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Improving Accuracy in Object Detection Using Region-Based Convolutional Neural Network

Improving Accuracy in Object Detection Using Region-Based Co...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： T.M Kiran Kumar Srinivas Aluvala Preetam Mandal K. Saranya Karunakara Rai B Department of Computer Science & Engineering Siddaganga Institute of Technology Tumkur India School of Computer Science & Artificial Intelligence SR University Warangal India Department of ECE Siddhartha Institute of Technology and Sciences Hyderabad India Department of ECE Sona College of Technology Salem India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The object detection is a fundamental computer vision task that involves identifying and locating objects within a video or image. This technique employed various shape patterns for suggestion to recognize the certain objects in an input data. The existing methods have drawbacks like cannot identify the small region of objects in image. In this research, the Region-based Convolutional Neural Network (RCNN) is proposed for object detection. The dataset utilized for object detection is Caltech101 dataset and it is pre-processed by normalization and LAB color transformation to enhance the image quality. The preprocessed images are given into ResNet-50 based feature extraction and RCNN is utilized for classification. The performance of RCNN model is estimated through the performance measures of accuracy, precision, recall, specificity and f1-score. The RCNN attained high accuracy of 95.78%, precision of 94.81%, recall of 94.27%, specificity of 93.65% and f1-score of 92.49% which is comparatively superior than other existing methods like Histogram of Oriented Gradient (HOG) and Local Ternary Pattern (LTP), Hierarchical Max-poling (HMAX) and Fuzzy C-means.

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Smart Multiphase Power Converter in the Fault-Tolerant Machine Development for Aerospace Applications

Smart Multiphase Power Converter in the Fault-Tolerant Machi...

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International Conference on Science Technology engineering and Management (ICONSTEM)

作者： S. Aslam Madhavi Shamkuwar S. B G Tilak Babu J Suresh Pavithra G Natrayan L Electrical and Electronics engineering Chaitanya Bharathi institute of technology Proddatur Andhra Pradesh MCA Zeal Institute of Business Administration Computer Application and Research Pune Maharashtra Department of ECE Aditya Engineering College Surampalem Electrical & Electronics Engineering Ramachandra College Of Engineering Eluru A.P Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The aircraft industry has long used cutting-edge technologies to ensure equipment security, dependability, and performance. In aerospace applications, fault tolerance-the ability of a system to perform reliably despite errors or failures-is essential. Developing devices that work well even when things go wrong is vital. Using a Smart Multiphase Power Converter (SMPC) and a Model Reference Adaptive Control (MRAC) algorithm, this abstract improves aircraft fault tolerance. Multiphase power converters are increasingly used in airplanes due to their high power density, fault tolerance, and efficiency. The MRAC algorithm controls this operation. MRAC continuously optimizes system settings to match the reference model. MRAC algorithms adapt aeronautical control systems to maintain peak performance despite external interference, changing operational parameters, and broken parts. Combining the SMPC and MRAC algorithms in aerospace fault-tolerant machine design has many benefits. First, it checks the system's state and adjusts the controller parameters to account for any deviations from the ideal, ensuring great reliability. In conclusion, smart multiphase power converters and model reference adaptive control can improve aeronautical fault-tolerant machine architecture. This technology benefits the aircraft sector with its fault tolerance, efficiency, and adaptability. As aerospace technology advances, imaginative solutions are essential for safe and reliable future missions in Earth's atmosphere and beyond. Space operations can be safer and more efficient because to this study's implications for machinery design that can withstand extreme circumstances.

关键词： Performance evaluation Fault tolerance Adaptation models Atmospheric modeling Fault tolerant systems Terrestrial atmosphere Security

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A Development of Cloud Based Robotics Design Networks for Industry Applications

A Development of Cloud Based Robotics Design Networks for In...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Sathi G Neeraj Varshney Praveen Sharma B.Jasmine Punitha R. Sundar S P V SubbaRao Dept of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Engineering and Applications (CEA) GLA University Mathura Department of Computer Science & Engineering (CS & E) IES Institute of Technology and Management IES University Bhopal M.P India Department of Information Technology Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore TN Department of Electronics and Communication Engineering (ECE) Sreenidhi Institute of Science and Technology Hyderabad

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In modern robots, the usage of computationally expensive models involving deep neural networks, also referred to as DNNs, for tasks such as the localization of operations awareness, planning, and object recognition is becoming prominent. Nevertheless, resource-constrained machinery, such as low-power aerial vehicles, often lack the requisite internal computing resources to easily run cutting-edge simulations of neural networks. Cloud robotics appears as an answer, allowing robots to offload processing to centralized computers for greater precision models. Nonetheless, the ignored downside of cloud robots lies in the possible delay and data loss experienced during contact over crowded wireless networks. This study discusses the Robot Transferring responsibility Problem, exploring when and where robots should offload sense tasks that improve accuracy while reducing the costs involved with cloud communication. The method involves framing shifting as a sequence decision-making issue concerning robots and suggesting a remedy using sophisticated reinforcement learning. Through models and hardware tests employing advanced thinking DNNs, what was suggested sharing strategy improves vision task efficacy by 1.3 2.6 times as a result of standard strategies, allowing robots to increase their sense accuracy while incurring minimal communication via cloud costs.

关键词： computers Costs Accuracy Service robots Computational modeling Reinforcement learning Robot sensing systems

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Enhanced Task Offloading in Mobile Edge Computing: A Hybrid Approach Using Deep Q-Learning from Demonstrations and Heuristic Optimization

Enhanced Task Offloading in Mobile Edge Computing: A Hybrid ...

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Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Huishi Zhao Yitong Liu Xingcheng Liu Yi Xie Peiran Wu School of Electronics and Information Technology (SEIT) Sun Yat-sen University Guangzhou China Department of Electrical & Computer Engineering (ECE) University of Waterloo Waterloo Canada School of Information and Intelligent Engineering Guangzhou Xinhua University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

The increasing demands of delay-sensitive and resource-intensive applications in the IoT era have made Mobile Edge Computing (MEC) a promising solution for task offloading to nearby edge servers. Effective offloading in MEC requires strategies that balance energy consumption and meet strict deadline constraints to support the limited battery life of IoT devices and ensure low latency. Traditional heuristic algorithms, like DECO, efficiently manage energy and deadlines through predefined rules, while reinforcement learning (RL) methods adapt dynamically in changing environments. This paper proposes a hybrid model, DQfD-DECO, which combines the advantages of DECO and Deep Q-learning from Demonstrations (DQfD). By leveraging demonstration-based learning, our approach enhances initial offloading decisions and refines them through adaptive reinforcement learning. Comparative performance analysis in dynamic MEC scenarios reveals that DQfD-DECO achieves a superior balance of energy efficiency and deadline satisfaction, outperforming traditional rule-based methods in adaptability to fluctuating conditions.

关键词： Training Adaptation models Q-learning Multi-access edge computing Computational modeling Energy efficiency Data models Stability analysis Internet of Things Servers

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AI-based Prediction of Catheter-related Thrombosis Risk for Cancer Patients 24

AI-based Prediction of Catheter-related Thrombosis Risk for ...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Guo, Yaoqi Ma, Yun Shao, Zijian Bi, Weichen Wang, Yanfeng School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Department of Comprehensive Oncology National Cancer Center National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing China

ISBN: (纸本)9798400701726

Cancer patients face a heightened risk of venous thromboembolism (VTE), emerging as the second most prevalent cause of death within this population. Central venous catheterization (CVC), a routine procedure in cancer care, amplifies the VTE risk, leading to catheter-related thrombosis (CRT). Although traditional risk-assessment models and certain AI methods exist for VTE prediction, their capability and application in CRT risk prediciton for cancer patients remains limited. This paper addresses the shortcomings of current models (RAMs) by crafting a dedicated AI model to predict CRT risks for cancer patients. Leveraging a dataset encompassing 10,512 cancer patients undergoing catheterization over a decade, we meticulously select nine specific features for model construction, resulting in an impressive 0.794 AUROC in prediction, 54.9% higher than baseline. Furthermore, we estimate CRT-free probability using the Kaplan–Meier method. We also develop a WeChat Mini Program designed for efficient data collection and risk prediction, enhancing the efficiency of CRT risk detection for both doctors and patients. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Machine learning

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Modified Chaotic Grey Wolf Optimization Algorithm for Energy Aware in WSN

Modified Chaotic Grey Wolf Optimization Algorithm for Energy...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： L. M. I. Leo Joseph Gubbala Jahnavi Deepika P S Dinesh S. Vijayashaarathi N. Samanvita Department of ECE School of Engineering SR University Warangal India Malla Reddy Engineering College for Women Autonomous India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of ECE Sona College of Technology Salem India Department of Electrical & Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Wireless Sensor Network (WSN) is group of spatially distributed sensors which monitors environmental exchanges that data with each other over wireless channels. Energy aware in WSN are referred to as saving or balancing energy between nodes in WSN. Energy saving protocols are utilized to minimize energy consumed through WSN. Energy balancing protocols effectively distribute energy consumption throughout network. In this research, the Modified Chaotic Grey Wolf Optimization (MCGWO) algorithm is proposed for the effective energy aware in WSN. The optimal cluster head and route paths are selected by utilizing the proposed optimization algorithm. the developed MCGWO algorithm minimizes the nodes’ energy utilization while increasing the data transmission in WSN. The performance of proposed technique is estimated by utilizing performance measure of throughput, packet delivery ratio, delay and energy consumption. The proposed method attained high packet delivery ratio of 94.4%, 93.7%, 92.1% and 90.9% for 50, 100, 150 and 200 nodes which is comparatively higher than other existing algorithms like Cuckoo Search Algorithm (CSA), Ant Colony Optimization (ACO), Firefly Optimization Algorithm (FOA) and Grey Wolf Optimization (GWO).

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A Novel Cell Density Prediction Design using Optimal Deep Learning with Salp Swarm Algorithm 7

A Novel Cell Density Prediction Design using Optimal Deep Le...

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7th International Conference on Trends in electronics and Informatics, ICOEI 2023

作者： Balde, Govind Hanmantrao Ala Walid, Md. Abul Srinivasan, P. Yella, Venkata Rajesh Soumya, M. Rastogi, Ravi N.T.V.S's G. T. Patil Arts Commerce and Science College Research and P.G. Department of Zoology M.S. Nandurbar425412 India Department of Computer Science and Engineering Khulna9203 Bangladesh Sona College of Technology Department of Ece Tamilnadu Salem India Koneru Lakshmaiah Education Foundation Department of Biotechnology Andhra Pradesh Vaddeswaram India Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal India Nielit Electronics Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350397284

Cell density prediction can be defined as the process of predicting the number of cells in a given quantity of a culture or cell suspension. It is considered a common practice in cell biology since cell density had a significant impact on cell behavior and can be utilized for monitoring the health and growth of cell culture. Precise prediction of cell density was significant for a range of applications in cell biology., which includes bioprocessing, cell-based assays, and cell culture. Therefore, this article develops a novel Cell Density Prediction design using Optimal Deep Learning with Salp Swarm Algorithm (CDP-ODLSSA) technique. The presented CDP-ODLSSA technique predicts the cell densities accurately on the images of cell suspensions or cultures. To do so, the presented CDP-ODLSSA technique employs Long Short Term Memory-Autoencoder (LSTM-AE) model for prediction of cell densities. In addition, the hyperparameter tuning of the LSTM-AE model takes place by the use of Salp Swarm Algorithm (SSA). For experimental validation of the CDP-ODLSSA technique, a wide range of simulations was taken place. The obtained values highlighted the superiority of the CDP-ODLSSA technique compared to other approaches. © 2023 IEEE.

关键词： Long short-term memory

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Deep Distributed Learning and Spatial-Temporal Features for Facial Depression Detection 7

Deep Distributed Learning and Spatial-Temporal Features for ...

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7th International Conference on electronics, Communication and Aerospace Technology, ICECA 2023

作者： Arunachalam, Sripriya Ghate, Shubhangi N. Kopperundevi, N. Balamurugan, M.S. Kanagajothi, D. Srimathi, S. SRM Institute of Science and Technology Computer Science and Application Vadapalani Campus College of Science and Humanities India Ramrao Adik Institute of Technology Electronics and Telecommunications Engg. D. YiPatil University Maharashtra Navi-Mumbai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Panimalar Engineering College Department of ECE India Vel Tech Rangarajan Dr Shangunthala R&D Institute of Science and Technology Department of Mathematics India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350340600

One of the most frequent and debilitating mental illnesses is major depression. Video-based identification of depression using facial expressions has been proposed by a number of deep learning architectures. These structures are frequently modeled for regression with Euclidean loss to foretell the degree of depression. The ordinal correlation between depressive symptoms and exposure to facial depictions is not explored, and the models' resilience to noisy and ambiguous labeling is constrained as a result. Famous deep architectures have shown guarantee in facial depression acknowledgment, but they still lack proper discriminative power because of problems with a lack of labeled depression information for deep transfer learning, large variations in facial expression between people who have identical symptom severity, and subtle variations in facial expression between varying depression severities. This study models the challenge of facial depression identification as one of the learnings to distribute labels, and this study offers a deep combined label distribution and metric learning approach to this problem. Using distribution learning, this research study provides a deep learning architecture for predicting depressive symptoms. The proposed deep learning on spatiotemporal feature improves the process of depression prediction because the two learning modules work together to improve the feature's representation ability and discriminatory strength. The results of empirical evaluation of the proposed method on publicly available datasets prove the system efficacy. © 2023 IEEE.

关键词： Deep learning

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Artificial Driving and Spectral Graph Theory for Automated Plant Leaf Disease Classification

Artificial Driving and Spectral Graph Theory for Automated P...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Chhavi Bajpai Manish Gaur J Roselin Suganthi Vinayagam S S. Kaliappan Manoj Senthil K Department of Computer Science & Engineering Centre for Advanced Studies Lucknow India Department of Computer Science & Engineering Institute of Engineering and Technology Lucknow India Department of ECE K. Ramakrishnan College of Engineering Trichirapalli India Department of Bioscience Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Research and Development Lovely Professional University Jalandhar India Department of Electronics and Communication Engineering Kongu Engineering College Erode India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Having a certified plant pathologist visually inspect the diseased leaves is now the gold standard for disease diagnosis in plants. This is because symptoms are present in the vast majority of plant illnesses. Because manual disease diagnosis is slow and results are highly dependent on the skill of the pathologist, this is an ideal domain for computer-aided diagnostic *** one of the suggested methods are preprocessing, step two is feature extraction, and step three is training the model. Pictures are acquired, then processed, enhanced, resized, segmented, and finally converted from RGB to HSI as part of the preprocessing phase. The topic of this study is the extraction of numerous features for the purpose of leaf disease categorization. Hexagons of colors are retrieved for classification purposes. The model was trained using Spectral Graph Theory. On average, this state-of-the-art technique achieves a higher accuracy rate of 92.48% than CNN and SVM.

关键词： Support vector machines Training Accuracy Computational modeling Crops Color Feature extraction Graph theory Medical diagnosis Diseases

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Analysis of Electroencephalographic Signals to Study the Behavior of Brain Frequencies for the Study of Academic Stress 2

Analysis of Electroencephalographic Signals to Study the Beh...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Moyeenudin, H.M. Hannah, S. Anuradha, T. Muthalagu, R. Seedha Devi, V. Jose Anand, A. Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai600117 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu 6000262 India Department of Electrical and Electronics Engineering KCG College of Technology Tamil Nadu Chennai600097 India Department of Biomedical Engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu 603308 India Department of IT Jaya Engineering College Tamil Nadu Chennai602024 India Department of ECE KCG College of Technology Tamil Nadu Chennai600 097 India

ISBN: (纸本)9798350340235

The current study perform an analysis of electroencephalographic signals to study the behavior of brain frequencies in subjects who are under academic stress generated by a cognitive task, while listening to music or being silent. Creation of a corpus of more than 10 subjects under different sound stimuli is created. Characterization of brain signals are characterized for the identification of academic stress. Protocol is designed and brain signals are collected to observe the relationship between music listening and academic stress. EEG signal classifiers are used to identify differences between different sessions. Analysis of brain frequencies are analysed obtained in the sessions for each participant. © 2023 IEEE.

关键词： Academic Stress Brain Frequencies Brainwave Patterns EEG (Electroencephalography) Stress Response

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