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PEGASIS - LEACH: Energy Efficient Protocols with Route Optimised Techniques 2

PEGASIS - LEACH: Energy Efficient Protocols with Route Optim...

引用

2nd International Conference for Advancement in technology, ICONAT 2023

作者： Chauhan, Sandeep Nene, Manisha J. Defence Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

The ad-hoc deployments of Wireless Sensor Network (WSN) in military operations is to gather information in critical areas where human interaction is limited. To improve the capability of WSN model there is a need to improve the power efficiency of the models. Power Efficient Gathering in Sensor Information Systems (PEGASIS) is tree based whereas Low-Energy Adaptive Clustering Hierarchy (LEACH) is clustering-based protocols which are used to improve the overall efficiency of ad-hoc WSN deployment and also increases the operational capability of the WSN *** paper aims to compare the results of PEGASIS and LEACH which is optimized with Particle swarm optimization (PSO) making it LEACH-PSO protocol. LEACH is optimized with PSO as it works on clustering as these techniques helps it to find the correct cluster head (CH) which will make it more efficient and also improves the overall deployment of the WSN architecture. PSO is stochastic optimizer technique based on the intelligence and movement of swarms therefore it assist in finding the correct CH. PEGASIS works on chain-based flow of information therefore it can be clubbed with ACO which will help in increasing the overall efficiency by identifying the correct leader Head (LH). PEGASIS AND LEACH when clubbed with route optimizing techniques such as Ant Colony Optimizer, PSO and Grey Wolf Optimizer has improved and effective WSN deployment. Simulation is done in MATLAB. © 2023 IEEE.

关键词： Wireless sensor networks

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Trespasser Detection and Tracking System for the Borders 7

Trespasser Detection and Tracking System for the Borders

引用

7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Logaprakash, M. Archana, K. Manojkumar, S. Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350340600

Intruder discovery is still remaning as a challenging task in national boundaries. Currently, the static sensors are used to build an obstruction in irregular areas to identify intruders. By considering the recent innovations in sensor technologies, this study proposes an intelligent sensor module for intruder identificatio. The proposed wireless sensor module can improve the boundary inclusion. In particular, the proposed wireless sensor network technology aims to enable boundary inclusion to detect the moving intruders. The proposed improved boundary inclusion strategy has the ability to overcoem the location errors with fluctuating sensor speed. Furthermore, the goal of this research is to broaden the inclusion possibility among all possible sensor based monitoring sessions. When compared to the static sensors, the inclusion rates have improved significantly with a comparable number of sensors. © 2023 IEEE.

关键词： Support vector machines

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XAI: An Explainable AI Model for the Diagnosis of COPD from CXR Images 2

XAI: An Explainable AI Model for the Diagnosis of COPD from ...

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2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Ikechukwu, Agughasi Victor Murali, S. Maharaja Institute of Technology Mysore Department of Computer Science and Engineering Mysuru India

ISBN: (纸本)9798350314168

Chronic Obstructive Pulmonary Disease (COPD) is a major health concern worldwide, the third leading cause of death. However, it often goes unnoticed until it reaches severe stages. Although spirometry tests are the definitive method for COPD diagnosis, their accessibility is limited in under-resourced regions. In contrast, Chest X-rays (CXRs) are more universally available, suggesting their potential utility as preliminary screening tools for COPD. This study applies deep learning algorithms to the large-scale VinDR-CXR dataset to identify early-stage COPD using CXRs. The research employs the ChestX-ray14 dataset for model pre-Training and then utilizes VinDR-CXR for model development and validation. Through transfer learning, it was observed that the Xception base model with a recall of 98.2% obtained via fine-Tuning was better than the ResNet50 model. Grad-CAM (Gradient Class Activation Mapping) heatmaps and SHAP (SHapley Additive exPlanations) offer explainability for true positive cases on the Xception model from both datasets. The findings underscore the potential of DL models in facilitating early COPD detection via CXRs, especially where spirometry remains less accessible. Further exploration into multimodal-based COPD diagnosis is recommended for subsequent research. © 2023 IEEE.

关键词： Large datasets

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Design and Implementation of Advanced Driver Assistance Systems

Design and Implementation of Advanced Driver Assistance Syst...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ali, Waleed Abd-Elshafi Saleh, Hatem Abdelhamid Eid, Haneen Salah Fathi, Ahmed Mohamed Mohamed, Abd-El Rahman Amr Higher Institute of Engineering EL- Shorouk Academy Department of Communications and Computer Engineering Cairo Egypt Misr University for Science and Technology Department of Electronics and Communications Engineering Giza Egypt

ISBN: (纸本)9798350351408

Advanced Driver Assistance Systems (ADAS) are a pivotal advancement in automotive technology, enhancing vehicle safety and driving experience. In our project, we have successfully designed and built a robotic prototype car. We have implemented a comprehensive control system for the car's movement, and the following features of ADAS systems: Bump Detection, Precise Lane Tracking, Adaptive Cruise Control, Traffic Sign Recognition, and Blind Spot Detection. Bump detection systems are equipped to detect and alert drivers of speed bumps and other road irregularities, in which we use NVIDIA Jetson Nano, Astra Pro Plus Depth camera as RGB and Depth sensor and use Custom AI Model based on YOLOv5 to detect different Bumps on the road and we use Python, TensorRT, and OpenCV. Lane Departure Warning (LDW) alerts drivers when their vehicle unintentionally drifts out of their lane, we use NVIDIA Jetson Nano as our main microcontroller unit (MCU) responsible for our Machine learning and Astra Pro Plus Depth camera as our RGB sensor, we use Python, Python's libraries such as OpenCV, and NumPy to implement LDW. Adaptive Cruise Control (ACC) adjusts the vehicle's speed automatically to maintain a safe distance from the vehicle ahead, we use NVIDIA Jetson Nano, Astra Pro Plus Depth camera as RGB and Depth sensor and use Custom AI Model based on YOLOv5 to detect different vehicles on the road, we use Python, TensorRT, and OpenCV. Traffic Sign Recognition can interpret and display traffic signs to keep drivers informed about speed limits, no-entry signs, and more, we use NVIDIA Jetson Nano, and Astra Pro Plus Depth camera as our RGB sensor and use Custom AI Model based on YOLOv5 to detect different Traffic Signs on the road, we use Python, TensorRT, and OpenCV. Blind Spot Detection (BSD) technology helps drivers identify vehicles in their blind spots and alerts them to avoid dangerous maneuvers, we use ESP32 as an MCU and Ultrasonic sensor, and use C++ to program the BSD feature. © 2024

关键词： Advanced driver assistance systems

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Advanced Fake Review Detection via Aspect Extraction and TPA-BiLSTM-CSSA 2

Advanced Fake Review Detection via Aspect Extraction and TPA...

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

作者： Thakkar, Nilam Kumar, Arvind Sukumar, P. Sarode, Deepika G. Patil, Harshal Vijayakumar, E. Ldrp - Institute of Technology & Research Ksv University Svkm Department of Information & Technology Gujarat India School of Computer Applications Mriirs Haryana Faridabad India Nandha Engineering College Department of Computer Science and Engineering Erode India Dr.D.Y.Patil Institute of Technology Department of Engineering Pune Pimpri India Iilm University Department of Computer Science and Engineering Greater Noida India KIT-Kalaignar Karunanidhi Institute of Technology Department of Mca Coimbatore India

ISBN: (纸本)9798350373110

In recent years, methods to detect bogus reviews have attracted the attention of numerous businesses and educational organizations. In order for reviews to accurately represent genuine user experiences and opinions, it is quite challenging to identify fake reviews. Supervised learning has been a popular approach to tackle the problem. The issue is that manually marking fake reviews with a degree of reliability that would be useful for training is very difficult, if not impossible. There are three phases to the suggested strategy preprocessing, feature extraction, and training the model. This proposed employs a series of preprocessing techniques to get the dataset's raw data ready for computational operations. Dimensionality reduction and the quantification of content-related and text-segment-related features comprise feature extraction. To train the model, a TPA-BiLSTM-CSSA was used. On average, the suggested method achieves a higher accuracy of 93.71% than BiLSTM and CSSA. © 2024 IEEE.

关键词： Fake detection

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Effective Teaching Aids for People with Dyslexia 5th

Effective Teaching Aids for People with Dyslexia

引用

5th International Conference on Information Systems and Management science, ISMS 2022

作者： Banerjee, Jaya Chakraborty, Durbar Chakraborty, Baisakhi Basu, Anupam Department of Computer Science and Engineering National Institute of Technology Durgapur713209 India

ISBN: (纸本)9783031311529

Dyslexia refers to a learning disability that affects a wide range of individuals, specifically children worldwide. Subjects with dyslexia have different experiences with recognizing the letters and the words comprising it. While several modern technologies have been envisaged for the purpose of detecting dyslexia effectively, the means to assist this learning disorder is largely inadequate till date and technical interventions in this regard have either been quite slim or the solutions presently at hand fail to take up a pragmatic approach towards dealing with the issue at hand. In this research, we have adopted an application-based efficient approach which will assist and enhance the learning process of the people who have been medically diagnosed with dyslexia. People with dyslexia tend to have problems reading text or electronic documents in the conventional font which is largely used across various resources and this invariably slows down their learning process. Dyslexia Assistant aims to address this issue and takes up multiple feasible approaches which will help enhance the visual as well as auditory perception of the concerned person with dyslexia during the course of the learning process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Exploratory Data Analysis: An Analysis on Geotagged Twitter COVID Data 3

Exploratory Data Analysis: An Analysis on Geotagged Twitter ...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Gethse Sharon, D. Juliet, Sujitha Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350322842

Exploratory data analysis is the need to analyze data in depth. It comprises of basic methods like spot anomalies, stemming, hypothesis testing, check assumptions and visualize data. The sentiment analysis is a main function of exploratory analysis of data which is carried out along with keyword extraction is mentioned in the paper. The main objective of this proposed work is carried out by analyzing the twitter COVID-19 data. The pandemic played a major role in every living thing and so did it spread through the whole world by means of social media. The spread of the information is gathered here by using exploratory data analysis in python programming. © 2023 IEEE.

关键词： COVID-19

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FabricSense: A Tactile-Based Fabric Structure Detection Methodology for Automatic Temperature Adjustment

FabricSense: A Tactile-Based Fabric Structure Detection Meth...

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2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Dayananda, Chaithanya Hemashree, P. Harshini, M. Sai Guruprasad, N. Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India Annamcharya Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350302820

Based on tactile information and content-based image retrieval (CBIR), this methodology proposes a novel way for fabric structure detection and automatic temperature control. The technology accurately identifies fabric structures and establishes the ideal ironing temperature by combining tactile qualities, visual elements retrieved from fabric photographs, and programmed thermostat sensors. Unlike standard visual-based methods, this approach analyses colour, texture, and shape properties of fabric samples using tactile information and CBIR algorithms, enabling reliable fabric structure detection. Through testing and improvement, the system's performance is confirmed, providing exact temperature adjustment and improving the ironing experience. © 2023 IEEE.

关键词： Iron

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Safe Characteristic Signature Systems with Different Jurisdiction Using Blockchain in E-Health Records 1

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3rd International Conference on Data science, Machine Learning and Applications, ICDSMLA 2021

作者： Dalali, Shivakumar Pramod, B.K. Kumar, Ranjith Thejas Jain, M.J. Department of Computer Science Engineering Don Bosco Institute of Technology Bengaluru India

ISBN: (数字)9789811959363

ISBN: (纸本)9789811959356

Nowadays users can store the information remotely in the cloud and whenever they want, they can access information at any place with distributed storage system. For the information stored in remote places, information respectability reviewing is used to ensure truthfulness of the information put away in the virtual storage. In distributed virtual storage, for example, the Bank Transaction Records system, the virtual storage may consist of important information. The important information is not to be displayed to the unknown persons while the virtual records are accessed. Accessed documents are encoded and that can understand the sensitive data carefully and neatly. The most effective method to acknowledge information offering to sensitive data are carefully handled in remote places, data reviewing not been systematically examined till today. So, to address this issue, we are bringing the far-off information examine to ensure the idea that acknowledges information directed to sensitive data handled carefully here. In this plan, a tool is brought to clean the information squares comparing to most wanted data of the record and changes these information squares’ marks to essential one for the cleaned document. These marks are carried out to check the completeness of the cleaned document in the span of reviewing. Afterward our scheme uses the record stored in the virtual storage that is accessed and used by needy ones based on the precondition that the important content is secured. The distant information rectitude inspect is ready to be competently executed. Suggested plan is based on personality-based-cryptography. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensitive data

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Classification of Brain Disorder Diseases using Machine Learning based Data Mining Techniques

Classification of Brain Disorder Diseases using Machine Lear...

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2024 International Conference on science, technology, engineering and Management, ICSTEM 2024

作者： Shalini, Alajangi Balarengadurai, C. Prasad, Boddu Ravi Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of CSE Marri Laxman Reddy Institute of Technology and Management Hyderabad India

ISBN: (纸本)9798350376913

Brain disorders include a wide range of illnesses, including mental illnesses and neurological disorders, for which an early and precise diagnosis is essential to successful treatment. This review aims to give an in-depth analysis of current developments and problems in this important field by examining the evolution of machine learning (ML) with respect to data mining usage in the categorization of brain illnesses. The study starts by clarifying the need of prompt and accurate diagnosis in brain illnesses, highlighting the possibility of utilizing machine learning and data mining tools to supplement conventional diagnostic approaches. A thorough analysis of the literature demonstrates the wide range of approaches used for classifying brain disorders, from sophisticated deep computational systems to traditional machine learning methods. The exploration clarifies the complexities of model construction by covering important topics including feature selection, dataset qualities, and assessment measures. Numerous research show how well machine learning approaches may be used to differentiate between various brain illnesses, showing encouraging outcomes in regard to specificity, sensitivity, and accuracy. The report also outlines the difficulties those working in this subject encounter, such as the requirement for vast and diverse datasets, accessibility, and variety of data. Potential alternatives are also covered, including the creation of model-based interpretation for improved practicality and the synthesis of multimodal information. This study is an invaluable tool for scholars, employees, and health care providers working at the nexus of data mining, machine learning, and brain illness categorization. Through a comprehensive analysis of the present status of the sector and the identification of potential areas for advancement, this study adds to the continuing efforts to use technologies in the identification and therapy of brain illnesses. © 2024 IEEE.

关键词： Data mining

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