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Blockchain Solutions: Blockchain as a Service and Implementation Strategies 2

Blockchain Solutions: Blockchain as a Service and Implementa...

引用

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Balpande, Niteen Prasad, Srinivas Gitam Institute of Technology and Management Computer Science and Engineering Visakhapatnam India

ISBN: (纸本)9798350325799

A unique decentralised ledger, subsequently known as the Blockchain, allows numerous transacting parties to agree upon the shared state of the ledger without intermediaries or reliable third parties. This study investigates the growing practice of employing blockchain technology as a service and its potential uses across various sectors. The paper discusses the benefits of implementing Blockchain as a service while explaining blockchain technology's fundamental ideas and characteristics, such as decentralisation, immutability, and transparency. Companies and their respective offerings within the contemporary marketplace engage in deliberations regarding the prospective benefits associated with the utilization of Blockchain as a service(BAAS), like cost-efficiency, scalability, and security. The study also covers real-world applications of Blockchain as a service, including payment processing, identity verification, and supply chain management. The reach of blockchain-based financial systems went beyond crypto-currencies with the introduction of the Turing-complete Ethereum blockchain. The paper explores the different blockchains such as Ethereum, Cardano, Tron, Polygone, Avanache and other Consensus Mechanism based blockchains. The work is based on assessment of blockchains, publication, and technical report data based on the security and transparency and the outcome of using decentralised blocakchain as a service, as a alternative to the traditional services based on the centralisation. © 2023 IEEE.

关键词： Smart contract

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Utilizing Metaheuristic Machine Learning Techniques for Early Diabetes Detection 2

Utilizing Metaheuristic Machine Learning Techniques for Earl...

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2nd International Conference on Informatics, ICI 2023

作者： Jain, Anjali Singhal, Alka Jaypee Institute of Information Technology Computer Science and Engineering Noida India

ISBN: (纸本)9798350343830

The pervasiveness of diabetes mellitus has been raised quickly in recent years. According to present statistics nearly 90% of diabetic people have type 2 diabetes globally. The only solution for this is an early prevention strategy, as it has no longterm proper treatment. Nowadays, Metaheuristic optimization algorithms along with various deep learning and machine learning algorithms and techniques are now being utilized in the medical field for chronic diseases with effectiveness and better outcomes than conventional methods. In this paper the authors objective is to increase the effectiveness of early diabetes detection and suggesting the food items based on carbohydrates, fiber and sugar content. The Bat metaheuristic approach is inspired by biological systems, was utilized to choose the key features during the preprocessing of medical data. The performance of the suggested methods was contrasted with that of BAT nature optimized algorithm, which is metaheuristic bio-inspired feature selection algorithm. Different classifiers were then used in conjunction with the BAT and Firefly algorithms. Then datasets used for implementation is Pima India Diabetes Dataset, Hospital Frankfurt and food nutrition data set for diet recommendation. Smote is applied for data balancing class distribution. The outcomes demonstrate that the BA approach performs better on balanced dataset than, with 97% accuracy especially when the dataset's instance count rises. © 2023 IEEE.

关键词： Nutrition

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Government Infra Assessment System

Government Infra Assessment System

引用

2023 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2023

作者： Thakur, Reena Gadbail, Abhijeet Gomkale, Akash Jambhulkar, Tanuksha Bhujade, Harshal Charpe, Aditya Jhulelal Institute of Technology Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350398748

Without involving any other parties and abstaining from any other compromised operations across India, Government Infra Assessment System would provide all the necessary information regarding Government Infrastructure to the Government itself. Government employees will not only benefit from time savings, but also from the ability to view the database whenever they want without waiting. © 2023 IEEE.

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Fusion of Image and Point Cloud for Accurate Obstacle Detection in Autonomous Driving 6

Fusion of Image and Point Cloud for Accurate Obstacle Detect...

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6th International Conference on Machine Learning and Machine Intelligence, MLMI 2023

作者： Liu, Yunxiang Ma, Haili School of Computer Science and Information Engineering Shanghai Institute of Technology China

ISBN: (纸本)9798400709456

Obstacle detection is of great significance for autonomous driving. Accurately identifying obstacles and transmitting obstacle information to the underlying planning and control system is essential for effective and safe autonomous driving. Obstacle detection is commonly performed using sensors such as LiDAR and cameras. LiDAR has poor detection performance for small objects, while cameras excel at recognizing various objects but cannot directly obtain object position information. A good solution for obstacle detection is to combine cameras and LiDAR. In this approach, obstacle detection involves first detecting bounding boxes of obstacles from the image data obtained by the camera, and then matching these bounding boxes with point cloud data obtained from the LiDAR to obtain precise information about the obstacles. Obtaining accurate detection bounding boxes is an important challenge in this approach. Therefore, this paper designs experiments to compare the detection performance of different object recognition algorithms in terms of bounding box detection. Comparative experiments have demonstrated that the YOLOv8 algorithm exhibits excellent performance in image object detection. When combined with point cloud data, it achieves precise detection of obstacles in autonomous driving, yielding accurate results. © 2023 ACM.

关键词： Object detection

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An AgroBot: Natural Language Processing Based Chatbot for Farmers 4

An AgroBot: Natural Language Processing Based Chatbot for Fa...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Marla, Anushka Paul, Rejath Saha, Arupam Kumar Basha, Niha Kamal Balasundaram, Anandhakrishnan School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350300888

In an industry that is enduring a rapid transformation, rowers are gaining access to new technologies that help them improve agricultural yields and resource management. TensorFlow-built chatbots can provide instantaneous assistance to producers. TensorFlow is a machine learning (ML) framework for data automation, model monitoring, and model retraining. It was used to analyse more than 80 million call records from the government portal Kisan Call Canter (KCC) using multiple data preprocessing techniques in conjunction with TensorFlow. Training enabled the chatbot to distinguish between agricultural questions, crop management, soil health, and pest control intentions and entities. This chatbot intends to implement a voice recognition module that will enable users to submit speech-based queries rather than text-based ones. In the future, the chatbot can be trained to comprehend images with high precision and be able to identify crops, provide recommendations on how to increase crop productivity, and promote the need for sustainable farming. The automaton has been evaluated using metrics for precision and error reduction. It's conceivable that TensorFlow-trained chatbots could revolutionize agriculture by providing producers with real-time data and advice on how to increase their yields and revenue. © 2023 IEEE.

关键词： Crops

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IoT-based Smart Drain Monitoring System with Real-Time Alert Messages and Data Analysis 3

IoT-based Smart Drain Monitoring System with Real-Time Alert...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Suheb, Syed Sha Soni, Siddhartha Saravanan, Pranav Thurai Pandian, M. Berlin, M.A. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350343632

In contemporary urban design, reliance on smart drainage systems for effective water management and environmental safety is paramount. The study delves into the development and application of a real-time smart drainage system, continuously assessing crucial environmental factors like temperature, humidity, organic and inorganic gas levels, and drainage water levels. Utilizing a network of sensors and advanced algorithms, the technology analyzes data in real-time, issuing immediate warnings when any monitored component surpasses a predefined threshold value. This enables rapid user response to avert potential dangers. The study comprehensively covers the design, implementation, and evaluation of the Smart Drainage System, showcasing its efficacy in environmental protection and ensuring the sustainability of urban water resources. The ultrasonic sensor measures drainage levels, while the MQ-135 sensor quantifies the drainage system's PPM carbon footprint. Monitoring air quality, the MQ-135 sensor detects various gases like ammonia, nitrogen oxides, and carbon dioxide. The study employs the MQ-135 sensor to measure the carbon footprint in drainage water, serving as an indicator of pollution and environmental degradation. In cases where the carbon footprint PPM levels exceed a predetermined threshold, the smart drainage system notifies users, allowing them to promptly implement preventive measures and avoid potential hazards. The MQ04 sensor plays a key role in measuring methane gas proportion. © 2023 IEEE.

关键词： Drainage IoT (Internet of things) MQ (Mikro Quant)-04 sensors MQ (Mikro Quant)-135 sensors smart sustainability ultra sonic sensors

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Handwritten Digit Recognition Using Neural Network with Gabor Filter for Information Fusion

Handwritten Digit Recognition Using Neural Network with Gabo...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2022

作者： Kumar, Akshay Murugan, Brindha Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031151743

Because of several aspects such as variability in individual’s personal writing style, handwriting recognition is the most difficult area to master. Despite the massive amount of research and development that has gone into it, it has yet to tackle all of the commercially important and conceptually intriguing concerns. Many document processing and evaluation systems that use digital image processing techniques need handwritten digit recognition. In many pattern recognition applications, document processing and document analysis are becoming more ubiquitous. The aim of this paper is to propose a system which will take a digital image as an input and will automatically detect and display the text present in it. This will save the time and efforts and will reduce the chances of involvement of human error in the system. This system can be further used in many applications like reading postal addresses, bank check amount, forms, etc. The paper used a Gabor filter bank for extracting features from a digit image. This reduction in preprocessing overhead brings up the performance of the system in prediction of digits. The prediction of this system is almost real time with very high accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Enhancing Network Security: A Novel Hybrid ML Approach for DDoS Attack Detection in SDN 15

Enhancing Network Security: A Novel Hybrid ML Approach for D...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rakesh, V.S. Vasanthakumar, G.U. Department of Computer Science and Engineering Cambridge Institute of Technology Bengaluru Visvesvaraya Technological University Karnataka Belagavi India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Visvesvaraya Technological University Karnataka Belagavi India

ISBN: (纸本)9798331300579

Software-defined networking represents a ground breaking advancement in network technology, characterized by its desirable attributes such as enhanced flexibility and manageability. Although ongoing, the issue of DDoS assaults in SDN is characterized by malicious and obtrusive network traffic that overwhelms SDN resources. Despite numerous security methodologies aimed at detecting DDoS attacks, the challenge of effectively addressing this issue continues to persist as an active area of research. The XG-Light Hybrid, a unique hybrid system, has been developed in this work as a solution to this problem. This discovery is significant because it has the potential to dramatically increase the reliability of DDoS attack detection in SDN environments, hence boosting network security and stability. Key findings reveal that the proposed hybrid approach outperforms individual machine learning algorithms with respect to DDoS detection. © Grenze Scientific Society, 2024.

关键词： DDoS Detection Light GBM Machine Learning Network Traffic SDN XG Boost

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Insurance Amount Prediction Based on Accidental Car Damage Level using AI 8

Insurance Amount Prediction Based on Accidental Car Damage L...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Kumar, D. Manoj Reddy, Chadipriala Nithin Kumar Sai, Gunisetty Venkata Chowdary, Puligundla Sujan Srm Institute of Science and Technology Electronics and Computer Engineering Chennai India

ISBN: (纸本)9798350396638

This document presents a machine learning-based approach for predicting the insurance amount for a car damage claim based on the level of damage caused by an accident. The proposed model uses a dataset of past car damage claims, including the level of damage and the corresponding insurance amount paid out. The dataset is subjected to the application of a number of machine learning algorithms, such as decision trees, random forests, and neural networks. The effectiveness of these algorithms is assessed using a variety of measures, including accuracy, precision, recall, and F1-score. The results show that the model achieves high accuracy and F1-score, indicating its effectiveness in predicting the insurance amount based on the level of damage. This proposed approach has the potential to improve the accuracy and efficiency of the insurance claims process, ultimately benefiting both insurance companies and their clients. Insurance companies use a variety of factors to determine the amount of compensation paid out to a customer for a car damage claim. One of the most important factors is the level of damage caused by the accident. However, accurately assessing the level of damage can be a difficult undertaking, as it requires a high level of expertise and experience. To address this challenge, machine learning techniques can be used to build models that can predict the insurance amount based on the level of damage. In order to do this, a model must be trained using past data to identify trends and connections between the severity of the harm and the associated insurance payouts. Overall, insurance amount prediction based on accidental car damage level using AI is a promising area of research and has the potential to benefit both insurance companies and their clients. © 2023 IEEE.

关键词： Insurance

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Enhancing Patient Care: A Data-Driven Approach Using Decision Tree-ELM Models for Improved Safety and Quality 3

Enhancing Patient Care: A Data-Driven Approach Using Decisio...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Telsang, Supriya Shrikant Singh, Akhya Sanjay Kaur, Simarpreet Hazela, Bramah Singh, Shikha Chittapragada, Hemanand Vishwakarma Institute of Technology Bibwewadi Maharashtra Pune India Shah & Anchor Kutchhi Engineering College Mumbai India Amity University Amity School of Engineering and Technology Lucknow Department of Computer Science and Engineering Uttar Pradesh India Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Andhra Pradesh Madanapalle India

ISBN: (纸本)9798350354171

In recent years, educational initiatives that seek to combine teaching medical students about patient safety and quality improvement (QI) have received funding from both the World Health Organization and the American Association of Medical Colleges. When developing curriculum, it is important to keep in mind the interests and values of the target audience. The proposed used a poll to find out how students felt about lessons on patient safety and quality improvement. It would be more efficient to incorporate patient safety and quality improvement into clinical education rather than teach it in separate computer modules or during pre-clinical lectures. This proposed approach consists of three stages, which are feature selection, model training, and preprocessing. Before doing thorough text processing, stop words are typically filtered out in conventional methods of natural language processing and information retrieval. Unigram and bigram selection are part of feature selection. During the entire model training process, a Decision Tree-ELM Ensemble was utilized. This innovative approach outperforms ELM and Decision Tree with an average accuracy of 94.36%. © 2024 IEEE.

关键词： Students

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