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A Proposed Framework for Smart Geo-Fencing for Religious Places 7

A Proposed Framework for Smart Geo-Fencing for Religious Pla...

引用

7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Vishnoi, Ankit Choudhury, Tanupriya Patni, Jagdish Chandra Sar, Ayan Mahdi, Hussain Falih Maharashtra Pune412115 India CSE Dept. Lavale Campus Maharashtra Pune412115 India Pune India School of Computer Science Uttarakhand Dehradun248007 India University of Diyala Dept. of Computer and Software Engineering Baquba Iraq

ISBN: (纸本)9798350383065

The innovative system known as 'Smart Geo-fencing for Religious Places' integrates Blockchain and Zero Power Device technology to enhance the reverence of sacred sites. By amalgamating the robustness of blockchain with geofencing capabilities and electronic apparatuses, this solution delineates invisible perimeters encircling religious structures like mosques, churches, and temples. As worshippers enter these digitally marked zones with their smartphones, the setup is capable of monitoring select operations such as making calls, sending texts, or receiving app alerts to foster a serene atmosphere. Furthermore, leveraging blockchain's prowess guarantees that all recorded data remains secure and unalterable. The incorporation of devices that consume minimal energy is crucial for ensuring sustainability. This sophisticated fusion promotes tranquility within religious communities by adeptly marrying contemporary technological advancements with age-old spiritual practices. © 2023 IEEE.

关键词： Blockchain

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Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays

Explainable AI-based Detection and Interpretation of Abnorma...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Sai Smrithi Muthukumar Bhavika Gandham Trisha Vijayekkumaran Jyotsna C Aiswariya Milan K Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes.

关键词： Measurement Adaptation models Visualization Accuracy Explainable AI X-rays Reliability X-ray imaging Medical diagnostic imaging Image classification

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Fingerprint Recognition System Based on XGBoost Matching Algorithm for Forensic Analysis 2

Fingerprint Recognition System Based on XGBoost Matching Alg...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Adnan, Myasar Mundher Priyanka, K. Lande, Ayapal Swamy, Illa Mahesh Kumar Revathi, R. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India School of Computer Science & Artificial Intelligence Sr University Warangal India Anurag University Department of Information Technology Hyderabad India Karpagam Academy of Higher Education Department of Computer Science Echanari Coimbatore India

ISBN: (纸本)9798350373110

In modern era, fingerprint authentication systems are used for easy and low-cost establishment of security which are mostly computationally complex and inaccurate. Therefore, this research utilizes highly efficient technique Oriented FAST and Rotated BRIEF with eXtreme Gradient Boosting (ORB-XGBoost) algorithm for fingerprint recognition. The input data is segmented to give clear area of impression, Gabor filter is employed to provide rotation, scale-invariance by retaining necessary information then the data is binarized for easy processing. The features are extracted by Oriented FAST and Rotated BRIEF (ORB) method which utilizes Features from Accelerated and Segments Test (FAST) and Binary Robust Independent Elementary Feature (BRIEF) techniques. The FAST algorithm is used for key point extraction to determine edges of an image and BRIEF algorithm converts the extracted key points into binary feature vector for faster computation. The feature vectors are given as input to XGBoost algorithm for feature matching which is highly efficient in classification with majority voting phenomena and avoids overfitting problem with regularization concept. The performance of proposed ORB-XGBoost method is evaluated by FVC 2002 DB1 dataset which achieved 96.32% accuracy. The proposed method attained accurate results when compared against Support Vector Machine-Genetic Algorithm (SVM-GA), Random Sample Consensus (RANSAC). © 2024 IEEE.

关键词： Gabor filters

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An Advanced Smart Biotechnological Algal Plate Air Purification System for Use in Conjunction with Real-Time Air Quality Monitoring

An Advanced Smart Biotechnological Algal Plate Air Purificat...

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International Conference on computer and Communications Technologies (ICCCT)

作者： Bharathy S Srivatsaav Murali Jaswant Singh dept. Computer Science Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Air pollution has become a health and environmental hazard as its effects are worsened by the increase inor pollution levels. Conventional air purification methods, although effective, are not environmentally friendly and can be expensive. We propose an Advanced Smart Biotechnological Algal Plate Air Purification System that combines the real-time monitoring of air quality with biological purification processes. An ATmega328 microcontroller, which synchronizes different parts of the system to function well together, powers the entire system. The gas sensor senses air contaminants and draws polluted air with a fan to the algal bioreactor chamber, onto which microalgae consume carbon dioxide and contaminants and output clean air. The biological system is controlled by a microcontroller where a DC fan is used to switch the flow of air in the system allowing the pollutants to be optimized to expose more and expose it to algae purification. It also handles the control of GPIO-based sensors and actuators to allow air quality metrics tracking and adaptive management. It contains an adjustable alarm and relay for turning on the fan in response to dangerous levels of pollution as well an LCD for easy reading of system status. The GPIO interface provides the ease of connecting other sensors and actuators making scalability possible for various applications. This simple and low-cost environmental friendly approach combines microcontroller-assisted technology with biological cleaning to give environmentally friendly air to urban, industrial and household consumers as the cost-effective alternative to existing conventional air purifiers.

关键词： Air cleaners Technological innovation Fans Purification Microcontrollers Algae Air pollution Real-time systems Liquid crystal displays Monitoring

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Smart Plant Watering System for Advanced Irrigation

Smart Plant Watering System for Advanced Irrigation

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International Conference on Advances in Information Technology (ICAIT)

作者： Pasupuleti Sai Pujitha Bhoomika H R Konduri Saideep T. Rahul Reddy Sumit Kumar Banshal Mousumi Karmakar Shekhar R dept. of computer science & engineering Alliance University Bangalore India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The Smart Plant Watering System is an Internet of Things (IoT) solution for automated plant irrigation. It combines an ESP8266 Wi-Fi module for real-time monitoring with soil moisture and DHT11 sensors for data collection. It makes use of tools such as Blynk and Thing Speak to facilitate data visualization and remote control. An online crop recommendation systems using ML approaches and written in Python makes recommendations for the best crops to plant depending on the analysis of sensor data. With its ability to maximize irrigation, reduce water use, and improve crop management, this system represents a major breakthrough in precision agriculture for increased productivity and environmental sustainability.

关键词： Irrigation Soil moisture Pumps Crops Vegetation mapping Sensor systems Internet of Things

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Optimizing Network Traffic Prediction for Network Digital Twins: The Impact of Look-Back Period and Forecast Horizon

Optimizing Network Traffic Prediction for Network Digital Tw...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Sengendo, John Granelli, Fabrizio University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350368369

Over recent years, many advancements in networks have taken place with now the advent of 6G. With these growing advancements, challenges in managing networks also emerge. Network Digital Twins (NDTs) is one of the potential technology in solving many of these challenges because of the capability to virtually replicate the physical network elements. One of the key challenges is predicting network traffic especially with the exponentially growing number of devices in the network. In this paper, we study and show how varying the 'look-back period' and 'forecast horizon' significantly affects traffic predictions. Look-back period or window size is how much of the historical data is used by a prediction algorithm to make predictions. Forecast horizon is how far in the future we can predict. These highly impact how accurately traffic is predicted in networks and as well significantly determine how well Digital Twins (DTs) for networks accurately reflect traffic of the physical network. © 2025 IEEE.

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Real- Time Prediction of Medicinal Herb Plants using Deep Learning

Real- Time Prediction of Medicinal Herb Plants using Deep Le...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： R Sumathy R Jehitha Shalom G Ragul A Nickson Antony Dept of Computer Science and Engineering KIT - KalaignarKarunanidhi Institute of Technology Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Medicinal plants have been important in India's traditional healthcare for centuries, but identifying them can be challenging due to the many different species. This project aims to create a real-time system that recognizes medicinal herb plants using Convolutional Neural Networks (CNN), a type of deep learning that works well for image recognition. The system will be trained on a dataset of herb plant images, using techniques like resizing and data augmentation to improve performance. By doing this, the model will learn to spot even small differences between plant species. We will evaluate the system's effectiveness through accuracy, precision, and recall metrics to ensure reliable identification. By making it easier to identify medicinal plants, this project could help preserve traditional Indian herbal knowledge and encourage the use of these herbs in modern healthcare. In the future, the system will also incorporate real-time prediction capabilities, allowing users to identify herb plants instantly in their natural environments. Additionally, the system could be expanded to include more plant species, contributing to the protection and conservation of medicinal plant biodiversity.

关键词： Medicinal plants Deep learning YOLO Image recognition Medical services Feature extraction Real-time systems User experience Convolutional neural networks Biomedical imaging

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Design of Violence Event Detection System Based on CCTVs by Human Body Pose Recognition

Design of Violence Event Detection System Based on CCTVs by ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chang, Kuei-Chung Liao, Yi-Ching National Yunlin University of Science and Technology International Graduate School of Artificial Intelligence Taiwan National Changhua University of Education Dept. of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Violent incidents have happened regularly and usually cause tragedies due to without rescued immediately. Therefore, we expect to study a violence detection and alarm system based on human body pose recognition of images captured from public CCTVs. The system uses OpenPose for real-time skeleton recognition to detect poses based on the abnormal angles or speeds of movement between the various joints of the limbs to determine whether it is violent behavior or not. If possible violent events are detected, the system will trigger an alarm and notify the police or security guard to deter the attacker. Victims will be able to get assistance as soon as possible through this recognition system even if they have obstacles for calling help by themselves. Experimental results show that the target violence behaviors can be detected successfully. © 2023 IEEE.

关键词： Gesture recognition

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Systematic Analysis of Underwater Image Dehazing and Object Detection

Systematic Analysis of Underwater Image Dehazing and Object ...

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Image Processing (ASIP), Asia Symposium on

作者： Anushka Ghei Dhriti Rajesh Krishnan Gautam Santhosh S. Natarajan Dept. of Computer Science and Engineering PES University Bengaluru India

ISBN: (数字)9798350376159

ISBN: (纸本)9798350376166

Dehazing underwater images is crucial for several underwater applications, including marine biology, exploration, and monitoring. Because of colour distortion, light attenuation, and scattering, underwater imaging presents particular difficulties. This research presents a systematic survey on using transformers and different Generative Adversarial Networks (GANs) for dehazing underwater photos. This survey study looks into transformer-based models, analyzes GAN designs (CycleGAN, Pix2Pix, and UWGAN) in detail, and compares them using a range of metrics, such as Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), Frechet Inception Distance (FID), and F1-score. Further analysis was carried out using CLAHE on the best-performing models. Our findings emphasize the benefits and drawbacks of the different designs that were employed as well as prospective directions for further study and advancement.

关键词： Surveys Photography Analytical models Systematics Measurement uncertainty Scattering Object detection Generative adversarial networks Transformers Monitoring

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Contribution of Algorithm Visualizations to Students' Learning Skills: A Pedagogical Approach

Contribution of Algorithm Visualizations to Students' Learni...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Harem Ali Kheder Asaf Varol Dept. of Computer Engineering College of Engineering and Computer Sciences Lebanese French University Erbil Iraq Dept. of Engineering Management and Technology College of Engineering and Computer Science The University of Tennessee at Chattanooga Chattanooga TN US

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

The Domain Algorithm visualizations are multimedia-based representations of algorithms that help students learn how they operate and behave. Algorithm visualizations provide numerous benefits to instructors and students, including an improved understanding of difficult algorithms in programming. By stripping down the complexities of algorithms and presenting them in simplified visual ways, students can obtain a more thorough knowledge of how they perform. Instructional approaches, such as guided instruction or discovery learning, are critical to the pedagogical effectiveness of algorithm representations. Guided instructions can considerably boost learners' overall performance in problem-solving activities that incorporate visualizations. At the same time, discovery education promotes exploration and experimentation, yet it may result in longer learning times and increased mental demands. A combination of these approaches is required for optimal effectiveness of instruction. In this paper, we investigate how the pedagogical effectiveness of algorithm visualizations should be determined by a variety of elements, including student characteristics, visual and cognitive processing ability, algorithm representation, and instructional context based on a literature review and interviews with field experts, and a bunch of recommendations is provided that has witnessed by authors during their long teaching periods.

关键词： Visualization Education Linguistics computer science education Complexity theory Security Problem-solving

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