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Air Purification Robotics using Cloud and Deep Q Networks for Autonomous systems 1

Air Purification Robotics using Cloud and Deep Q Networks fo...

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1st international conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart systems, ISTEMS 2024

作者： Reddy, Elangovan Guruva Damodhar, T. S. Balaji Yuvaraj, S. Vathani, B. Santha Sivakumar, S. Malathi, N. Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram India Madanapalle Institute of Technology and Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Mahalashmi Women's College of Arts and Science Department of Management Studies Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. Cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues. © 2024 ieee.

关键词： Robotics

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IoT-Driven Precision Farming: Enhancing Crop Yield and Sustainability Through Real-Time data Analytics

IoT-Driven Precision Farming: Enhancing Crop Yield and Susta...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Jaya Krishnan, A.P. Laldinpuia Hema, R. Lotus, Rayappan Nair, Resmi R. Ragini, Y.P. Sona College of Technology Department of Ece Tamil Nadu Salem India Mizoram University Department of Mathematics and Computer Science Mizoram Aizawl India Saveetha Engineering College Department of Ece Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Department of Biosciences Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

Precision farmers are enhancing crop yields and crop life through the implementation of IoT-based agricultural management. This investigation investigates the methods by which precision farmers are employing Internet of Things (IoT) devices for real-time crop management through data collection, analysis, and decision-making. The impact of IoT on soil moisture, nutrient levels, and ambient conditions is investigated in this study using a sensor network, drones, and smart irrigation systems. Yield, resource efficacy, and cost-effectiveness are significantly enhanced in comparison to conventional agricultural methods. The IoT is employed in precision farming to address current agricultural issues in novel ways. It accomplishes this by precisely regulating agricultural inputs and providing data analytics insights. According to the research, IoT technology has the potential to address the increasing global demand for food in a sustainable and environmentally favorable manner. The study scales and interoperate the IoT to advance precision farming. © 2024 ieee.

关键词： Smart agriculture

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An Exoskeleton Robot with More Active Degree of Freedoms and Larger Range of Motion for Hand Rehabilitation 9

An Exoskeleton Robot with More Active Degree of Freedoms and...

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9th ieee international conference on advanced Robotics and Mechatronics (ICARM)

作者： Sun, Xijin Liu, Mengqi Sun, Mingze Sun, Ning Li, Ke Shandong Univ Sch Control Sci & Engn Inst Intelligent Med Engn Jinan 250061 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

ISBN: (纸本)9798350385731;9798350385724

Exoskeleton robots are a useful technique for hand rehabilitation. Most current exoskeleton robots have low active degrees of freedoms (DOFs) and confined range of motion (ROM), which may constrain the hand dexterity and motion area for rehabilitation. This study aimed to develop a novel exoskeleton robot with more active DOFs and larger ROM for hand rehabilitation. The novel exoskeleton robot was designed with 22 DOFs. The mechanical structure of the exoskeleton was optimized by kinematic modeling and optimization problem construction. The ROM was evaluated using extraction of the structure optimized robot motion space envelope. The effect of the novel exoskeleton was examined by an experiment using data of brain tissue oxy-hemoglobin (HbO). This study collected brain tissue HbO concentrations without training and with the assistance of the exoskeleton robot for comparison. Results showed that the ROM of the exoskeleton was improved from 169 cm(3) to 207 cm(3). The mean and maximum concentrations of brain tissue HbO during hand motion driven by the exoskeleton were 0.0429 (mmol/L) *mm and 0.0921 (mmol/L) *mm, respectively. By contrast, the mean and maximum without exoskeleton-assistant motion were -0.0541 (mmol/L) *mm and 0.0138 (mmol/L) *mm, respectively. These results suggested that the brain tissue HbO during exoskeleton-assisted training was notably elevated compared to non-training conditions. This is a preliminary study showing the effectiveness of the novel exoskeleton robot.

关键词： Exoskeleton (Robotics)

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Performance optimization of Learning Trees modules in the ECL repository 3

Performance optimization of Learning Trees modules in the EC...

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3rd international conference on Communication, Control, and intelligent systems, CCIS 2024

作者： Shashank, B. Sudershan, K.S. Shobha, G. Manonmani, S. Dev, Roger Rv College of Engineering Dept. of Computer Science Bangalore India LexisNexis Risk Solutions United States

ISBN: (纸本)9798331528201

Multi-Node computation, also known as distributed computing, is a paradigm that allows for the efficient utilization of multiple interconnected nodes or machines to perform complex computational tasks. By dividing the workload and distributing it across multiple nodes, multi-node computation enables parallel processing, scalability, and improved performance for a wide range of applications. HPCC systems® from LexisNexis Risk Solutions is a proven, open source solution for big data insights that can be implemented by businesses of all sizes. HPCC systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. ECL is the Enterprise Control Language designed specifically for huge data projects using the HPCC systems platform. It is extremely scalable and a powerful declarative data-processing language with native multi-processing capabilities and used for HPCC systems. Some core ECL functions, like the ones handling Learning Tree algorithms of Machine Learning Bundle are recursive in nature and hence high computational time is needed. This paper thus presents a solution to achieve optimized processing of ML learning tree algorithms in ECL through embedding python libraries. The proposed approach was tested on standard datasets and a significant decrease in computation time. For Real Estate Valuation Regression dataset the computation time was reduced from 11.371 seconds to 0.886 seconds using the proposed method showing 1324% improvement with no impact on the accuracy. Random Forests are also implemented using the proposed Decision Tree method allowing the users to specify the number of decision trees to be used in the forest. © 2024 ieee.

关键词： Parallel processing systems

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Modeling of Multi-Core CPU Power Consumption during Online Video conference 11

Modeling of Multi-Core CPU Power Consumption during Online V...

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11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Zhuravska, Iryna Obukhova, Kateryna Petro Mohyla Black Sea Natl Univ Dept Comp Engn 68 Desantnyk10 UA-54003 Mykolaiv Ukraine

ISBN: (纸本)9781665426053

Recently, more and more services - industrial, educational, medical, etc. - are provided remotely, via the Internet. This has become especially relevant in the field of health care, for example, during the rehabilitation period for post-stroke and post-infarction patients. In this case, several video conferencing sessions are opened simultaneously on the doctor's gadget. This leads to overload of the gadget's processor ( CPU), a significant increase in power consumption, overheating of the cores, and as a result to a violation of the stability of the connection. The proposed Colored Petri Net model and developed software for each cores' monitoring the processes in the CPU, built on the *** architecture, allow decision- making on CPU load management with increasing the share of use of LITTLE cores. This approach will increase the time of use of the user device without recharging to 6.8%.

关键词： video conference remote rehabilitation post-stroke post-infarction patients multi-core CPU big.LITTLE architecture power consumption monitoring Colored Petri Net model

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advanced Persistent Threats Detection based on Deep Learning Approach 6

Advanced Persistent Threats Detection based on Deep Learning...

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ieee 6th international conference on Industrial Cyber-Physical systems (ICPS)

作者： Eke, Hope Nkiruka Petrovski, Andrei Robert Gordon Univ Sch Comp Aberdeen Scotland Natl Subsea Ctr Sch Comp Aberdeen Scotland

ISBN: (纸本)9798350311259

advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach networks, often, over multiple network hosts and evades detection. It also uses "low-and-slow" approach over a long period of time. Resource availability, integrity, and confidentiality of the operational cyber-physical systems (CPS) state and control is highly impacted by the safety and security measures in place. A framework multi-stage detection approach termed "APT(DASAC)" to detect different tactics, techniques, and procedures (TTPs) used during various APT steps is proposed. Implementation was carried out in three stages: (i) data input and probing layer - this involves data gathering and pre-processing, (ii) data analysis layer;applies the core process of "APT(DASAC)" to learn the behaviour of attack steps from the sequence data, correlate and link the related output and, (iii) Decision layer;the ensemble probability approach is utilized to integrate the output and make attack prediction. The framework was validated with three different datasets and three case studies. The proposed approach achieved a significant attacks detection capability of 86.36% with loss as 0.32%, demonstrating that attack detection techniques applied that performed well in one domain may not yield the same good result in another domain. This suggests that robustness and resilience of operational systems state to withstand attack and maintain system performance are regulated by the safety and security measures in place, which is specific to the system in question.

关键词： advanced Persistent Threats Cyber-Physical systems Critical Infrastructures Deep Learning Industrial Control systems Supervisory Control and data acquisition

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Holistic Knowledge Framework for Improving PC Design using Big data Analytics 7

Holistic Knowledge Framework for Improving PC Design using B...

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7th international conference on intelligent computing and Control systems, ICICCS 2023

作者： Rajendran, T. Vignesh, O. Priyadharsini, V. Jagadeesh, K. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Easwari Engineering College Department of Electronics and Communication Engineering Chennai India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India GRT Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350397253

Cyberspace is massively expanding every day, and the users of these digital devices are looking for more innovative applications to ease their day-to-day work. The main objective of any device is to use available system resources at a maximum optimum level and give high performance to the users. Especially in personal computers (PC), there is still a massive gap in the user experience and utilization of available system resources. There are several situations where error conditions and system responsiveness lag. Over several years the digital world evolved and provided high-performance computing. Furthermore, the PC design enhancements could be carryout through the participation of PC users. That could be accomplished by collecting the system performance indicators, system behavior and properties, software application usage, user working log, and other wide range of real-time data from the PC. These telemetry data needed to be retrieved from an extensive array of PCs then these datasets would be reposited on the centralized servers. By applying the big data analytical methods and other mining algorithms over these extensive datasets to identify the hidden patterns and discover more knowledge about the key indicators that would help improve PC performance and user experiences. Through this knowledge acquisition model, the organizations and research institutions could develop the most resilient and adoptable PC designs and software applications developments. © 2023 ieee.

关键词： Personal computers

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Design of Message and File Delivery System Based on C plus plus and QtToolkit 4

Design of Message and File Delivery System Based on C plus p...

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2023 4th international conference on Computer Engineering and intelligent Control, ICCEIC 2023

作者： Huang, Min School of Humanities Shanghai Institute of Technology Fengxian District Shanghai China

ISBN: (纸本)9798350308877

Messaging and document delivery systems are tools that can reduce the burden of record-keeping on staff, across disciplines and job content. Even though they are widely used, messaging and document delivery systems need to be constantly developed and improved. This is due to the differences in the needs of different industries and organizations, and the growing needs of emerging industries and fields. In addition, constant advances in technology and user feedback drive developers to continually improve existing software to help users in ways that are more responsive to their work and more efficient. With messaging and file transfer systems, users can improve data collection efficiency, reduce the occurrence of errors, and ensure data accuracy and consistency. The multi-threaded design allows the software to handle multiple tasks simultaneously, increasing the speed of data collection and report generation. Meanwhile, through socket network programming, the software can exchange data and communicate with other systems or devices to realize real-time data updating and sharing. In summary, the message and file delivery system developed in this paper provides an effective way of implementation, and it is hoped that this approach can provide some inspiration for others as a viable form. © 2023 ieee.

关键词： data acquisition

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intelligent Identification and Analysis Method for Potential Faults of Spacecraft in Space Environment 10

Intelligent Identification and Analysis Method for Potential...

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10th international conference on Big data computing and Communications, BIGCOM 2024

作者： Chong, Jingyi Zhou, Haocheng He, Wei Institute of Telecommunication and Navigation Satellite China Academy of Space Technology Beijing China Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

ISBN: (纸本)9798331509538

In the complex space environment, targeting the characteristics of high integration and complexity in the new generation of spacecraft systems, and driven by the design needs of long lifespan and high reliability of spacecraft platforms, this paper proposes a system-level potential failure identification and analysis method based on the System-Theoretic Accident Model and Processes (STAMP). During the overall design process, this method initiates the subsystem reliability design requirements under space conditions. By constructing a system control logic architecture, the method analyzes potential unsafe control actions and determines the failure scenarios by inspecting each element in the control and feedback loops, proposing constraints on control processes to eliminate or mitigate the likelihood of failures, forming targeted subsystem reliability design requirements. © 2024 ieee.

关键词： Space platforms

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Enhancing Customer Personality Prediction using advanced Machine Learning Techniques and data Balancing Strategies: A Comprehensive Approach to Addressing Imbalanced datasets in Marketing Analytics 1

Enhancing Customer Personality Prediction using Advanced Mac...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Selvalakshmi, V. Sree, T. Manju Usha Saranya, S. Devi, A. Uma Basha, Md Shaik Amzad Velammal College of Engineering and Technology Department of Master of Business Administration Madurai India Department of Master of Business Administration Hyderabad India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Alpha College of Engineering Department of Management Studies Chennai India Hyderabad India

ISBN: (纸本)9798331529246

This study tackles imbalanced customer response prediction by integrating advanced resampling methods with diverse machine learning and ensemble techniques. Novel resampling approaches (SMOTE, Borderline-SMOTE, ADASYN, and SMOTEENN) were systematically applied alongside Logistic Regression, Random Forest, Gradient Boosting, LightGBM, SVM, and Neural Networks. Stacking and voting ensembles further consolidated individual model strengths. Results show that coupling resampling with ensembles significantly boosts minority class metrics. For example, ADASYN combined with LightGBM achieved a 95% accuracy and substantially improved minority class recall and precision, while SMOTEENN with a voting ensemble reached a 99% recall, albeit with a slight precision trade-off. These findings underscore that employing sophisticated data rebalancing techniques in conjunction with hybrid ensembles can surpass traditional models, offering a balanced improvement in metrics such as accuracy, precision, recall, and F1 score. The practical significance lies in enabling more reliable predictions in highly imbalanced scenarios, ultimately enhancing decision-making processes. © 2025 ieee.

关键词： Adversarial machine learning

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