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Evolution of Mobile Computing: From Text-Based to Visual-Based Interactions

Evolution of Mobile Computing: From Text-Based to Visual-Bas...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Akhilendra Pratap Sharma, Deeplata Soumya, K. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Deportment of Chemistry Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Cell computing has seen a marked evolution over the past many years, beginning with text-primarily based interactions among carrier and device customers and progressing to visually pushed reports primarily based on large screens and touch inputs. Early mobile phones featured bodily keypads that enabled users to interact with the tool via written instructions, such as coming in with cellphone numbers and sending text messages. This approach changed into nicely appropriate for brief data retrieval and typing in brief bursts. As the generation matured, touchscreens became a more popular entry method. The creation of gestures, such as swiping and multi-contact, revolutionized the manner customers interacted with their gadgets, allowing them to freely discover and access content in a green and intuitive way. Moreover, larger bodily sizes coupled with excessive-decision presentations allowed customers to control content and better appreciate the visuals easily. The evolution of cell computing has additionally created a platform for bringing collectively disparate technologies. Cellular apps, for example, are able to combine text, photographs, sound, and video to offer a multi-modal reveal for users. It has made using cell devices more engaging and enabled customers to interaction with complex structures in an optimized form thing. © 2024 IEEE.

关键词： Combine Computing Customers Interaction Photographs Revolutionized Touchscreens

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Prediction of Web Page Relevance in Focused Crawling Using Artificial Neural Networks 3

Prediction of Web Page Relevance in Focused Crawling Using A...

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

作者： Dhabliya, Dharmesh Alawadi, Ahmed Hussein Varkale, Akshay Aljanabi, Mohammad Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Deportment of Computer Science & Engineering Bhopal India Alirqia University Department of Computer Baghdad Iraq

ISBN: (纸本)9798350342338

In the rapidly expanding digital ecosystem, efficient web crawling is paramount to extract relevant information from the vast expanse of the internet. This research addresses the challenge of predicting web page relevance in focused crawling using Artificial Neural Networks (ANNs). The study leverages a curated dataset of web pages, annotated for their relevance to specific domains. A multi-layer perceptron architecture was employed for the ANN, with features extracted from the web pages serving as inputs. The model was trained and validated using a split of 70% training and 30% testing data. Experimental results indicate that the ANN achieved an accuracy of 93.20%, outperforming traditional methods by a margin of 8%. This demonstrates the potential of ANNs in enhancing the efficiency of focused web crawlers, ensuring more accurate data extraction in less time. The findings pave the way for integrating deep learning techniques into web crawling systems, optimizing them for the evolving digital landscape. © 2023 IEEE.

关键词： Artificial Neural Networks Data Extraction Deep Learning Techniques Focused Crawling Web Page Relevance

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Analysis for Text Detection on Scenic Images Using Deep Learning 3

Analysis for Text Detection on Scenic Images Using Deep Lear...

引用

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

作者： Vardhan, Harsh Joshi, Kamal Chhabra, Prachi Shrivastava, Aparna Kumar, Sunil Chhabra, Gunjan Jss Academy of Technical Education Department of Information Technology UP Noida India Csjm University Deportment of Information Technology UP Kanpur India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350342338

Text detection in scenic images is the process of precisely localizing instances of text in them. This detection is required for a numerous computer vision applications including text recognition, autonomous vehicles, script narrators, translation apps etc. Text detection and localization refer to the process of identifying and locating text within an image or document. Scene text detection was mostly limited to putting together handcrafted low-level attributes of the texts contained in images until the emergence of deep learning in this area. A combination of methods or exploring domainspecific approaches help in enhancing the efficacy of detecting text in scenic images. In this paper, we have implemented the performance of three text detection models EAST, CRAFT and Text Fuse Net on two benchmark public datasets used for detection and localization. Experiments on two benchmark datasets show that the Text Fuse Net achieves state-of-the-art performance. Specifically, we have achieved an F measure of 94.6% and 92.2% on IC-2013 and IC-2015. respectively, followed by CRAFT [89.8%, 84.8%] and EAST [85.3%, 80.7%1. © 2023 IEEE.

关键词： CRAFT EAST IC-13 IC-15 Text Fuse Net

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Potato Leaf Disease Detection and Classification Using Deep Learning Technique 4

Potato Leaf Disease Detection and Classification Using Deep ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Sharma, Aditi Singh, Vineeta Sharma, Chandani Ansari, Ghazala Kumar, Santosh Joshi, Kapil Astana It University Department of Computer Science and Engg. Astana Kazakhstan Chhatrapati Shahu Ji Maharaj University Department of Computer Science and Engineering Kalyanpur Uttar Pradesh U.P. Kanpur208024 India Department of Cse Ambale Haryana Mullana India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Ghaziabad India Sharda School of Basic Sciences and Research Sharda University Department of Mathematics UP Greater Noida201310 India Uttaranchal University Deportment of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350387490

With the advancement of agriculture technology and the use of machine learning in diagnosis plant disease, it becomes more effective to research in plant health agriculture development. This paper talks about how deep learning and convolutional neural networks can help identify potato diseases. It looks at some research that used these methods to accurately recognize and group potato diseases. It also covers topics like weakly supervised learning, spectral imaging, and combining CNN and RNN for potato disease detection. It shows how transfer learning and hyperspectral imaging can improve how well deep learning models diagnose potato diseases. It compares different methods like k-means, multiclass SVM, deep belief networks, and convolutional ne-ural networks to see how good deep learning models are. It suggests that deep learning can find and group different types of potato diseases and proposes a deep learning model that can automatically diagnose crop diseases and pests on potatoes. It ends by saying deep learning can provide reliable and early warning systems for farmers and improve farming practices. © 2024 IEEE.

关键词： Agriculture CNN Deep learning Potato leaf RNN

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Evolution of Mobile Computing: From Text-Based to Visual-Based Interactions

Evolution of Mobile Computing: From Text-Based to Visual-Bas...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Akhilendra Pratap Singh Deeplata Sharma Soumya K Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Deportment of Chemistry Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

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Prediction of Web Page Relevance in Focused Crawling Using Artificial Neural Networks

Prediction of Web Page Relevance in Focused Crawling Using A...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Dharmesh Dhabliya Ahmed Hussein Alawadi Akshay Varkale Mohammad Aljanabi Department of Information Technology Vishwakarma Institute of Information Technology Pune India College of Technical Engineering The Islamic university Najaf Iraq Deportment of Computer Science & Engineering IES College of Technology Bhopal India Department of Computer Alirqia university Baghdad Iraq

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Metaheuristic Optimization for Path Planning in UAV Networks for Long-Distance Inspection Tasks

Metaheuristic Optimization for Path Planning in UAV Networks...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： J. Akshya M. Manochitra R. Indu Poornima T. Jeevitha M. Saravanakumar M. Sundarrajan Mani Deepak Choudhry Department of Computational Intelligence SRM Institute of Science and Technology Chennai Computer Science and Engineering (Cyber Security) Dr. N.G.P. Institute of Technology Coimbatore Department of Information Technology KGiSL Institute of Technology Coimbatore Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Networking and Communications SRM Institute of Science and Technology Chennai Deportment of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

With broad applicability in surveillance, delivery, and environmental monitoring, multi-drone path planning has received a great deal of attention recently. So far, wide applicability has been explored by various applications of Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, which have already established reliable solutions to navigation and coverage through drone path optimization. However, in dynamic, obstacle-dense environments, these algorithms fail to adapt in real-time with energy efficiency. Traditional GA and PSO approaches can be effective for generating an initial path but fail to respond to collision avoidance and efficient resource management. In complex and unpredictable conditions, ACO fails to converge rapidly, which leads to suboptimal paths and increased energy consumption. To overcome such barriers, we present here an HGARL for enhanced multi-drone path optimization. The proposed algorithm thus combines the population-based search capability of GA with RL's adaptive learning ability, therefore providing an algorithm that in addition to finding optimal paths adjusts the paths according to changing environmental situations. HGARL presents better performance compared to existing methods. In path convergence, it reaches up to 15%, and in energy consumption, it reduces up to 20% with safe collision-free navigation. The strong potential of HGARL for robust, efficient, and adaptive solutions of real-time multi-drone operation in complex and dynamic environments has set a new benchmark for UAV path planning.

关键词： Energy consumption Navigation Heuristic algorithms Reinforcement learning Path planning Real-time systems Reliability Genetic algorithms Drones Convergence

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An Investigation of Smart Detection for Small Lung Tumor with Tumor Pattern Recognition Algorithm

An Investigation of Smart Detection for Small Lung Tumor wit...

引用

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Hariharan, G. Prasanth, P. Devarani, P. Arthi Sajana, T. Indhumathi, C. Kumar, Ashok Malla Reddy University Deportment of Artificial Intelligence and Machine Learning Telangana Hyderabad500100 India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamil Nadu Chennai600062 India R. M K College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Thiruvallur601206 India KoneruLakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram522502 India R.M. K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai601206 India BanasthaliVidyapith Department of Computer Science Rajasthan304022 India

ISBN: (纸本)9798350334357

The Small-cell lung tumor is the prime public concern, resulting in increased mortality. Various therapeutic approaches have made progress in the handling of small-cell lung tumor. It's considered the backbone of cytotoxic chemotherapy treatment. However, clinical research shows that because small-cell lung tumor is chemically approachable, high response rates among small-cell lung tumorinfecters are generally not translated into cure. Therefore, it is high time to make progress in small-cell lung tumor research because every success in clinical trials is fought, and every failure is a lesson. A recent study of small-cell lung tumor has improved the prognosis of patients. Some notable research areas have provided new options for patients by conducting multiple clinical trials. In this paper smart edge detection based small lung tumor identification method was proposed. This model operates with the pattern recognition algorithm model. This algorithm smartly finds the edges of the tumor cells and provides the edge results. This will helpful for the doctors to identify the tumor part exactly. © 2023 IEEE.

关键词： Tumors

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Analysis for Text Detection on Scenic Images Using Deep Learning

Analysis for Text Detection on Scenic Images Using Deep Lear...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Harsh Vardhan Kamal Joshi Prachi Chhabra Aparna Shrivastava Sunil Kumar Gunjan Chhabra Department of Information Technology JSS Academy of Technical Education Noida UP India Deportment of Information Technology School of Engineering and Technology (UIET) CSJM University Kanpur UP India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India

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AI-Driven Control Strategies for Power Electronics Converters 9

AI-Driven Control Strategies for Power Electronics Converter...

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9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Lalitha, S.D. Danamaraju, Chaitanya Raj, M. Prabhu Kumar, Dinesh Sethuraman, R. Mohanaprlya, V. R.M.K. Engineering College Department of Computer Science And Engineering Tamil Nadu Kavaraipettai601206 India Rns Institute of Technology Department of Electrical And Electronics Engineering Tamil Nadu Bengaluru560098 India Dr. Mahalingam College of Engineering And Technology Department of Electrical And Electronics Engineering Tamil Nadu Pollachi642003 India Iet Dr. RammanoharLohia Avadh University Deportment of Electrical Engineering Uttar Pradesh Ayodhya224001 India Karpagam Academy of Higher Education Department of Electrical And Electronics Engineering Tamil Nadu Coimbatore641021 India Bannari Amman Institute of Technology Department of Electrical And Electronics Engineering Tamil Nadu Erode638401 India

ISBN: (纸本)9798350365092

Artificial intelligence (AI) in converter control techniques has transformed power electronics. The integration improved power electronic system adaptability, performance, and efficiency. This research examines power electronics converter AI-driven control techniques in detail. When dealing with complex and nonlinear systems, conventional power converter control approaches like PID controllers are limited. Neural networks, fuzzy logic, and machine learning algorithms offer promising solutions to these problems. AI-driven control approaches are used to inverters, rectifiers, and DC-DC converters in the research. The benefits of AI include greater robustness to parameter fluctuations, harmonic distortion reduction, and transient response. Artificial intelligence allows adaptive and predictive control, enabling intelligent and self-governing power electronic systems. These include large datasets, complex training, and real-time computations. Overall, power electronics converters with AI-powered control techniques affect their design and operation. Power electronics and artificial intelligence offer new opportunity to construct smart, adaptive, and high-performing energy conversion systems. This research is essential for researchers, engineers, and practitioners interested in the latest advances in artificial intelligence and power electronics. © 2024 IEEE.

关键词： Adaptive control systems

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