the training set for deep learning methods is still a challenge in this field. In this article, a method to solve the single-sample face and ear recognition problem is proposed that combines transfer learning with sample expansion to increase recognition accuracy. The proposed method uses sample expansion by intensity variation in random pixels before feeding the images to the VGG16 deep learning model with pre-trained ResNet50 weights on the ImageNet database for feature extraction. The proposed method is tested on two popular databases, ORL face and AMI ear databases. Accuracy results prove the effectiveness of the proposed method as it outperforms the conventional principal component analysis (PCA) feature extraction method by 26.51% and 46.47% in terms of classification accuracy on the ORL face and AMI ear database. This strengthens the assumption that deep learning models with appropriate sample expansion can be useful even in scenarios where we have smaller databases with limited or single training samples. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Book Recommendation Systems Using Collaborative Approach: A Comprehensive Study

Enhancing Book Recommendation Systems Using Collaborative Ap...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Isha Afaque, Ahmad Pratyay, Adarsh Ranjan, Kumari Priti Kumar, Ankit Jena, Prem Prakash Lovely Professional University Department of Computer Science and Engineering Jalandhar India Lovely Professional University Department of Computer Science and Engineering Jalandhar India Lovely Professional University Department of Computer Science and Engineering India

ISBN: (纸本)9798350378092

In this study, we delve into the creation of a Book Recommendation System utilizing collaborative filtering techniques to offer customized book suggestions to users. Beginning with the cleansing of a dataset encompassing book details, user information, and their ratings, we ensure data accuracy for our analysis. Within this dataset, we uncover intriguing insights such as user demographics, popular authors, and book ratings, which inform our recommendation framework. Leveraging collaborative filtering, particularly Singular Value Decomposition, in order to separate the user-item matrix to generate tailored recommendations based on users' historical interactions. To gauge the system's effectiveness, we assess its performance using metrics such as recall@5 and recall@10, demonstrating its capability to deliver pertinent book recommendations. Additionally, we explore alternative recommendation strategies including popularity-based and author-based approaches, providing a comprehensive comparative analysis. Ultimately, our Book Recommendation System aims to enhance user book selection experiences, fostering greater engagement and satisfaction within the reading community. © 2024 IEEE.

关键词： Collaborative filtering

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