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2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Dulhare, Uma N. Mubeen, Azmath Department of Computer Science and Artificial Intelligence Muffakham Jah College of Engineering and Technology Hyderabad500034 India Deparment of Computer Science and Engineering University College of Engineering Osmania University Hyderabad500007 India

In the past few years there have been many cases in which the patient may get some form of nodule on the body and it becomes very difficult to identify the type of nodule and its relative disease. Sometimes the nodule may be a simple skin lesion or it may be cancerous cyst or it may be due to a severe condition of rheumatoid arthritis. This research work concentrates on classifying the nodules from a given data set of images and detecting the Rheumatoid Nodule (RN) and its risks. Many studies have been conducted for detection of different nodules but specific to rheumatoid nodule identification and its risks on other organs are minimum as per my cognizance. In imaging study, Deep Convolutional Neural Networks (DCNN) have shown efficient accomplishment, hence the model proposed uses dual three-dimensional Deep Convolutional Networks design for the detection of RN and an improved Res Net for classification, respectively. Rheumatoid Nodule detection was performed through Pure Deep Convolutional Networks (PDCNN) and then feature selection was done using Particle Swarm Optimization (PSO) and later classification was done using the Residual Network (Res-Net) on proficiently learned features from Particle Swarm Optimization. The Deep Learning (DL) model for Rheumatoid Nodule detection and classification, blended with medical aspects, aids in minimizing the errors and False Positive (FP) rate and help in early detection of rheumatoid nodule. The recommended system was assessed on dataset collected from different orthopaedic hospitals in the city and improved results were obtained in comparison to the prevailing approaches. © 2022 Elsevier B.V.. All rights reserved.

关键词： Classification (of information)

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A Novel Electronic Wheel Chair Design using Artificial Intelligence Assisted Smart Sensors and Controller 5

A Novel Electronic Wheel Chair Design using Artificial Intel...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Kumar, Abhay Singh, Manmohan Singh, Megha Ramprabu, J. Dubey, Alok Mujoo, Sheetal Ies College of Technology Department of Computer Science and Engineering Bhopal India Oriental University Department of Computer Science and Engineering Indore India Kumaraguru College of Technology Deparment of Eee Coimbatore641049 India College of Dentistry Jazan University Department of Preventive Dental Sciences Jazan Saudi Arabia College of Dentistry Jazan University Denartment of Maxillofacial Surgery and Diagnostic Sciences Jazan Saudi Arabia

ISBN: (纸本)9798350385649

This research work introduces a design paradigm for an electronic wheelchair, incorporating Artificial Intelligence (AI) assisted smart sensors and controllers to enhance overall functionality and user experience. Core components include advanced sensors for environmental and health data, an intelligent controller driven by AI algorithms, and motorized wheels for achieving seamless mobility. The smart sensors, spanning temperature, ECG, oxygen, and heart rate sensors, contribute to real-time health monitoring and environmental awareness. The AI-assisted controller optimizes wheelchair navigation, providing a responsive and adaptive system. Comparative performance metrics reveal the proposed design's superiority over traditional control methods, demonstrating heightened precision, sensitivity, specificity, and an impressive overall accuracy. User feedback corroborates the success of this innovative design, attaining high ratings for ease of use, health monitoring capabilities, overall comfort, and higher accuracy. This work outlines a transformative approach to electronic wheelchair design, emphasizing cutting-edge technologies to address user needs, enhancing performance standards, and redefine the landscape of mobility assistance and proposed DNN model achieved an accuracy of 0.97. © 2024 IEEE.

关键词： Adaptive control systems

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MPCPA: Multi-Center Privacy Computing with Predictions Aggregation based on Denoising Diffusion Probabilistic Model

arXiv

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arXiv 2024年

作者： Luo, Guibo Zhang, Hanwen Wang, Xiuling Chen, Mingzhi Zhu, Yuesheng School of Eletronic and Computer Engineering Peking University Shenzhen China Deparment of Computer Science Stevens Institute of Technology NJ United States

Privacy-preserving computing is crucial for multi-center machine learning in many applications such as healthcare and finance. In this paper a Multi-center Privacy Computing framework with Predictions Aggregation (MPCPA) based on denoising diffusion probabilistic model (DDPM) is proposed, in which conditional diffusion model training, DDPM data generation, a classifier, and strategy of prediction aggregation are included. Compared to federated learning, this framework necessitates fewer communications and leverages high-quality generated data to support robust privacy computing. Experimental validation across multiple datasets demonstrates that the proposed framework outperforms classic federated learning and approaches the performance of centralized learning with original data. Moreover, our approach demonstrates robust security, effectively addressing challenges such as image memorization and membership inference attacks. Our experiments underscore the efficacy of the proposed framework in the realm of privacy computing, with the code set to be released soon. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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MuLan: Multimodal-LLM Agent for Progressive and Interactive Multi-Object Diffusion

arXiv

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arXiv 2024年

作者： Li, Sen Hsieh, Cho-Jui Wang, Ruochen Cheng, Minhao Zhou, Tianyi Department of Computer Science & Engineering HKUST Hong Kong Department of Computer Science UCLA United States College of Information Science & Technology PSU United States Deparment of Computer Science UMD United States

Existing text-to-image models still struggle to generate images of multiple objects, especially in handling their spatial positions, relative sizes, overlapping, and attribute bindings. To efficiently address these challenges, we develop a training-free Multimodal-LLM agent (MuLan), as a human painter, that can progressively generate multi-object with intricate planning and feedback control. MuLan harnesses a large language model (LLM) to decompose a prompt to a sequence of sub-tasks, each generating only one object by stable diffusion, conditioned on previously generated objects. Unlike existing LLM-grounded methods, MuLan only produces a high-level plan at the beginning while the exact size and location of each object are determined upon each sub-task by an LLM and attention guidance. Moreover, MuLan adopts a vision-language model (VLM) to provide feedback to the image generated in each sub-task and control the diffusion model to re-generate the image if it violates the original prompt. Hence, each model in every step of MuLan only needs to address an easy sub-task it is specialized for. The multi-step process also allows human users to monitor the generation process and make preferred changes at any intermediate step via text prompts, thereby improving the human-AI collaboration experience. We collect 200 prompts containing multi-objects with spatial relationships and attribute bindings from different benchmarks to evaluate MuLan. The results demonstrate the superiority of MuLan in generating multiple objects over baselines and its creativity when collaborating with human users. © 2024, CC BY.

关键词： Diffusion

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Street-crimes modelled arms recognition technique (SMART): Using VGG 2

Street-crimes modelled arms recognition technique (SMART): U...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Ali Shah, Syed Atif Ahmad Al-Khasawneh, Mahmoud Uddin, M. Irfan Al-Madinah International University Deparment of Computer Science Kuala Lumpur Malaysia Northern University Nowshehra Pakistan Kohat University of Science and Technology Deparment of Computer Science Kohat Pakistan

ISBN: (纸本)9781665432221

Open access of arms(weapons) is one of the very common threat to the peace. Criminal activities especially violence directly hits peace and economic status of the society which results in stress and downfall;one of the major reason for all this anarchy is the open access to arms. This disordered situation can be handled using automatic arms detection. CNN along with its variant models demonstrated to be the best among all other machineries. In this paper VGG is compared with LeNet and AlexNet and a system is proposed for arms detection from video surveillance and named it SMART (Street-crimes Modelled Arms Recognition Technique). Results shown the efficacy of the proposed model (SMART) as compared to other models. © 2021 IEEE.

关键词： Crime

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Novel characterization and tuning methods for integrating processes

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International Journal of Information technology (Singapore) 2024年第3期16卷 1387-1395页

作者： Jorge, Cárdenas-Cabrera Jorge, Henríquez-Novoa Wendell, Martínez-Rodríguez Edalatpanah, S.A. Shariq, Aziz Butt Naz, Sumera Javier, Jimènez-Cabas Gabriel, Piñeres-Espitia Department of Computer Science and Electronics Universidad de La Costa Calle 58 55-66 Atlántico Barranquilla 080002 Colombia Department of Applied Mathematics Ayandegan Institute of Higher Education Ferdowsi St RVF6+JJ9 Mazandaran Tonekabon *** Iran Deparment of Computer Science University of South Asia 47-Tufail Road Punjab Lahore 110021 Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

The manuscript presents new methodologies for process characterization and control tuning for integrated systems. Integrative systems are widespread in the industrial environment. Some control systems for level, pressure, concentration, and temperature are examples. Although many papers have been published on the subject, no standard has been established, so this remains an open problem. The proposed characterization method consists of deriving the response of the integrating system and applying characterization techniques for self-regulated processes. The proposed tuning method is based on deducing tuning equations for a PD control from the λ tuning rules. The performance of the proposed methods is validated through simulation and experimental tests. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Integrating process Process characterization/identification λ tuning

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POE: Process of Elimination for Multiple Choice Reasoning

arXiv

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arXiv 2023年

作者： Ma, Chenkai Du, Xinya School of Computer Science and Engineering University of Electronic Science and Technology of China China Deparment of Computer Science University of Texas Dallas United States

Language models (LMs) are capable of conducting in-context learning for multiple choice reasoning tasks, but the options in these tasks are treated equally. As humans often first eliminate wrong options before picking the final correct answer, we argue a similar two-step strategy can make LMs better at these tasks. To this end, we present the Process of Elimination (POE), a two-step scoring method. In the first step, POE scores each option, and eliminates seemingly wrong options. In the second step, POE masks these wrong options, and makes the final prediction from the remaining options. Zero-shot experiments on 8 reasoning tasks illustrate the effectiveness of POE, and a following analysis finds our method to be especially performant on logical reasoning tasks. We further analyze the effect of masks, and show that POE applies to few-shot settings and large language models (LLMs) like ChatGPT. © 2023, CC BY.

关键词： Zero-shot learning

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Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer Learning

arXiv

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arXiv 2023年

作者： Kunwar, Suman Ferdush, Jannatul Faculty of Computer Science Selinus University of Sciences and Literature Ragusa Italy Deparment of Computer Science and Engineering Jashore University of Science and Technology Jashore Bangladesh

As the global population continues to expand, the demand for natural resources increases. Unfortunately, human activities account for 23% of greenhouse gas emissions. On a positive note, remote sensing technologies have emerged as a valuable tool in managing our environment. These technologies allow us to monitor land use, plan urban areas, and drive advancements in areas such as agriculture, climate change mitigation, disaster recovery, and environmental monitoring. Recent advances in AI, computer vision, and earth observation data have enabled unprecedented accuracy in land use mapping. By using transfer learning and fine-tuning with RGB bands, we achieved an impressive 99.19% accuracy in land use analysis. Such findings can be used to inform conservation and urban planning policies. © 2023, CC BY.

关键词： Remote sensing

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Recognition of Secure Data Transmission in Cloud Platform using Deep Learning

Recognition of Secure Data Transmission in Cloud Platform us...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： R Bhavya M. Guru Vimal Kumar U.M. Ramya R Janagi M. Ganesan S. Deepa Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. SangunthalaR&D Institute of Science and Technology Chennai India Deparment of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode India Deparment of Information Technology Hindusthan College of Engineering and Technology Coimbatore India Department of Computer Technology-PG Kongu Engineering College Perundurai Erode India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The recognition of secure data transmission in a cloud platform requires the integrity of sensitive information with higher confidentiality. This is obtained using the aid of deep learning techniques. The robustness of the system is improved using a random forest algorithm. In secure data transmission, the deep learning algorithms are trained to adopt various patterns of communication with the identification of certain anomalies. They result in security threats. They involve suspicious user behaviour with unexpected information exchange. The overall accuracy and interpretability of the system are achieved using a random forest algorithm which combines multiple decision trees. They are used for handling the high-dimensional data and obtaining the relationship between complex patterns. They helps in the identification of various security threats in the data communication through cloud platforms. This helps in the automatic decision-making process during malicious behaviour. The obtained dataset contains both the secure and insecure datasets. The extracted features acts as an input for the random forest classifier. These features are analyzed by the random forest and provides information that helps to influence the decision-making process. The integrated approach improves the accuracy of the security threats with enhancing transparency in the automatic decision-making techniques. They provide trust in the security measures in the cloud platforms. The collaboration of deep learning with a random forest algorithm helps in obtaining secure data transmission.

关键词： Deep learning Accuracy Decision making Organizations Feature extraction Robustness Safety

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Enhanced 360 Degree Cognitive Awareness for Heavy Vehicle Drivers Through Haptic Feedback

Enhanced 360 Degree Cognitive Awareness for Heavy Vehicle Dr...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Shah Fahim Chowdhury Abid Mahamud Rabbi Mushfiq Qayyum Saif Nafisa Anjum Khan Khandaker Tabin Hasan Deparment of Computer Science and Engineering American International University Bangladesh (AIUB) Dhaka Bangladesh Faculty of Science and Technology American International University Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

This research explores the use of haptic technology in heavy vehicle driving safety, focusing on its potential to enhance driver assistance systems and improve driving experience. The study addresses driver-induced causes of traffic accidents, including drivers' inability to see around the vehicle due to its length and height. The proposed system provides 360-degree cognitive awareness through haptic feedback, which can improve road safety and mitigate challenges in diverse driving conditions. By using haptic feedback, the driver will be able to get a tangible sense of the presence of objects surrounding the vehicle. Through a comprehensive review of existing literature and experimental studies, the research demonstrates the potential of haptic feedback in revolutionizing the driving experience. The findings highlight the optimum mapping between sensors and vibration motors positions on the human body

关键词： Vibrations Signal processing Sensor systems Haptic interfaces Safety Sensors Time factors Robots Vehicles Testing

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