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ForeScanGuard: Proactive Monitoring and Detection for Sustainable Forest Conservation

ForeScanGuard: Proactive Monitoring and Detection for Sustai...

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International Conference on Inventive Computation Technologies (ICICT)

作者： R. Santhana Krishnan S. Balamurugan M. Rekha R Arul Jose K. Haribabu A. Essaki Muthu Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Mathematics Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering P.S.R.R College of Engineering Sivakasi India Department of Electrical and Electronics Engineering RajaRajeswari College of Engineering Bangalore India Department of Mechanical Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In response to the increasing environmental threats, ForeScanGuard emerges as a vital forest monitoring system. Integrating advanced sensors and a proactive anomaly detection algorithm, it employs a 5-channel fire sensor for early fire detection, ultrasonic and vibration sensors for identifying tree cutting activities, and a BMP120 sensor to confirm tree falls. GPS modules attached to trees allow real-time location tracking, transmitted via Wi-Fi to a cloud server. The ForeScanGuard Algorithm analyzes sensor data for anomalies like fire outbreaks, illegal logging, and tree falls. Its proactive alerts facilitate rapid responses from forest officials, ensuring effective forest management. ForeScanGuard, with multiple sensors and cloud-based analytics, represents a significant advancement in smart forest monitoring, promoting conservation efforts through real-time anomaly detection and informed decision-making. This comprehensive solution enhances ecosystem protection, fostering a sustainable and proactive approach to forest conservation.

关键词： Vibrations Cloud computing Forestry Real-time systems Environmental management Random forests Monitoring

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Optimization and Exploration of the Volume Scheduling Problem in Logistics Network

Optimization and Exploration of the Volume Scheduling Proble...

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Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： Junyou Luo Xinyue Gong Hao Tan Chunguang Chen Yu Kang Taoran Fu School of Computational Science and Electronics Hunan Institute of Engineering Xiangtan China School of Computer and Communication Hunan Institute of Engineering Xiangtan China

Compared to traditional business activities, e-commerce activities are more susceptible to the impact of logistics networks and various emergency situations. Therefore, in the event of a special situation, e-commerce operators urgently need an optimized emergency response plan. This article aims to establish a logistics network scheduling model to help operators cope with emergencies and improve operational efficiency in emergency situations. The article establishes a path planning model that considers how the logistics network can quickly plan when a warehouse or road suddenly closes, which can meet the logistics transportation requirements and minimize the number of logistics adjustments required. The article focuses on the classification integration and visualization of logistics network data, evaluates the importance of each logistics location and road, determines the workload and efficiency, dynamically adjusts the logistics sites, and discusses the impact of logistics site closures and the feasibility of this research method. The planning model developed in this article has strong reference value for the mobilization plan of real logistics.

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Bridging Theory and Practice: Innovative Approaches in Contemporary engineering Education

Bridging Theory and Practice: Innovative Approaches in Conte...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Santosh Kumar S Bharathi S H Venkata Siva Reddy R Manuel Castro Deptartment of CSE (Data Science) Sri Venkateshwara College of Engineering Bengaluru India School of Electronics and Communication Engineering REVA University Bengaluru India Electrical and Computer Engineering Department UNED Madrid Spain

ISBN: (数字)9798350376234

ISBN: (纸本)9798350376241

This study investigates innovative approaches in Analog electronics education aimed at bridging the gap between theoretical knowledge and practical application in engineering. By analyzing case studies from University A and University B, the research highlights successful implementations of virtual reality (VR) integration, cooperative education programs, project-based learning, and adaptive learning platforms. For example, VR integration at University A led to a 30% increase in design accuracy and a 40% rise in student satisfaction. Similarly, University B’s cooperative education program achieved a remarkable 95% employer satisfaction rate, underscoring its effectiveness in preparing students for the workforce. These methods address critical challenges such as resource constraints and faculty training needs, providing valuable insights for curriculum development and policy-making. The study emphasizes the importance of leveraging technological advancements to offer students real-world experience and enhance their problem-solving skills. By showcasing these successful models, the research provides practical guidance for educators aiming to improve student competence and readiness for professional engineering careers. This work contributes to the ongoing discourse in engineering education, offering a framework that other institutions can adopt to integrate theory and practice more effectively. Ultimately, it aims to enhance educational outcomes and career readiness for engineering graduates, ensuring they are well-equipped to meet the demands of the modern workforce. This study serves as a comprehensive guide for improving engineering education.

关键词： Training Industries Adaptive learning Solid modeling Electronic learning Accuracy engineering profession Virtual reality Problem-solving engineering education

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Deep Reinforcement Learning for Resource Allocation in Wireless communication Systems

Deep Reinforcement Learning for Resource Allocation in Wirel...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Chettiyar Vani Vivekanand Gokul Talele B. Jaishanthi Harihara Gopalan S K. Maheswari B. Uma Maheswari Department of Electronics and Communication Engineering RMK College of Engineering and Technology Thiruvallur Tamil Nadu India Department of Data Science IIIT Banglore India Department of Computer Science and Engineering Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana Department of Computer Science and Engineering St. Joseph's College of Engineering OMR Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In wireless communication systems, deep reinforcement learning (DRL) methods are used to find the best way to use resources so that the system works at its best. When it comes to cellular networks, the old ways of doing things aren't working well enough to meet the growing need for efficient use of limited resources. The reason for this is that demand is rising very quickly. With this , the authors hope to come up with a new way to use DRL's features to distribute resources in real-time based on how the network is performing. That's what thisis all about. Also, deep neural networks are built into the system. This lets it learn how to adapt and improve strategies for allocating resources, which eventually makes the system work better overall. For the purpose of finding out if DRL is both possible and useful in handling the difficulties that come with wireless contact, this is being done. Changing traffic loads, the state of the route, and the needs of the users are just a few of the other things that are taken into account.

关键词： Cellular networks Wireless networks Telecommunication traffic Artificial neural networks Deep reinforcement learning Real-time systems Mathematics

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Chromosome Image Enhancement for Efficient Karyotyping

Chromosome Image Enhancement for Efficient Karyotyping

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2022 International Conference on Innovative Trends in Information Technology, ICITIIT 2022

作者： Remya, R.S. Prasad, Hari Hariharan, S. Gopakumar, C. Department of Computer Science and Engineering Kerala India TKM College of Engineering Department of Centre for Artificial Intelligence Kerala Kollam India Regional Cancer Centre Department of Cancer Research Kerala Thiruvananthapuram India Department of Electronics and Communication Engineering Kerala India

ISBN: (纸本)9781665406703

Chromosome images are susceptible to sensor and staining noises, inhomogeneity, and blurring which prevent efficient karyotyping. In this research work, image processing methods are systematically extended for the preprocessing of chromosome images, and a novel approach for denoising and enhancing the chromosome images is proposed. The proposed approach is mathematically modeled and evaluated with subjective and objective measures. Promising results are obtained which are further substantiated with the post-classification of the segmented chromosomes from the preprocessed input image. Performance of the proposed method is quantified in terms of MSE (Mean Squared Error), PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity Index Measure), FSIM(Features Similarity Index Measure), SAM(Spectral Angle Mapper), and SRE(Signal to Reconstruction Error ratio). An MSE of 8.164, PSNR of 39.037, SSIM of 0.9654, SAM of 81.729, SRE of 63.842, and FSIM of 0.6128 are obtained, on average for a set of 10 test images which were previously degraded with Gaussian noise and Gaussian blur. Post-classification accuracy improved from 88% to 95% as and when the proposed preprocessing is followed by the classification task. © 2022 IEEE.

关键词： Image enhancement

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IoT ML Driven Holistic Health Monitoring and Fitness Assessment Empowering Proactive Wellbeing Management

IoT ML Driven Holistic Health Monitoring and Fitness Assessm...

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Students Conference on engineering and Systems (SCES)

作者： Sachin Mishra Anurag Singh Dept. of Computer Science and Engineering Government Engineering College Raipur Raipur Chhattisgarh India Dept. of Electronics & Communication Engineering International Institute of Information Technology Chhattisgarh Naya Raipur India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374728

In recent years, significant advancements in the area of non-invasive and wearable technology have paved the way for widespread applications of personalized and remote health monitoring. Health monitoring enables proactive management of one's well-being, and enhances overall health outcomes. With the advent of such technologies there are lots of platforms developed. But such platforms are not successful in providing the followings: 1) the ability to monitor all major health vitals in one unified application, & 2) the provision of a holistic health score derived from these non-invasive vitals for fitness measurement. In response, this paper presents a novel solution featuring: 1) An IoT-ML enabled platform for self-monitoring of important health vitals, and 2) A fitness assessment score derived through ML algorithm using the health vitals of a person. By integrating data on various health parameters including heart rate, blood oxygen levels, and sleep patterns, advanced machine learning models are developed to analyze this data and predict the fitness score that accurately reflects an individual's overall health and fitness level. The performance of the proposed estimation model is evaluated using R2 and mean square error (MSE) and the best value is achieved using the XGBoost regressor of values 0.967 and 0.0033. This solution empowers individuals to take control of their well-being and improve their overall quality of life. Overall, the integration of non-invasive methods and IoT sensors for health vitals monitoring and fitness assessment presents a novel approach with significant potential to revolutionize health care. By empowering individuals with real-time insights and a comprehensive fitness score, this solution facilitates informed decision-making and enables individuals to proactively manage their health and well-being, ultimately enhancing their overall quality of life.

关键词： Measurement Pediatrics Medical services Mean square error methods Predictive models Prediction algorithms Real-time systems

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Empirical Assessment of Artificial Intelligence Enabled Electronic Voting System Using Face Biometric Verification Strategy

Empirical Assessment of Artificial Intelligence Enabled Elec...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： M. Tamilselvi B. Manimaran Sanasam Chanu Inunganbi Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai Tamil Nadu Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu Department of Computer Science and Engineering Koneru Lakshamiah Education Foundation Vijayawada India

Each and every citizen has a primary concern to vote for the nation, in which it is the basic need to all countries in the globe. But the fact is, every election around 20% votes are not polled from the voter side. This is due to the conditions of rural village problems, lack of time, health issues and so on. So that the need for an electronic voting system is formed, but the major issue in the system is security. All the voters have a question in their mind is the concern of privacy and security issues. This paper is intended to design a novel face based biometric methodology to authenticate to place their valuable vote in the correct manner. The goal of this methodology is to create an electronic voting mechanism that will make elections more reliable and convenient. Voters prefer to submit their ballots in specific locations for voting, such as polling booths and are willing to wait in a long line to do so. Fake votes cast by ineligible voters might generate just as many issues. For that reason, this work proposes an electronic voting mechanism that has the potential to be quite helpful. The government may be able to conserve the budget normally spent on elections thanks to this technique. This proposed scheme introduced a face recognition approach to identify the respective voter and authenticate them to vote further, only if the respective person is eligible to vote with correct identity. The logic of Artificial Intelligence (AI) is applied to check the process, that is the face matching with the trained samples and the resulting section provides the proper proof to the efficiency of the proposed scheme in detail. As a whole, the system is being created to facilitate the work of the Independent Electoral Commission of India and to lessen the burden on people who work there.

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Large Language Models (LLMs): Hypes and Realities

Large Language Models (LLMs): Hypes and Realities

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computer science and Emerging Technologies (CSET), International Conference on

作者： Sudhir K. Routray Abhishek Javali K P Sharmila Mahesh K. Jha M. Pappa Monika Singh Department of Computer Science and Engineering CMR University Bangalore India Department of Electronics and Communication Engineering CMR Institute of Technology Bangalore India

Artificial intelligence (AI) has created a lot of buzz in recent years. Using machine learning and other AI techniques several intelligent initiatives have been tested. The large language model is one of them. A large language model (LLM) normally refers to a type of AI model that is trained on vast amounts of text data to understand and generate human-like language outputs. These models are designed to capture the statistical patterns and structures present in the training data, enabling them to generate coherent and contextually relevant responses. The widely known ChatGPT is one of the LLMs which can do several tasks and answer many questions. It is trained with a huge number of data sets and a large number of parameters. In addition to ChatGPT, many other LLMs such as the Google Bard, Claude v1, Bison 001, Cohere, Falcon, and Guanaco-65B have surfaced in recent times. In this paper, we study the basic principles and features of LLMs. We go through their brief history, abilities, limitations, challenges and future prospects.

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Optimization of New Radio Physical Uplink Shared Channel Resources Allocation with Efficient Demodulated Referenced Signals and PT-RE Deployment

Optimization of New Radio Physical Uplink Shared Channel Res...

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Electrical electronics and Computing Technologies (ICEECT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Kunj Bihari Sharma Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This paper explores the resource allocation optimisation of New Radio Physical Uplink Shared Channels with an emphasis on the effective implementation of Phase Tracking Reference Signals and Demodulation Reference Signals in 5G networks. Understanding how important these reference signals are to dependable communication, we created a Python framework to model and display their distribution on a time-frequency grid. By carefully modifying important factors including mapping type, PRB allocation, and symbol allocation, our method greatly increased resource efficiency, decreased overhead, and improved signal quality. The results show how sophisticated resource allocation techniques may be used to maximise system performance, providing important information for the creation of more reliable and effective 5G NR communication systems

关键词： Wireless communication Time-frequency analysis System performance Symbols New Radio Resource management Reliability Uplink Optimization Demodulation

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Efficient Real-Time Human Activity Recognition with IoT Sensor Data Using Conditionally Parametrized Convolutions

Efficient Real-Time Human Activity Recognition with IoT Sens...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Mayank Lovanshi Vivek Tiwari Anurag Singh Rajesh Ingle Department of Computer Science Engineering International Institute of Information Technology Naya Raipur India Department of Computer Science Engineering ABV-Indian Institute of Information Technology Gwalior India Department of Electronics Communication Engineering International Institute of Information Technology Naya Raipur India Department of Computer Science Engineering Symbiosis Skills of Professional University Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Human Activity Recognition (HAR) is a trading area in computer vision and deep learning. However, boosting the performance of deep learning models often necessitates increasing their size or capacity, which raises computational demands. The high operational costs present challenges for real-time HAR on sensor-based devices. Shallow learning approaches, while computationally efficient, generally fail to deliver comparable performance. Consequently, there is a critical need for deep learning methods that can effectively balance accuracy and computational cost, a topic that remains largely underexplored. This paper introduces a computationally efficient CNN that utilizes conditionally parametrized convolution, designed specifically for real HAR on sensor-based devices. The model is tested on the OPPORTUNITY dataset, achieving existing accuracy without increasing computational load. Through extensive ablation studies, we show that this high-capacity network surpasses baseline models while maintaining a similar level of computational efficiency. The proposed method can directly replace existing deep HAR frameworks and be easily implemented on sensor-based platforms for real-time applications.

关键词： Performance evaluation Deep learning Accuracy Costs Convolution Computational modeling Real-time systems Computational efficiency Human activity recognition Load modeling

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