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Enhanced Aerial Vehicle Detection Using a Customized YOLOv8 Model for real-World applications 9

Enhanced Aerial Vehicle Detection Using a Customized YOLOv8 ...

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9th international conference on Communication and Electronics systems, ICCES 2024

作者： Thalapilly, Maanav Agasti, Ketone Nambiar, Niranjan S. Suresh, Ananthu Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350377972

Aerial vehicle detection is a critical function in surveillance, disaster response, and many defense systems. In this paper, a customized model, which extends the YOLOv8 model to detect aerial vehicles with exact precision and efficiency with four classes-categorized as helicopters, airplanes, drones, and UAVs-is developed. The dataset comprises 5,050 labeled images of various sources, augmented for robustness, and formatted in the YOLO-compatible structure for smooth training. The proposed approach integrates optimized depth and width multipliers for reducing complexity of computation at a preserved high detection accuracy. Its efficiency reflects in an mAP of 98.7%, which is superior to YOLOv8 (97.1%) and YOLOv5 (95.8%). Comparison in the two regimes indicates that the work offers superior precision and computational efficiency, hence suitable for real-world scenarios where resources are constrained. Results confirm the proposed approach is efficient in dealing with the cases of small objects, overlapping entities, and complex backgrounds, and its potential could be for further development of aerial surveillance technologies. © 2024 ieee.

关键词： Aircraft detection

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Risk Modelling and Prediction of Financial Management Using FTL-CSGRU Approach 1

Risk Modelling and Prediction of Financial Management Using ...

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1st ieee international conference on Electronics, computing, Communication and Control Technology, ICECCC 2024

作者： Mothilal Nehru, G. Kudare, Roopali P. Karnawat, Amrita Tatia Rohini, J. Singh, Saurabh Nishant, Neerav SRM UNIVERSITY Faculty of Science & Humanities Department of computer Applications Chengalpattu India PCET's S B Patil Institute of Management Department of MBA Maharashtra Nigdi Pune India Indira School of Business Studies Tathawade Maharashtra Pune India Erode Sengunthar Engineering College Computer Science and Engineering Tamil Nadu Perundurai India Prestige Institute of Management and Research Department of Finance Indore India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350371802

The need of building information technology is becoming more evident to those working in corporate financial management as the digital era arrives. An increasing number of modern societies are using enterprise resource planning (ERP) systems for their financial management needs. That way, companies can collect financial data in real-time and analyze it later. The steps of preprocessing, feature extraction, and training the model are all sequentially laid out. When it comes to statistical approaches used for data preprocessing, z-score normalization, decimal scaling, and min-max all offer very accurate results. You get the greatest results with MCML when you compare it to NCA, PCA, and Cluster-cluster LDA, the four approaches that comprise feature extraction. The suggested method employed an FTL-CSGRU strategy for model training. This method seems antiquated in comparison to the cutting-edge FTL and GRU. The statistics showed a significant improvement with an accuracy rating of 97.36%. © 2024 ieee.

关键词： Enterprise resource planning

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Information system for flood monitoring based on IoT and AI 2

Information system for flood monitoring based on IoT and AI

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2nd international conference on Smart Technologies and systems for Next Generation computing, ICSTSN 2023

作者： Wilson, Allan J Pon Bharathi, A. Anoop, M. Angelin Jeba Malar, J. Amrita College of Engineering and Technology Department of Electronics and Communication Engineering Nagercoil India Alpha Arts and Science College Department of Computer Applications Chennai India A.M.Jain College Department of Computer Science Chennai India

ISBN: (纸本)9798350348002

One of the most significant areas where realistic solutions have been put into place to minimise harm is flood management. In this research project, we are creating an IoTenabled adaptive AI technique for effective fluid management. Floods in Kerala, India, have recently seriously damaged the environment, infrastructure, and population. Various IoT devices gathered information on temperature, precipitation rates, and other variables. This study uses a real-time flood visualisation technology to provide an information system for flood monitoring. A system is constructed by first creating two systems: a client system comprised of hardware for detecting floods and a server system comprised of software for monitoring floods and distributing the collected data. All of the information on the flood in Kerala is compiled and split in halves, with the first set serving as training data and the second as test data. The suggested hybrid model is trained using six different machine learning models, and it achieves a maximum accuracy of 99.64%. We were able to prevent losses because to the system's seamless design. Research reveals that the client of the flood detection system may deliver real-time data on flood levels, weather conditions, and temperatures. Additionally, data from client systems can be collected by flood monitoring information systems and stored in MySQL. © 2023 ieee.

关键词： Visualization

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real-time Sliding Mode Fault Diagnosis for a Three-Wheeled Omnidirectional Mobile Robot 18

Real-Time Sliding Mode Fault Diagnosis for a Three-Wheeled O...

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ieee 18th international conference on Automation Science and Engineering (ieee CASE)

作者： Lizarraga, Adrian Begovich, Ofelia Ramirez, Antonio Cinvestav Guadalajara Dept Elect Engn Zapopan 45019 Jalisco Mexico

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

This work introduces the design of a sliding mode fault diagnoser capable of diagnosing simultaneous faults in a three-wheeled omnidirectional mobile robot. The approach proposes an augmented system comprising the system states and the dynamics of the faults. Based on this, a sliding mode observer is designed to estimate both, the system state and fault magnitudes, allowing the fault diagnosis. This approach leads to light and efficient implementations, which is suitable for real-time applications. The approach has been tested in a real mobile robot showing satisfactory results.

关键词： Fault diagnosis Computer aided software engineering Codes Automation Observers Robot sensing systems real-time systems

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A Multi - User Effective Computation Offloading Mechanism for MEC System: Batched Multi-Armed Bandits Approach 25

A Multi - User Effective Computation Offloading Mechanism fo...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Li, Hangfan Shi, Lin Zhong, Xiaoxiong Wang, Xinghan Ji, Yun Zhang, Sheng Guilin University of Electronic Technology Guilin541004 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518000 China

ISBN: (纸本)9798350330014

With the development of 5G technology, mobile edge computing (MEC) is becoming a useful architecture, which is envisioned as a cloud extension version. Users within MEC system could save plenty of time on data transmission. Algorithms within Multi-armed bandits (MAB) framework are powerful tools helping users offloading tasks to their best servers. However, as the system scale growing, servers will face higher pressure and users will waste more time on offloading tasks. The situation by applying traditional MAB algorithms is more serious. Therefore, in this paper, we propose a Batch-based Multi-user Server Elimination (BMSE) to solve such problem. BMSE contains two sub-algorithms, BMSE in User Level (BMSE-UL) and BMSE in System Level (BMSE-SL). BMSE- UL is applied by users for initial purpose which will help themselves discard servers with obvious bad performance. BMSE-SL is applied in the MEC system which could offload tasks from different users collectively. Furthermore, we establish the optimality of the proposed algorithms by proving the sub-linearity convergence of their regrets and demonstrate the effectiveness by extensive experiments. © 2023 ieee.

关键词： Mobile edge computing

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Tell Me Where To Go: Voice-Controlled Hands-Free Locomotion for Virtual reality systems 30

Tell Me Where To Go: Voice-Controlled Hands-Free Locomotion ...

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30th ieee conference Virtual reality and 3D User Interfaces (ieee VR)

作者： Hombeck, Jan Voigt, Henrik Heggemann, Timo Datta, Rabi R. Lawonn, Kai Univ Jena Jena Germany Univ Hosp Cologne Cologne Germany

ISBN: (纸本)9798350348156

As locomotion is an important factor in improving Virtual reality (VR) immersion and usability, research in this area has been and continues to be a crucial aspect for the success of VR applications. In recent years, a variety of techniques have been developed and evaluated, ranging from abstract control, vehicle, and teleportation techniques to more realistic techniques such as motion, gestures, and gaze. However, when it comes to hands-free scenarios, for example to increase the overall accessibility of an application or in medical scenarios under sterile conditions, most of the announced techniques cannot be applied. This is where the use of speech as an intuitive means of navigation comes in handy. As systems become more capable of understanding and producing speech, voice interfaces become a valuable alternative for input on all types of devices. This takes the quality of hands-free interaction to a new level. However, intuitive user-assisted speech interaction is difficult to realize due to semantic ambiguities in natural language utterances as well as the high real-time requirements of these systems. In this paper, we investigate steering-based locomotion and selection-based locomotion using three speech-based, hands-free methods and compare them with leaning as an established alternative. Our results show that landmark-based locomotion is a convenient, fast, and intuitive way to move between locations in a VR scene. Furthermore, we show that in scenarios where landmarks are not available, number grid-based navigation is a successful solution. Based on this, we conclude that speech is a suitable alternative in hands-free scenarios, and exciting ideas are emerging for future work focused on developing hands-free ad hoc navigation systems for scenes where landmarks do not exist or are difficult to articulate or recognize.

关键词： Human-centered computing Human computer interaction (HCI)- -computing methodologies- Artificial intelligence Natural language processing Speech recognition

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Performance and Power Consumption of real-time Task Scheduling for embedded systems Devices

Performance and Power Consumption of Real-Time Task Scheduli...

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ieee international conference on Consumer Electronics (ICCE)

作者： Gustavo M. Torres João V. L. De Souza João G. F. Aguiar Janislley O. de Sousa Sidia Institute of Science and Technology Manaus Brazil

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

real-time Operating systems (RTOS) play a pivotal role in the embedded systems domain by ensuring timely and predictable task execution. This characteristic is crucial for applications where delayed or missed deadlines can lead to system failure or degraded performance. Therefore, this aspect must be explored for constrained devices in Internet of Things (IoT) applications. This study analyzes three ready queue algorithms and varying CPU frequencies and low-power states in Zephyr RTOS, focusing on how prioritizing critical tasks impacts performance and energy consumption. Our results indicate that evaluating CPU task scheduling performance and power consumption provides valuable insights for enhancing energy savings in IoT applications. Optimizing active power consumption improves energy savings by completing tasks faster at higher frequencies and switching to a deep sleep state.

关键词： Performance evaluation Energy consumption time-frequency analysis Power demand embedded systems Energy conservation Switches real-time systems Internet of Things time factors

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Design of Long-Term Evolution Based Mobile Edge computing systems to Improve 5G systems 2

Design of Long-Term Evolution Based Mobile Edge Computing Sy...

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2nd international conference on Edge computing and applications, ICECAA 2023

作者： Arun, V. Azhagiri, M. Srm Institute of Science and Technology Tamilnadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Campus Tamilnadu Chennai India

ISBN: (纸本)9798350347579

Mobile edge computing is an emerging technology that enables mobile devices to access cloud services with reduced latency by moving computing, storage, and network control closer to the network edge. This advancement allows mobile devices to operate for longer periods on a single charge. With the advent of 5G communications, there is increasing interest in exploring the integration of mobile edge computing within existing mobile networks. The proposed work aims to develop a portable 5G computing system that offloads computational tasks to edge servers, thereby reducing latency and improving energy efficiency. The work focuses on designing efficient workload allocation algorithms to optimize the distribution of tasks to the supported servers. By implementing and evaluating a practical mobile-based computing system based on Long-Term Evolution (LTE), this research aims to demonstrate the real-world effectiveness of such mobile edge computing programs, with particular emphasis on reducing delays and enhancing energy efficiency. © 2023 ieee.

关键词： Computation offloading

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Advancement of Drug Discovery Using Artificial Intelligence and Machine Learning

Advancement of Drug Discovery Using Artificial Intelligence ...

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2024 ieee international conference on computing, applications and systems, COMPAS 2024

作者： Nilima, Sadia Islam Hossain, Md Azhad Sharmin, Sadia Rahman, Rukshanda Esa, Hammed Manik, Mia Md Tofayel Gonee Hasan, Rakibul International American University Department of Business Administration 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States International American University Department of Information Technology Wilshire Blvd STE 1000 Los AngelesCA90010 United States Westcliff University Department of Computer Science 17877 Von Karman Ave 4th Floor IrvineCA92614 United States Westcliff University Department of Business Administration 17877 Von Karman Ave 4th Floor IrvineCA92614 United States

ISBN: (纸本)9798331529765

The current boom in artificial intelligence and machine learning has significantly reduced human labor and greatly improved quality of life. This paper explores how AI and ML are being applied to streamline drug discovery and development, enhancing both efficiency and accuracy. Nevertheless, the limited effectiveness, distribution to unintended targets, time-consuming nature, and exorbitant expenses provide significant obstacles and difficulties that affect the process of drug design and development. The study involved an extensive literature review of numerous research articles and effectively highlight and discuss about the application of various advanced algorithms for drug discovery. The study highlighted how AI and ML contribute to reducing the time and cost associated with drug development and discovery. These technologies allow for the use of simulations, which can potentially obviate the need for clinical trials, and enable researchers to conduct extensive studies on various compounds without experimental trials. The results illustrate the extensive application of AI and ML in drug discovery, indicating a promising future for these technologies. They hold significant potential for further exploration and application in drug discovery and development by researchers, students, and the pharmaceutical industry. Finally, the progress in AI and deep learning presents a promising chance for the rational development and exploration of drugs, ultimately benefiting humanity. © 2024 ieee.

关键词： Drug discovery

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IDENTIFICATION AND CLASSIFICATION OF FINGERPRINTS USING AUTOMATED DEEP-LEARNING TECHNIQUES

IDENTIFICATION AND CLASSIFICATION OF FINGERPRINTS USING AUTO...

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2024 international conference on IT and Industrial Technologies, ICIT 2024

作者： Bano, Rida Zia, Muhammad Azam Asif, Muhammad Noureen, Ayesha Adnan, Ahmad Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Lahore Pakistan

ISBN: (纸本)9798331504465

Fingerprint Identification systems (AFIS) face significant challenges in real-time applications due to the vast number of comparisons required for large fingerprint databases. To address this, we present a deep learning-based fingerprint categorization technique to enhance individual identification efficiency. To address This categorization technique uses deep learning to find specific fingerprint information. Specific fingerprint information, including left-right hand classification, sweat-pore classification, scratch classification, and finger classification, was intended to be distinguished by the suggested method. The fingerprint dataset Scoring was collected from a public fingerprint dataset. The proposed CNN model, implemented in Python and Google Colab, enhances biometric systems effectiveness by using deep learning techniques like Resnet 50, MobileNet, LeNet, and Hybrid model, demonstrating its strengths. The results of the planned research would be helpful for business and for assisting investigators in connecting one crime scene to another involving the same suspect. © 2024 ieee.

关键词： real time systems

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