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Systematic Review of GAN for Enhancing Efficiency in AI in Gaming

Systematic Review of GAN for Enhancing Efficiency in AI in G...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Singh, Jasdeep Islam, Sardar M. N. Tewatia, Manav Garg, Deepanshu Fatima, Neda School of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad India Victoria University Department of Computer Science and Engineering Melbourne Australia School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798350388800

A thorough analysis of Generative Adversarial Networks (GANs) and how they might be used to improve AI gaming efficiency is presented in this research. Game developers now have never-before-seen possibilities for innovation, immersion, and personalization thanks to GANs, which have become a potent tool for producing realistic data samples across a variety of fields. This review investigates the architecture, training methods, applications, difficulties, and potential future directions of GANs in AI gaming through a thorough analysis of the body of research and publications. The review demonstrates how GANs can transform the game industry by highlighting their many uses in procedural content generation, texture synthesis, character animation, environment design, and content customization. Nevertheless, there are certain difficulties in integrating GANs into AI games, such as computational resources, mode collapse, training instability, and assessment *** well as legal matters. To tackle these obstacles, multidisciplinary research endeavors and cooperation across academic institutions, corporate entities, and regulatory agencies are necessary. Going forward, new avenues for study and developments in GAN technology hold great potential for getting over these roadblocks and opening up fresh possibilities in AI gaming. We can create a future where AI powered gaming experiences change the parameters of interactive entertainment and provide players globally with more engaging, varied, and personalized gaming experiences by utilizing the possibilities of GANs and addressing the related obstacles. © 2024 IEEE.

关键词： Generative adversarial networks

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Predictive Modeling and Analysis of COVID-19 with Machine Learning Techniques 5

Predictive Modeling and Analysis of COVID-19 with Machine Le...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Pant, Garima Pant, Janmejay Kotlia, Priyanshi Singh, Devendra Khulbe, Sumit Bhatt, Jaishankar Graphic Era Hill University Department of PC/PDP Bhimtal Campus India Graphic Era Hill University Department of Computer Science and Engineering Bhimtal Campus India Amrapali group of institutes Department of Computer Applications Uttarakhand Haldwani India SSJ University Department of Information Technology Uttarakhand Almora India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350367171

For successful management of COVID-19 pandemic outbreak, case detection has to be done through Machine Learning (ML). The Machine Learning (ML) techniques can be used to process the patient's data, such as imaging and results of various tests, to detect Covid19 cases. This study aims to present a computer learning model for COVID-19 severity and course prediction using initial manifestations. As a result of training and classification, supervised learning methods are included in the proposed approach in order to detect the presence of the corona virus COVID-19 and classify it. Estimation and predication of COVID-19 infection can be accomplished by machine learning principle and statistical and computational models on how to assess negative and positive impacts of the virus. The data was analyzed using two machine learning models: Random forest (RF) and Logistic regression (LR) have been chosen as classification algorithms to build models based on the obtained data. The results found out that both LR and RF were equally accurate knowing that the overall accuracy recorded was good. The following model provides a wayof accurately categorizing patients, making it easier for health care professionals and policymakers to track the risky individuals. © 2024 IEEE.

关键词： COVID-19

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Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques 5

Weapon Detection Identification and Classification for DCNN ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ashok Kumar, J. Dhivya, G. Selvaraj, P. Karthikeyan, V. Ganesh Ramesh, S.P. Lakshmi, S. Jeya Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science and Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computing SASTRA Deemed to Be University Thanjavur Tamil Nadu Thirumalaisamudram India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

Over 850,000 people die every year as a direct result of gun violence, yet civilians hold more than 85% of the world's weapons. Detecting weapons via manual surveillance has not been successful. It is critical to automate a system that detects weapons. A large number of convolution neural network CNN-based automated weapon identification systems have shown promising results. These methods are crucial for weapon identification but expensive. These devices produce a large amount of false negatives since monitoring videos are not high enough quality or visible enough to identify weapons. Finding the sweet spot between fast detection and a few false positives and negatives in weapon detection is the main goal of this research. In addition to defining an Area of Interest, the suggested structure uses YOLO. The models use previously processed images with backgrounds that have been blurred using a median filter. Metrics such as F1 scores, accuracy, recall, False Positive, and False Negative will be used to assess the suggested architecture. This investigation shows that YOLO-v5 can quickly identify and has a high recall rate. The deep convolution neural network DCNN required 0.17 ms for each frame, but this one only required 0.010 ms, which would indicate that security and weapon detection have good promise. Starting with a labeled collection of weapon and non-weapon photos is the initial stage in DCNN and YOLOv5 weapon detection. Use YOLOv5's ability to recognize multiple things within frames to train a real-time object localization and identification model. DCNNs categorize weapons and extract complex data effectively. DCNN classifies objects, whereas YOLOv5 detects, bounds, and tracks them. Optimize both models' hyperparameters and apply data augmentation for better performance. Install the integrated system on cutting-edge equipment for real-time weapon identification and classification. © 2024 IEEE.

关键词： Median filters

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An Effective Forest Fire Prediction based on Contrastive Vision Transformer with Pool Former 2

An Effective Forest Fire Prediction based on Contrastive Vis...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. Husain, Saef Obad Ezhil, G. Indhumathi, V. Revathi, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Humanities Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Applications Sona College of Arts and Science Salem India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Since years, forest fires are a significant concern globally, causing damage to ecosystems and human settlements. For effective prevention and mitigation predicting forest fires is important, different factors contribute to forest fires which includes human activities, topography, climate change and weather conditions. Traditional approaches for predicting forest fires had faced significant challenges which include limited validation of prediction models and lack of standardization. Therefore, this paper proposes Contrastive Vision Transformer (CViT)-Pool Former for predicting forest fire which is employed on Forest Fire Big Data dataset which enabled better comparability. This dataset is preprocessed by using data augmentation, noise reduction and data enhancement techniques. Then, the features are extracted by using CViT which extracted optimal features based on weather conditions and captured contextual relationships by maximizing mutual information. After that, Pool Former light weight attention module is used for predicting fires which gave more accurate predictions by enabling targeted prevention and mitigation, minimized unwanted deployments and resource waste, boosted prediction accuracy by token fusion and enhanced representation learning. The proposed CViT-Pool Former method established better results including accuracy (92.8%), precision (89.4%), recall (91.4%) and F1-score (90.2%) when compared with existing Region Proposed Convolutional Neural Network (R-CNN). © 2024 IEEE.

关键词： Premixed flames

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A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning 3

A Hybrid Edge-Cloud Computing Approach for Energy-Efficient ...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Singh, Jagendra Dabas, Partibha Bhati, Sunita Kumar, Santosh Upreti, Kamal Shaik, Nazeer School of Computer Science Engineering and Technology Bennett University Greater Noida India Dr Akhilesh das Gupta Institute of Technology and Management Dept. of Computer Science & Engineering Delhi India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India Department of Computer Science Ghaziabad India SRIT-Autonomous Anantapur Department of Computer Science Engineering India

ISBN: (纸本)9798350342338

This paper explores the novel application of Deep Reinforcement Learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption. © 2023 IEEE.

关键词： Cloud Data Centers Deep Reinforcement Learning Edge Computing Energy Efficiency Smart Surveillance

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Apple Fruit Flower Detection Using Auto-Encoder Convolutional Neural Network

Apple Fruit Flower Detection Using Auto-Encoder Convolutiona...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Srivastava, Yashi Rawat, Ruchira Shrivastava, Anurag Rashmi Bansal, Saloni Gupta, Priyanka Al-Fatlawy, Ramy Riad Lloyd Institute of Engineering & Technology Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Department of Computer Science and IT Noida International University U.P. India Department of Computer Engineering and Applications GLA University Mathura India Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

The yield estimation task altogether relies upon the way toward identifying and checking the quantity of fruits on trees. In production of fruit, basic yield the board choices are guided by bloom intensity, i.e., the quantity of flowers presents in a plantation. The intensity of Bloom technique is still commonly assessed by methods for human visual investigation. Mechanized PC vision frameworks for flower recognizable proof depend close by designed procedures that work just under explicit conditions and with restricted *** work comprises of four significant advances, (I) System preparing for Fully Convolutional Network (FCN), (ii) Preprocessing, (iii) Component Extraction, (iv) Division. Initially, a strategy for assessing high-resolution pictures with deep FCN on Graphics Processing Unit (GPU). Then, secondly a non-linear and linear algorithms are presented for lessening the image noise, so the exact flower identification can be ensured. Thirdly, highlight extraction is valuable to diminish the quantity of assets required for handling without losing significant or applicable data. By applying Local Binary Pattern (LBP), surface example likelihood can be summed up into a histogram. At last, isolate a image with high resolution into sub patches, assess all patches with the help of AECNN, at that point apply the refinement calculation on acquired scoremaps to figure final version of the mask segmentation. Trial results are led utilizing two datasets on flower pictures of AppleA and AppleB. Results are estimated regarding the measurements like Precision(P) and Recall(R). © 2024 IEEE.

关键词： Graphics processing unit

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Deep Learning-Based Image Analysis to Track Vehicles 2

Deep Learning-Based Image Analysis to Track Vehicles

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prabakaran, P. Jayasudha, R. Sandhu, Mukta Gupta, Sandeep School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Dr.N.G.P.Institute of Technology Department of Mathematics Coimbatore India Shri Vishwakarma Skill University Skill Department of Computer Science and Engineering Haryana Palwal India Department of Electrical Engineering Dehradun India

ISBN: (纸本)9798350371055

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches. © 2024 IEEE.

关键词： Artificial Intelligence HD Map Traffic Monitoring System Vehicle Type Classification YOLOv4

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Application of Developmental Services based on IoT Efficiency 6

Application of Developmental Services based on IoT Efficienc...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Rawat, Vandana Singh, Neelam Bijalwan, Rahul Verma, Poonam Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of Computer Applications Graphic Era Hill University Dehradun India

ISBN: (纸本)9798350398496

Internet of things (IoT) is a fast and conforming technology, having a standard emergence primarily connected to the wireless communication between actuators, gadgets, and electrical services all in general referred to as instructions. Developmental services refer to the activities and services promoting community development and are considered as an integral part of developmental services, which include seamless and coherent experience. It is a phenomenon rich in technical skills and broader experiences consisting of mechanical frameworks and platforms, which aim towards selecting the most efficient and cost-effective tools. The collection of valuable information and relaying on them accurately are the two most important features provided by the efficient IoT services. © 2023 IEEE.

关键词： Efficiency

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Artificial Intelligence-Enabled regression model for used car price prediction

Artificial Intelligence-Enabled regression model for used ca...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Gaurav Pandey, Saroj Kumar Yadav, Dhirendra Prasad Singh, Kamred Udham Singh, Teekam Kumar, Ankit Gla University Department of Computer Engineering & Applications UP Mathura India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Deciding if the recorded cost of a pre-owned vehicle is a difficult errand, because of the numerous variables that drive a pre-owned car's cost. The focal point of this venture is creating AI models that can precisely anticipate the cost of a trade-in vehicle dependent on its highlights, to make educated buys. We'll be going to look at changed models and calculations which can anticipate the cost of pre-owned vehicles proficiently. More than 70 million passenger vehicles were shipped in 2016. This represents a significant increase in vehicle production over the past decade. As a result, the secondary market for used cars has grown rapidly in recent years. We'll be noticing various strategies and afterward propose the best-fit model to foresee the cost of pre-owned vehicles. Choosing whether a pre-owned vehicle merits the posted cost when you see postings online can be troublesome. A few components, including mileage, make, model, year, and so forth can impact the real worth of a vehicle. From the point of view of a dealer, it is likewise a quandary to value a pre-owned vehicle properly. In light of existing information, the point is to utilize AI calculations to foster models at anticipating utilized vehicle costs. © 2023 IEEE.

关键词： Decision trees

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Non Linear Grid Based Search Energy Optimization for AUV Aided Underwater Acoustic Sensor Network 10

Non Linear Grid Based Search Energy Optimization for AUV Aid...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Ayyadurai, M. Maniraj, S.P. Sujatha, K. Deeptha, R. Anu, D. Suresh Babu, T.N. Rajalakshmi Engineering College Department of Cse Chennai India Srm Institute of Science and Technology Department of Cse Chennai Ramapuram India Srm Institute of Science and Technology Department of It Chennai Ramapuram India Presidency College Department of Computer Applications Bangalore India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Melmaruvathur India

ISBN: (纸本)9798350353068

Recently, the significance of the Underwater Acoustic Sensor Network (UWASN) attracts the researchers due to the huge benefits of data collection obtained from the sensor nodes under the ocean. Resource exploration in underwater uses a sensor node for data sensing, collecting, processing and forwarding. These sensor nodes are energy constrained and often depleted, then becomes inactive. This occurrence may lead to network failure while computing optimal node selection, hence proposes non linear Grid-Based Search Energy Optimization (GBSEO) algorithm to find the optimal forwarding node with Autonomous Underwater Vehicle (AUV). The GBSEO algorithm considers the underwater Doppler Effect, sedimental drift and water current based on non linear searching and thereby improves the overall efficiency of the network by optimizing the energy of sensor nodes. AUV utilizes non linear optimization to improve the path selection based on GBSEO and thereby increases the network lifetime, energy efficiency and reduces the time delay. The results show that the proposed GBSEO algorithm outperforms the traditional algorithm Clustering Algorithm Optimization (CAO). © 2024 IEEE.

关键词： Autonomous underwater vehicles

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