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EMARec: a sequential recommendation with exponential moving average

Neural Computing and Applications 2024年第21期36卷 12917-12933页

作者： Chen, Rui Wang, Zonglin Tang, Cundong Zhang, Jianwei Li, Pu Kong, Xiangjie Huang, Min College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Henan Joint International Research Laboratory of Computer Animation Implementation Technologies Zhengzhou450001 China School of Information Science and Technology Northwest University Xi’an710127 China Faculty of Information and Engineering Xuchang Vocational Technical College Xuchang461000 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China

Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential recommendation tasks. However, existing deep neural network-based sequential recommendation methods often ignore the noise information in user behavioral data that influence recommendation effectiveness, making recommendation models sensitive to noisy data. Additionally, these deep learning-based recommendation models often require a large number of parameters to capture user behavior patterns, leading to higher time complexity and susceptibility to overfitting due to noise fluctuations. To address these issues, in this paper, we apply the moving average concept from the field of time-series analysis to sequential recommendation tasks. The approach smooths sequence data, removes noise, making the data more stable and reliable, increases the model’s insensitivity to noise information, and better understands the evolution of user interests and behavioral patterns. Specifically, in our experiments, the moving average algorithm effectively denoises sequential data with low time complexity. Therefore, we propose a Sequential Recommendation with Exponential Moving Average with MLP architecture that makes the model more competitive in terms of time complexity. Experimental results conducted on two datasets demonstrate that EMARec outperforms state-of-the-art sequential recommendation methods across various common evaluation metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Recommender systems

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Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

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computers, Materials & Continua 2023年第3期74卷 5901-5914页

作者： R.Brindha S.Nandagopal H.Azath V.Sathana Gyanendra Prasad Joshi Sung Won Kim Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathur603203India Department of Computing Science and Engineering Nandha College of TechnologyErode638052India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Gyeongbuk-doKorea

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.

关键词： Phishing email data classification natural language processing deep learning cybersecurity

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Aiding Emergency Services Through Real Time Traffic Congestion Monitoring And Route Optimization Using Pic 2

Aiding Emergency Services Through Real Time Traffic Congesti...

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

作者： SThamarai Selvi, S.B. Thirumurugan, S. Kanish, P. Surya, M. K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798350384277

The management of road traffic has emerged as a pressing concern due to a significant increase in accidents and congestion, particularly in densely populated urban areas. To address these challenges, a comprehensive solution has been proposed, consisting of a smart traffic light control and congestion avoidance system tailored for emergency situations such as ambulance services. This system comprises two main components: a smart traffic light control system and a smart traffic routing system. The former intelligently manages traffic lights to prioritize emergency vehicles, while the latter aims to mitigate congestion by providing alternative routes. Equipped with vehicle sensors linked to embedded systems, which in turn connect to ZIGBEE for data exchange, the controller efficiently regulates traffic flow at signal junctions. By strategically placing sensors near traffic signal poles, the system accurately assesses lane densities, facilitating real-time traffic management. Data storage is facilitated through ZIGBEE, while a microcontroller unit communicates alternative route information via LCD display, enhancing overall traffic management efficacy. © 2024 IEEE.

关键词： Zigbee

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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Target tracking method of Siamese networks based on the broad learning system

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CAAI Transactions on Intelligence technology 2023年第3期8卷 1043-1057页

作者： Dan Zhang C.L.Philip Chen Tieshan Li Yi Zuo Nguyen Quang Duy Navigation College Dalian Maritime UniversityDalianChina Innovation and Entrepreneurship Education College Dalian Minzu UniversityDalianChina Computer Science and Engineering College South China University of TechnologyGuangzhouChina Department of Computer and Information Science Faculty of Science and TechnologyUniversity of MacaoMacaoChina School of Automation Engineering University of Electronic Science and Technology of ChinaChengduChina Faculty of Navigation Vietnam Maritime UniversityHaiphongVietnam

Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other ***,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time *** based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time ***,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during ***,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time *** broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning *** at the problems of Siamese networks and the characteristics of BLS,a target tracking method based on BLS is *** method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐*** broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the *** results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

关键词： broad learning system siamese network target tracking

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Combining Historical Data with Future Predictions to Assess Urban Noise and Air Quality 2

Combining Historical Data with Future Predictions to Assess ...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Siva Prasad, Veeram Venkata Bhanu Chand, K. Aruna Devi, R Uma Krishna, Jonnalagadda Srinivasarao, Badde Sai Spurthi Institute of Technology Computer Science And Engineering India Swarnandhra College of Engineering and Technology Department of Information Technology India India

ISBN: (纸本)9798350364699

Analysing Urban Noise and Air Quality by Integrating Past Data Accompanying Projections for the Future. By joining predicting models accompanying Internet of Things technology, this study tackles the question of judging buzz and air kind in urban regions. To do this, a model was used to devise noise and PM2.5 heat maps. Open-beginning traffic data was again used for correct contextualization. Using this form, find the districts with extreme levels of contamination and crashes are likely situated on busy roads and technical zones. R2 principles of 0.93 for PM2.5 and 0.90 for blast showed that the forecasting models, which involve conclusion trees and convolutional affecting animate nerve organ networks, were completely correct in guessing the levels of dirtiness and crash. These findings demonstrate the significance of actively seeking out methods or policies that have no adverse impact on environmental issues in reports when formulating municipal plans. Furthermore, the study proposes strategies to enhance the city's conservation of natural resources, drawing on the conclusive findings. These procedures contain the exercise of reduced-issuance zones and the promotion of green fields. A joined and data-compelled plan is important for compensating policy selections in city preservation of natural resources, and this study creates a solid gift to both scientific knowledge and useful use in these fields. © 2024 IEEE.

关键词： Air quality

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Machine Learning for Strategic Sales Forecasting in Retail Chains

Machine Learning for Strategic Sales Forecasting in Retail C...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Rajaram, Tharanimathi Udhayamoorthi, M. Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore641042 India

ISBN: (纸本)9798331510299

Sales of an individual item record maintained by supermarket chains/big retailers like Big Mart help in estimating consumer demand and thereby enable them to do effective inventory management. Through the analysis of data stored in the data warehouses, they access the anomalies and trends informing decision-making processes. This will be invaluable information to the retailers, in this case Big Mart, in predicting the future sales volume using machine learning techniques. A predictive model was developed utilizing XGBoost, Linear Regression, Logistic Regression, and SVM techniques to forecast sales for businesses like Big Mart. It was found that this model outperforms existing ones, providing more accurate sales predictions and enabling better inventory management decisions. © 2024 IEEE.

关键词： Chains

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Robust autonomous driving control using deep hybrid-learning network under rainy/snown conditions

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Multimedia Tools and Applications 2024年第41期83卷 89281-89295页

作者： Lee, Chao-Yang Khanum, Abida Sung, Tien-Wen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical Engineering National Cheng Kung University No.1 University Road Tainan City701 Taiwan College of Computer Science and Mathematics Fujian University of Technology No3 Xueyuan Road Fuzhou350011 China

The study introduces a groundbreaking two-stage deep hybrid learning architecture, Robust Autonomous Driving Control (RADC), designed to address the formidable challenge of ensuring safe and efficient autonomous driving in adverse weather conditions, including heavy rain and snow, in complex scenarios. In the first stage, our proposal utilizes an encoder as a variational autoencoder (VAE) model. This encoder leverages the VAE to extract feature information from the perceptual data surrounding the environment. Moving on to the second stage, we build a decoder as an Inception-Bidirectional Long Short-Term Memory (IBL) model. This decoder combines the VAE latent features obtained in stage one with additional control vehicle information tasks, including steering, speed, etc. Our approach involves predicting driving behavior along a predetermined route, allowing autonomous vehicles to stay centered on the road, simulating diverse driving scenarios, and achieving significant reductions in route-following time. This framework utilizes deep hybrid learning methods and harnesses Nvidia GPU capabilities to evaluate the effectiveness of InceptionNet, ResNet-50, MobileNet, DenseNet, and VGG16 convolutional neural networks. It enhances vehicle route following with improved metrics such as reduced inference time, heightened accuracy, and minimized lane changes in training. The RADC demonstrates exceptional performance, notably achieving a mean square error of 0.0464, 0.0346, and a 6-millisecond inference time in challenging weather like hard rain and snow. The proposed model is validated through comprehensive experiments in the Airsim environment, showcasing notable interpretability, generalization, and robustness capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision making

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Role of internet of things in protecting different wearable gadgets and materials

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Materials Today: Proceedings 2022年 56卷 3387-3393页

作者： Prasant, P. Bhardwaj, Shashank Gupta, Monisha Srivastava, Mahima Singh, Jitendra Maurya, Rajesh Kumar Computer Science and Technology Khalsa College of Engineering and Technology Amritsar India Department of Computer Applications KIET Group of Institutions Ghaziabad India Computer Science and Engineering Chandigarh University Punjab India College Uttarakhand Dehradun India Department of Computer Science Engineering SRM Institute of Science and Technology UP Ghaziabad India MCA ABES Engineering College Ghaziabad India

Out of the most entrusted technological revolutions applied for the advancement of living standards and creation of a more convenient operation in the professional world, the Internet of Things (IoT) has been progressing at the highest projected pace. The interconnectivity of gadgets and devices over the internet to produce smarter control, communication, and a swift and easy lifestyle makes it an amazing tech introduction. However, the IOT also brings along a huge flow of privacy and security concerns whilst considering the huge inflow of data breaches and cyberattacks in the network data. The advancements in the security protocols of the data thus bring along mistrust among the users of IoT networks who might then no longer wish to be a part of this technological revolution. Therefore, it is important to maintain security assurance in the IoT based connection networks of wearable gadgets. This paper discusses the symmetric and asymmetric approaches of encryption over IoT which can help protect the network of smart phones connected to the wearable to assure a safe and smart data commute and avoid access to private data over the same internet connection. © 2021

关键词： Network security

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