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Improving Data Processing Speed on Large Datasets in a Hadoop Multi-node Cluster using Enhanced Apriori Algorithm

Journal of Intelligent and Fuzzy Systems 2023年第4期45卷 6161-6177页

作者： Sundarakumar, M.R. Sharma, Ravi Fathima, S.K. Gokul Rajan, V. Dhayanithi, J. Marimuthu, M. Mohanraj, G. Sharma, Aditi Johny Renoald, A. School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Department of CSE Sona College of Technology Tamilnadu Salem India Department of Smart Computing Vellore Institute of Technology Tamilnadu Vellore India School of Computer Science and Engineering Parul Institute of Technology Parul University Gujarat India Department of EEE Erode Sengunthar Engineering College Tamilnadu Perundurai India

For large data, data mining methods were used on a Hadoop-based distributed infrastructure, using map reduction paradigm approaches for rapid data processing. Though data mining approaches are established methodologies, the Apriori algorithm provides a specific strategy for increasing data processing performance in big data analytics by applying map reduction. Apriori property is used to increase the efficiency of level-wise creation of frequent itemsets by minimizing the search area. A frequent itemset's subsets must also be frequent (Apriori property). If an itemset is rarely, then all of its supersets are infrequent as well. We refined the apriori approach by varying the degree of order in locating frequent item sets in large clusters using map reduction programming. Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC) is a classical algorithm which are used for data processing from the huge datasets but their accuracy is not up to the mark. In this article, updated Apriori algorithms such as multiplied-fixed-pass combined counting (MFPC) and average time-based dynamic combined counting (ATDFC) are used to successfully achieve data processing speed. The proposed approaches are based on traditional Apriori core notions in data mining and will be used in the map-reduce multi-pass phase by ignoring pruning in some passes. The optimized-MFPC and optimized-ATDFC map-reduce framework model algorithms were also presented. The results of the experiments reveal that MFPC and ATDFC are more efficient in terms of execution time than previously outmoded approaches such as Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC). In a Hadoop multi-node cluster, this paradigm accelerates data processing on big data sets. Previous techniques were stated in terms of reducing execution time by 60-80% through the use of several passes. Because of the omitted trimming operation in data pre-processing, our proposed new approaches will save u

关键词： Data mining

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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Computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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Game Therapy for Specially-Abled Individuals with PPO Reinforcement Learning in VR-based Educational Games 5

Game Therapy for Specially-Abled Individuals with PPO Reinfo...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Joypriyanka, M. Surendran, R. Sathish Kumar, P.J. Sivasangari, A. Saveetha Institute of Medical and Techinical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Sathyabama Institue of Science and Technology School of Computing Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

This research study presents a new application of reinforcement learning (RL), specifically Proximal Policy Optimization (PPO), in creating immersive virtual reality (VR) games aimed at aiding skill acquisition and emotional regulation for individuals with anger management difficulties. People with Down syndrome, Autism Spectrum Disorder, Cerebral Palsy (CP), Intellectual Disabilities (ID), and Stroke Survivors are mainly considered. The study examines how PPO-trained agents improve cognitive, social, and emotional regulation. Techniques include thorough hyperparameter optimization for the PPO algorithm with two sets. Iterative agent training in Unity and AWS Sagemaker uses cloud computing resources for parallelized training. Emotional regulation and positive behaviour change are assessed empirically and through user studies. Virtual reality (VR) cooperative RL games may offer comprehensive treatments for anger management, according to the results, which show promising gains in emotional regulation and teamwork. By enhancing interest and effectiveness in satisfying emotional regulation demands, the study demonstrates that AI-driven therapies and "gamified therapy,"which mixes game components into therapy, can revolutionize mental health. © 2024 IEEE.

关键词： Reinforcement learning

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Weighted Average Ensembled Probability Pipeline of Pre-trained CNN Models for Hand Gesture Classification 15

Weighted Average Ensembled Probability Pipeline of Pre-train...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gunti, Rahul Rohan Reddy, B. Swathi, Gunti Sharath Kumar Reddy, G. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai601103 India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350370249

The automatic detection of sign language from hand gesture images is fundamental for effective human-computer interaction, especially for individuals with hearing and speech disorders. Achieving accurate detection and classification of sign language gestures is paramount. However, accurately recognizing these gestures frequently presents challenges due to dull backgrounds, variations in gesture orientation, and fluctuations in lighting conditions. Previous approaches tried to overcome these challenges but still the model performance lacks recognition accuracy and visual quality. To address this need for accuracy, we proposed a Weighted Average Ensembled Probability Pipeline composed of EfficientNetB7, ResNet-50, and Xception models. Leveraging the Extensive Hand Gesture Recognition Database (HGRD), comprising 20,000 images, our individual models achieved notable accuracies: 96.498 % for EfficientNetB7, 93.2 % for ResNet-50, and 90.175 % for Xception. Through ensemble learning, we integrate these models to attain an impressive overall accuracy of 98.95 %. This research contributes to robust gesture recognition system and underscores the significance of ensemble techniques in enhancing performance for Gesture recognition systems. © 2024 IEEE.

关键词： Gesture recognition

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Actual Facial Mask Recognition Utilizing YOLOv3 and Regions with Convolutional Neural Networks 1

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Thilagavathy, A. Naveen Raju, D. Priyanka, S. RamBalaji, G. Gopirajan, P.V. Sureshkumar, K. Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India Department of Information Technology Saveetha Engineering College Chennai India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

With the Coronavirus Disease (COVID-19) outbreak in 2019, it appears as though time has stopped. The World Health Organization (WHO) determined that wearing a face mask was essential for limiting virus transmission to combat pathogen transmission. Nevertheless, physically choosing how often individuals wear face masks in public is a momentary operation. The need to track people using respirators required creating an automated process. Nowadays, different deep and machine learning techniques can be used productively. All of the requirements for this type of model have been addressed in this work. The necessity and architectural layout of the suggested method have been thoroughly examined, followed by a rigorous inspection of several methodologies and their related comparison analyses. We investigate optimum settings for the sequential convolutional neural network model to accurately identify the existence of masks avoiding overfitting. Face identification categorically deals with distinguishing a specific group of entities, i.e., face. It has numerous applications, such as autonomous driving, education, surveillance. The prevalent practice reconfigures the images before fitting them into the network to surmount the inhibition. Some face collections are head turn, tilt, and slant with multiple faces in the frame and different types of masks with different colors. The proposed method consists of a cascade classifier and a pre-trained Convolutional Neural Network (CNN), which contains two 2D convolution layers connected to layers of dense neurons. As grayscale rationalizes the algorithm and diminishes the computational requisites, it is utilized for extracting descriptors instead of working on color images instantaneously. It has categorical representation, and the data is converted to categorical labels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Lettuce Crop Yield Prediction Analysis using Random Forest Regression Machine Learning Model in Aeroponics System 2

Lettuce Crop Yield Prediction Analysis using Random Forest R...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Rajendiran, Gowtham Rethnaraj, Jebakumar Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350325799

Aeroponics is a type of soilless agriculture where plants are grown in a misted environment without the use of the soil. The process of yield prediction in aeroponics system for the lettuce crop is one of the key challenges because the crop yield is affected by variety of growth factors such as temperature, humidity, light and nutrient levels. Hence, to predict the lettuce crop yield in aeroponics, the machine learning models were utilized and these models can be trained on the historical data on crop yields and environmental variables. Here, in this work, the Random Forest (RF) regression is chosen as the machine learning model for yield prediction. The data used in this work were collected from the aeroponic lettuce farming tower. The purpose of the research work is to investigate the potential of the random forest model for efficient prediction of the lettuce crop yield in aeroponics system. The RF model can produce accurate prediction results of 92% which is better than the other regression algorithms in terms of forecasting lettuce crop production. Comparing this to the MAE of other regression models, which may be anywhere from 0.098 to 3.76 of the average reported yield, and it is observed that RF has a much more acceptable MAE. Hence, this work provides valuable information for farmers who are interested in utilizing this type of indoor farming to produce the high-yield of lettuce crops. © 2023 IEEE.

关键词： Lettuce

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QCVNET - Leveraging Quantum Technology in Convolutional Neural Networks for Improved Cardiovascular Disease Prediction 5

QCVNET - Leveraging Quantum Technology in Convolutional Neur...

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5th International Conference on Circuits, Control, Communication and computing, I4C 2024

作者： Karthigeyan, S. Diviya, M. Bhuvaneswari, R. Sabhanayagam, T. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Galgotias University UP Greater Noida India

ISBN: (纸本)9798331528539

The primary issue of global health today is Cardio-vascular diseases (CVDs) and thus requires accurate predictive models for detecting it early. Quantum technology combined with regular deep learning techniques are used by researchers here to improve the accuracy of forecast on heart diseases. While using an expanded dataset with clinical and demographic records, an innovative approach was found to use Quantum Convolutional Neural Network (QCVNET) structure that merged quantum layers into classical CNN framework with Principal Component Analysis (PCA) that allowed for consistency of features with respect to the quantum bits' number. The performance of the QCVNET was evaluated by comparing it to Quantum LSTM, Quantum DNN, Quantum GRU, and Quantum RNN, which are other quantum models enhanced with conventional neural networks, and other traditional non-quantum models like SVM, RF, RNN and DNN, using different indicators of quality like f1-score or recall. In early CVD prediction, the QCVNET model produced an impressive accuracy of 91.73%, beating all other models, showcasing the potential impact of quantum-enhanced deep learning for timely diagnosis and personalized treatment strategies while providing means to reduce the global burden of cardiovascular diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年第13期12卷 25757-25772页

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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LoRa WAN Architecture with Collusion Management and Collusion Avoidances Schemes in MAC Layer

LoRa WAN Architecture with Collusion Management and Collusio...

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International Conference on computing and Machine Learning, CML 2024

作者： Saravanan, N. Kayalvizhi, R. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India

ISBN: (纸本)9789819778386

Long Range (LoRa) is the most widely used technology for significant attention in recent years due to their ability to enable long-range, low-power connectivity, making it an ideal communication system for Internet of Things applications. LoRa radio is considered as the physical layer, MAC layer which was specifically developed for low power IoT devices. LoRa is one of the technologies in LPWAN, which demonstrated remarkable potential in various domains used for IoT applications. This research paper presents a comprehensive study of LoRa technology, covering its LoRa principles (Physical Layer, MAC Layer, Network layer) Communication Mechanism, Application usage. Through an in-depth analysis of the key features and advantages of LoRa, this paper aims to provide a holistic understanding of the technology and its potential implications for the future of IoT in Collusion management and collision avoidance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information management

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Systematic Literature Review of VANET Simulators: Comparative Analysis, Technological Advancements, and Research Challenges 1

Systematic Literature Review of VANET Simulators: Comparativ...

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1st International Symposium on Parallel computing and Distributed Systems, PCDS 2024

作者： Almutairi, Reham Bergami, Giacomo Morgan, Graham School of Computing Newcastle University United Kingdom College of Computer Science and Engineering University of Hafr Albatin Saudi Arabia School of Computing Newcastle University Newcastle Upon Tyne United Kingdom

ISBN: (纸本)9798350349658

Vehicular Ad-hoc Networks (VANETs) are integral to the future of smart vehicles and transportation, facilitating real-time information sharing and communication among vehicles to enhance safety, improve traffic flow, and reduce accidents. With the rise of self-driving vehicles and emerging technologies such as 5G and edge computing, new challenges arise for VANET simulators. These simulators must now accommodate dynamic environments, necessitating features such as high scalability, real-time large-scale experiment support, and node mobility. Furthermore, they should support battery management, recharging during movement, and integrate renewable energy sources. This survey evaluates current VANET simulators against these criteria, comparing their features, discussing advancements in VANET technology, and identifying ongoing research challenges. Our findings reveal that no existing VANET simulator fully supports the integration of renewable energy sources or the real-time recharging of vehicle batteries. Additionally, there is a significant gap in simulating the effects of encryption without implementing specific encryption algorithms. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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