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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Liu, Yang Chen, Liang He, Xiangnan Peng, Jiaying Zheng, Zibin Tang, Jie Sun Yat-sen University School of Computer Science and Engineering Guangzhou China University of Science and Technology of China School of Data Science School of Information Science and Technology Hefei China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350322279

Personalized recommendation is becoming increasingly important in online information systems in the current era of information explosion. In real-world scenarios, when a user considers which items to consume, the decision choice may be affected by her friends. For example, she may ask her friends for suggestions or be attracted by products purchased by one friend. As such, to provide satisfactory recommendation service, it is important to account for the evidence in social relations when they are available to use. Several prior efforts have been made to leverage social relations to build the recommender system and verified their utility. However, most existing methods, such as the well-known TrustSVD, leverage only first-order social relations, i.e., the direct neighbors that are connected to the target user. The high-order social relations, e.g., the friends of friends, which are very informative to reveal user preference, have been largely ignored. © 2023 IEEE.

关键词： Online systems

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An Improvement of K-means Algorithm for Big Data Clustering

An Improvement of K-means Algorithm for Big Data Clustering

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2023 International Conference on computer Applications technology, CCAT 2023

作者： Liu, Bingshan Zhang, Yaxuan Chen, Dandan Ministry of Education Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Nanjing210044 China Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China

ISBN: (纸本)9798350301540

Clustering data, the process of categorizing comparable data into distinct groups, is a fundamental task in data analysis. K-means is a widely used and fundamental clustering technique that employs iterative calculations to divide a given dataset into k clusters. It is particularly effective and adaptable for large datasets. However, determining the number of clusters and their centers in advance is a complex problem that often plagues researchers. In this article, we propose an innovative strategy to address these two obstacles by utilizing preprocessing to obtain the necessary information for k-means. We employ density-based clustering methods, represented by DENCLUE (DENsity-based CLUstEring), which can separate clusters regardless of the total number of clusters. DENCLUE is highly effective in producing excellent clusters and has received extensive attention. Our proposed method, Dek-means (Density-based kmeans algorithm), uses DENCLUE as a preliminary processing step to determine the quantity of clusters and calculate cluster centers. The resulting information is then passed on to k-means for final calculation and clustering. To evaluate the performance of our novel algorithm, we conducted tests on large datasets and analyze the results. The proposed method is guaranteed to converge to global minima, which improves the quality of the final result. © 2023 IEEE.

关键词： Large datasets

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BenCo: First Step Towards Coreference Resolution in Bengali

BenCo: First Step Towards Coreference Resolution in Bengali

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Hossain, Mojammel Islam, Md Samiul Uddin Ahmed, Jashim Kader Khan, Md Rahat University of Padova Information Engineering Padova Italy University of Alberta Computing Science AB Canada Ahsanullah University of Science & Technology Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350322972

Coreference resolution is well-studied in NLP;however, Bengali coreference resolution research has not been as well investigated as it has been for English and other rich languages. Bengali has a richer morphology than English despite having fewer resources. This research introduce a tiny new dataset of coreference annotations over Bengali texts from four domains in this article called BenCo. In this dataset, there are 48,610 tokens and 5488 mention annotations structured into 470 mention clusters. The article outline the procedure used to generate this dataset and use it to build an end-to-end neural network-based system. This study will help to clarify how Bengali coreference phenomena differ depending on the domain and inspires others to produce more materials in the language. Also, insufficient information transfer in the zero-shot multilingual situations, which may indicate the need for language-specific resources for this job. © 2023 IEEE.

关键词： Zero-shot learning

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Enhanced Multimodal Physiological Signal Analysis for Pain Assessment Using Optimized Ensemble Deep Learning

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computer Modeling in engineering & sciences 2025年第5期143卷 2459-2489页

作者： Karim Gasmi Olfa Hrizi Najib Ben Aoun Ibrahim Alrashdi Ali Alqazzaz Omer Hamid Mohamed O.Altaieb Alameen E.M.Abdalrahman Lassaad Ben Ammar Manel Mrabet Omrane Necibi Department of Computer Science College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia REGIM-Lab National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia College of Computing and Information Technology University of BishaBisha61922Saudi Arabia Cybersecurity Department College of Engineering and Information TechnologyBuraydah Private CollegesBuraydah51418 Saudi Arabia Department of Computer Science Prince Sattam bin Abdulaziz UniversityAl-Kharj1942Saudi Arabia

The potential applications of multimodal physiological signals in healthcare,pain monitoring,and clinical decision support systems have garnered significant attention in biomedical *** self-reporting is the foundation of conventional pain assessment methods,which may be *** learning is a promising alternative to resolve this limitation through automated pain *** paper proposes an ensemble deep-learning framework for pain *** framework makes use of features collected from electromyography(EMG),skin conductance level(SCL),and electrocardiography(ECG)*** integrate Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),Bidirectional Gated Recurrent Units(BiGRU),and Deep Neural Networks(DNN)*** then aggregate their predictions using a weighted averaging ensemble technique to increase the classification’s *** improve computing efficiency and remove redundant features,we use Particle Swarm Optimization(PSO)for feature *** enables us to reduce the features’dimensionality without sacrificing the classification’s *** improved accuracy,precision,recall,and F1-score across all pain levels,the experimental results show that the suggested ensemble model performs better than individual deep learning *** our experiments,the suggested model achieved over 98%accuracy,suggesting promising automated pain assessment ***,due to differences in validation protocols,comparisons with previous studies are still *** deep learning and feature selection techniques significantly improves model generalization,reducing overfitting and enhancing classification *** evaluation was conducted using the BioVid Heat Pain Dataset,confirming the model’s effectiveness in distinguishing between different pain intensity levels.

关键词： Pain assessment ensemble learning deep learning optimal algorithm feature selection

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Performance Enhancement of Adaptive Neural Networks Based on Learning Rate

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computers, Materials & Continua 2023年第1期74卷 2005-2019页

作者： Swaleha Zubair Anjani Kumar Singha Nitish Pathak Neelam Sharma Shabana Urooj Samia Rabeh Larguech Department of Computer Science Aligarh Muslim UniversityAligarh202002Uttar PradeshIndia Department of Information Technology Bhagwan Parshuram Institute of Technology(BPIT)GGSIPUNew Delhi110089India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology(MAIT)GGSIPUNew DelhiIndia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Deep learning is the process of determining parameters that reduce the cost function derived from the *** optimization in neural networks at the time is known as the optimal *** solve optimization,it initialize the parameters during the optimization *** should be no variation in the cost function parameters at the global *** momentum technique is a parameters optimization approach;however,it has difficulties stopping the parameter when the cost function value fulfills the global minimum(non-stop problem).Moreover,existing approaches use techniques;the learning rate is reduced during the iteration *** techniques are monotonically reducing at a steady rate over time;our goal is to make the learning rate *** present a method for determining the best parameters that adjust the learning rate in response to the cost function *** a result,after the cost function has been optimized,the process of the rate Schedule is *** approach is shown to ensure convergence to the optimal *** indicates that our strategy minimizes the cost function(or effective learning).The momentum approach is used in the proposed *** solve the Momentum approach non-stop problem,we use the cost function of the parameter in our proposed *** a result,this learning technique reduces the quantity of the parameter due to the impact of the cost function *** verify that the learning works to test the strategy,we employed proof of convergence and empirical tests using current methods and the results are obtained using Python.

关键词： Deep learning optimization convergence stochastic gradient methods

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Deployment of Co-operative Farming Ecosystems Using Blockchain 22nd

Deployment of Co-operative Farming Ecosystems Using Blockcha...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on information Assurance and Security, IAS 2022

作者： Mahapatra, Aishwarya Gupta, Pranav Swarnkar, Latika Gupta, Deeya Kar, Jayaprakash Centre for Cryptography Cyber Security and Digital Forensics Department of Computer Science & Engineering Department of Communication & Computer Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9783031274084

Blockchain has helped us in designing and developing decentralised distributed systems. This, in turn, has proved to be quite beneficial for various industries grappling with problems regarding a centralised system. So, we thought of exploring blockchain’s feasibility in the agricultural industry. India is a country where a large part of the population is still dependent on agriculture, however, there’s no proper system in use as yet that can help the farmers get the right price for their farm products and help the consumers get an affordable price for their ***, we propose a blockchain based decentralized marketplace where we will implement a collaborative model between farmers and consumers. This model will allow the farmers to record their potential crops and the expected output on decentralised ledger besides, enabling them to showcase their integrity and credibility to consumers. The consumers, on the other hand, can actually check everything about the farmers with the help of their information based on the previous supplies. This open and full proof digital market framework will thus reduce black marketing, hoarding, adulteration, etc. In this research paper, we have explored one possible blockchain model by creating a breach proof ledger of records. We have used solidity and ethereum frameworks for the working of the model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Blockchain and Artificial Intelligence Applications to Defeat COVID-19 Pandemic

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computer Systems science & engineering 2022年第2期40卷 691-702页

作者： Mohammed Baz Sabita Khatri Abdullah Baz Hosam Alhakami Alka Agrawal Raees Ahmad Khan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025India Department of Computer Engineering College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020,provide critical research criteria to assess the vulnerabilities of our current health *** paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness,about public health and healthcare *** view of this unprecedented health crisis,distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages,and with the higher *** the implementation level,blockchain integration,early detection and avoidance of an outbreak,identity protection and safety,and a secure drug supply chain can be *** the opposite end of the continuum,artificial intelligence methods are used to detect corona effects until they become too serious,avoiding costly drug *** paper explores the application of blockchain and artificial intelligence in order to fight with COVID-19 epidemic *** paper analyzes all possible newly emerging cases that are employing these two technologies for combating a pandemic like COVID-19 along with major challenges which cover all technological and motivational *** paper has also discusses the potential challenges and whether further production is required to establish a health monitoring system.

关键词： Artificial intelligence information privacy machine learning coronavirus(COVID-19) SARS-CoV-2 epidemic

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Accelerating Big Data Application by Eliminating Redundancy on Hadoop Cluster 29

Accelerating Big Data Application by Eliminating Redundancy ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Lei, Kelun Du, Shaokang You, Xin Xuan, Zhibo Kong, Haoran Yang, Hailong Shang, Jing Xiao, Zhiwen Wu, Zhihui Luan, Zhongzhi Qian, Depei Beihang University School of Computer Science and Engineering Beijing China China Mobile Information Technology Center China

ISBN: (纸本)9798350330717

Big data applications are widely adopted to mine valuable information from a tremendous amount of industry data, which is commonly represented as a series of map-reduce operations. Among various map-reduce frameworks, Hadoop is most commonly adopted for data processing at large scale. Although Hadoop eases the development of highly scalable distributed big data applications, inefficient implementation due to poor coding practice and deep software abstractions can cause severe performance issues such as unreasonable slowdown, high response latency, and waste of computing resources, which can lead to unsatisfactory serving delay or significant maintenance cost. In this paper, we first categorize three common types of redundant patterns in big data applications. Then we propose a tool-assisted optimization workflow to detect the redundant patterns automatically, which profiles the application by sampling hardware performance monitoring units. Moreover, we present a profiling visualization method that can help to pinpoint the redundant codes. Based on these approaches, we optimize several big data applications by eliminating redundancies, yielding up to 14.8% performance improvement. © 2023 IEEE.

关键词： Hadoop Performance Optimization Redundant Patterns

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Lumpy Skin Disease Detector 7

Lumpy Skin Disease Detector

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7th International Conference on Image information Processing, ICIIP 2023

作者： Sharma, Vidur Kanwar, Kushal Jaypee University of Information And Technology Department of Computer Science & Engineering Solan H.P Waknaghat India

ISBN: (纸本)9798350371406

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning algorithms are trained on data, and they can then be used to make predictions or decisions without human intervention. Lumpy Skin Disease (LSD) is a highly contagious and economically devastating viral infection that impacts cattle worldwide. The quick and accurate detection of LSD in cows is paramount for effective disease management and containment. In this paper, we harness the capabilities of machine learning, particularly using the TensorFlow framework, to create a robust and automated model for the early identification of LSD in cattle. © 2023 IEEE.

关键词： Convolutional neural networks

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Threshold Based Real-Time Landslide Prediction System Using Low-Cost Sensor Networks 3

Threshold Based Real-Time Landslide Prediction System Using ...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Kaushal, Arush Sehgal, Vivek Kumar Jaypee University of Information Technology Department of Computer Science and Engineering H.P. Waknaghat Solan India

ISBN: (纸本)9798350302288

Landslides pose a recurring threat in the Himalayan region, leading to devastating consequences in terms of human casualties and property damage. This research introduces a groundbreaking approach to real-time landslide monitoring and prediction, leveraging an affordable sensor network. The system employs a network of wireless sensors strategically placed to continuously monitor environmental conditions and gather pertinent data. This data is then transmitted to a cloud server via a WiFi module, where it is integrated into the PowerBI platform for visualization and analysis. To predict landslides in real-time, the collected data is fed into a machine learning model. The performance of three algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Decision Tree (DT), was evaluated using the acquired data. Among these algorithms, SVM demonstrated exceptional results, exhibiting low values for key error metrics such as Mean Squared Error (MSE=O.067), Mean Absolute Error (MAE=O.246), and Root Mean Squared Error (RMSE=0.204). Threshold values are determined based on the training model's output, enabling the generation of alerts and warnings at different levels. This innovative system significantly enhances preparedness and response strategies in landslide-prone areas, equipping stakeholders with timely information to effectively mitigate the impact on lives and infrastructure. © 2023 IEEE.

关键词： Decision trees

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