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Decentralized Fundraising Application Using Blockchain 1

Decentralized Fundraising Application Using Blockchain

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1st IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2022

作者： Dange, Rishi Sawant, Aditya Chavan, Aditya Bhardwaj, Prerna Bundele, Ashwini Pune Institute of Computer Technology Pict Department of Computer Engineering Pune India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

It is critical for a donor to understand where and how their money is being used. This will not only make them happy, but it will also encourage them to donate again and propose or publicise donations, resulting in a large donation amount for the needy. This paper proposes a decentralized application which utilizes blockchain and smart contracts which is built with the aim of helping the needy and underprivileged people by providing a platform that is secured, flawless, easy to use and transparent with the funds. Fundraising applications have come into existence to help people and communities alike. But these applications have been far from perfect. People have shown general distrust in a third-party to securely handle their funds before reaching the people donations were meant for. Some reports of misuse of collected funds have worsened the situation. The lack of transparency and involvement of third-party has hindered people from donating at times and have left many needy people without any help. To combat these issues, we have decided to build a fundraising website accessible to everyone whose working will be transparent. Registered users, once verified, can donate to various charity drives and funds and also ask for funds when in need. using Machine learning recommendations of similar fundraisers are made who like to donate to some particular charity drives. To acheive transparency, the blockchain technology is used to store records of donations made by the users. Decentralization of fundraising process also eliminates involvement of any third-party group. © 2022 IEEE.

关键词： Smart contract

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LegalAI Across Languages: A Comprehensive Survey on Tasks, Methodologies and Challenges 1

LegalAI Across Languages: A Comprehensive Survey on Tasks, M...

引用

1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Deshpande, Anjali Deshmukh, Pratiksha COEP Technological University Dept. of Computer Science and Engineering Pune India PICT Dept. of AI & DS Pune India School of Computational Sciences COEP Technological University Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798350355611

Natural Language Processing (NLP) is revolutionizing the legal domain, enabling tasks such as predicting legal outcomes, summarizing complex documents, identifying key entities, and assessing bail risks. This survey paper presents a comprehensive review of the existing research on NLP tasks applied to legal documents and judgments across various languages, with a special emphasis on low-resource languages. Despite the extensive work done in widely spoken languages like English, there remains a critical need to extend these advancements to languages with limited resources and data availability. This paper categorizes and analyzes the NLP tasks in the legal domain based on the languages they target. It highlights the methodologies employed, datasets utilized, and the performance of different models across diverse languages. Furthermore, it identifies the gaps in current research and proposes potential directions for future work, particularly in improving the accessibility and applicability of NLP solutions for legal texts in underrepresented languages. By providing a comprehensive overview of advancements and obstacles, this survey seeks to assist future research endeavors that bridge the disparity between high-resource and low-resource languages in legalAI applications. © 2025 IEEE.

关键词： Risk assessment

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Collating Random Forest Classifier and Artificial Neural Networks for the Risk Detection of Maternal Health

Collating Random Forest Classifier and Artificial Neural Net...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Nahatkar, Sneha Belhe, Adityaraj Sanjay Ganthade, Vedant Vinay Uravane, Prathamesh Suhas Pattewar, Tareek PICT Pune Electronics & Comp Engg Department Pune India Vishwakarma University Pune AIDS Department Pune India Vishwakarma University Pune Computer Engineering Department Pune India

ISBN: (纸本)9798331512965

This paper compares the performance of ANN models to Random Forests on benchmarking a test dataset against the risk levels of maternal health as low, mid, or high. The features considered are clinical features, namely age, blood pressure, and heart rate. The dataset was imbalanced and needed adapting the ANN architecture by fitting class weights and dropout regularization. The best test accuracy obtained in this fashion was 73%. As the ANN had learned many of the complex data patterns in the dataset, it was, however, still limited by the moderate size and skewness of the dataset. The Random Forest model with grid search produced a much better accuracy of 85.71% compared with ANN, thereby suggesting relatively high generalization across all the classes for better minority risk level classes. An ensemble approach of the Random Forest method appears to better serve toward high accuracy along with interpretability for clinical decision support in maternal health risk assessment. © 2025 IEEE.

关键词： Electronic health record

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Blockchain Solution to Electronic Healthcare Records

Blockchain Solution to Electronic Healthcare Records

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Pujashree Vidap Abhishek Bhargav Ratnamala Paswan Ashwini Jewalikar Department of Computer Engineering Pune Institute of Computer Technology (PICT) Pune

Electronic Health Records (EHR) are very sensitive and private records that requires at most security and privacy. Because of lack of trustworthy and reliable health data sharing mechanisms, the majority of EHR data sharing is still done via mail. This impacts the proper treatment of patients and results in unwanted delays between participating organisations. The sharing sensitive health records via mail or physically results in various issues such as privacy, security and interoperability. As a decentralised framework, Blockchain provide cryptographic guarantees of information security, integrity, secrecy and smart contracts for access to data. Since EHR are a sensitive and private data, hence the paper proposed a system which uses permissioned blockchain solution for storing and sharing electronic healthcare records using the Hyperledger fabric framework. The proposed system allows the patients to have total control over their electronically generated medical records, and they are able to authorise doctors to examine and add new EHRs using grant and revoke access methods. The proposed system is inherently good in interoperability and secure due to nature of framework used and provided cryptoaraphic guarantee of non-tampering.

关键词： Privacy Smart contracts Information security Fabrics Electronic healthcare Blockchains Reliability

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Design & Development of Smart Electric Vehicle Safety Device by using IoT and AI 4

Design & Development of Smart Electric Vehicle Safety Device...

引用

4th International Conference on Emerging Research in Electronics, computer Science and Technology, ICERECT 2022

作者： Thombare, Shreyas Baral, Samiksha Gangrade, Amitesh Jaiswal, Rupesh Indian Institute of Technology IIT Kharagpur Electrical Engineering & ATDC Department Kolkata India Pune Institute of Computer Technology PICT Electronic and Telecommunication Engineering Pune India

ISBN: (纸本)9781665456357

As the usage of electric vehicles grows in popularity, the risk of accidents caused by them is also drastically increased. High speed, drink-driving, distracting thoughts, overstress, and malfunctioning electronic gadgets, over the temperature in charging are the leading causes of accidents & fire. Also, in electric battery-operated cars, if the battery charging mechanism is not properly thermally stabilized, the battery may catch fire. As a result, the proposed system deals with accident detection systems that occur as a result of a driver's carelessness or system failure while operating an electric vehicle. Proposed Device created with a cardiac sensor in the seat belt and vehicle along with battery temperature, vibration & accelerometer sensors which can help to analyze the vehicle's yaw, roll, pitch, and slip motions. All of the data collected is a synced to ESP-32 controller through IoT-based cloud storage analysis to notify a neighboring helpline center about the accident. This data is also utilized in an AI-based model to detect one of the sovereign sources of death in growing countries i.e., cardiovascular disease, and it's cited by several throughout the globe. Cardiovascular disease, on the opposite hand, is called heart disease. This consolidates some dangerous factors of heart disease and the desire of the era of promoting correct, reliable and smart approaches that enable early identification to achieve quick treatment for the disease. This analysis paper also presents numerous attributes associated with a heart condition, and also the model is based on supervised algorithms of machine learning such as Support Vector Machines, Logistic Regression, Naïve Bayes, Decision tree, and random forest and algorithms of deep learning such as Artificial Neural Network, TabNet etc. It uses the prevailing dataset from V.A. Medical Center, Long Beach, and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. It includes 303 occurrences and 76 attributes. Only 13 a

关键词： Internet of things

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Understanding Unnatural Mortality: A Comprehensive Analysis of Machine Learning Approach for Classification of Causes 3rd

Understanding Unnatural Mortality: A Comprehensive Analysis ...

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3rd International Conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Gunje, Shamali Waghmare, Kalyani Sonawane, Sheetal Department of Computer Engineering PICT Pune India

ISBN: (纸本)9783031862892

Death is a tragedy regardless of its form, time, or manner in which it occurs. Death can be natural, unnatural through deliberate action of other, self-inflicted, or it can be due to the environmental factors. The primary aim of this paper lies in the comprehensive analysis and synthesis of existing literature concerning unnatural mortality. The literature is surveyed from various digital repositories for analyzing the trends and patterns of unnatural deaths. The study of literature identified the common causes of deaths. This paper offers a comprehension of the existing literature by combining and summarizing the patterns, trends, and contributing elements of unnatural fatalities. Additionally, a machine learning classification model is built to categorize news articles from the dataset into five different categories based on death causes. We conducted a comparative analysis using machine learning classifiers on 20,000 news articles from the Indian Express dataset. The Decision Tree model emerged as the most effective, with an accuracy of 90%. This comparison analysis demonstrates the various strengths and shortcomings of different classifiers in categorizing death causes. Our findings shed light on mortality patterns and show how machine learning can automate the classification of death-related data, allowing for early interventions and informed policy decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Taxonomies

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Development of the Decision Systems for Cloud Security Based on AI and ML 12th

Development of the Decision Systems for Cloud Security Based...

引用

12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Maral, Vikas Balasaheb Tambakhe, Manoj D. Dhage, Prashant Shukla, Jyoti Khalaf, Osamah Ibrahim Pise, Ganesh Shivaji Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Information Technology Sipna College of Engineering and Technology Amravati Maharashtra Amravati India Pune India Department of Computer Science and Engineering Arya College of Engineering Jaipur India Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Department of Information Technology PICT Maharashtra Pune India

ISBN: (纸本)9789819601387

One of the most important and crucial factors often considered in keeping a network secure is the adoption of an intrusion detection system. This process is commonly called as an IDS stage which is required to keep the network protected from certain attacks in real time. Due to the wide usage of cloud on a daily purpose, the platform is constantly evolving and becoming more advanced. This has helped the developers comprehend the necessity of advancing the process of IDS. However, there are certain obstacles and issues that might arise with its acceptance. One such factor is the problem of overhead. This problem of overhead occurs when the cloud server is overloaded with existing services and thereby cannot transit other services on urgency. In such a scenario, the working concept of an IDS comes into picture wherein this adaptive design helps the developers to overcome such issues and provide a conventional flow of services to the allocate resource. The primary purpose of the presented research paper is to adapt such a concept and therefore maximize the overall potential of the system. The author of the research paper also tends to enhance and increase the overall capacity required to identify the occurrence of such threats in an online manner which are expected to occur. A CICIDS2017 dataset is used to fulfill the purpose of the same and a recurring infrastructure is therefore built in order to understand the working nature of an intrusion detection system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Satellite Imagery Driven Flood Detection using Deep Learning

Satellite Imagery Driven Flood Detection using Deep Learning

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Dhawane, Pooja G. Navghare, Pranali Mane, Pranoti Nitnaware, Pranali N. Mashirkar, Sampada Mendhekar, Seema Y. Yeshwantrao Chavan College of Engg Dept. of Information Technology Nagpur India PICT Dept. of Computer Engineering Pune India Priyadarshini College of Engg Dept. of IOT Nagpur India School of Management Ramdeobaba University Nagpur India NC Pharmacy Wanadongri Nagpur India

ISBN: (纸本)9798331517953

Flood detection is crucial for effective dis-aster response and management, enabling early warning systems and targeted relief efforts. This paper proposes a deep learning-oriented approach for flood detection in Convolutional neural networks (CNNs) and transfer learning applied to satellite pictures techniques. Satellite imagery provides wide coverage and high spatial resolution, making it a valuable resource for flood monitoring. However, the complexity of flood patterns requires advanced computational techniques for accurate analysis. CNNs deep learning models have demonstrated impressive achievements in the realm of image classification, showcasing an ability to autonomously acquire significant features from visual data. The proposed methodology aims to train a robust flood detection model capable of discerning flooded and non-flooded regions within satellite images. transfer learning enhances the model's performance by adapting pre-trained models to t Transfer learning uses pre-trained models to improve the performance of the model. he flood detection domain, even in regions with limited historical flood data. the output of this work have the ability make revolution for flood detection systems and improve catastrophe handling techniques, leading to reduced vulnerability in flood-prone areas. by leveraging deep learning methodologies, we can advance our understanding of floods and build more resilient communities to confront this escalating threat. The paper introduces an innovative method for flood detection that combines CNNs and transfer learning in an effort to create a reliable and accurate flood detection model. The work's outcomes contribute to the advancement of flood monitoring systems, improving disaster response and reducing vulnerability in flood-prone areas © 2024 IEEE.

关键词： Satellite imagery

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Real-time weapon detection using Drone

Real-time weapon detection using Drone

引用

International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Deepak R. Hawale Pavin S. Game Department of Computer Engineering Pune Institute of Computer Technology (PICT) Pune India

ISBN: (纸本)9781665484527

Drones or Unmanned aerial vehicles (UAV's) are used widely because of their advantages like small size, low cost and portability. From past few years, drones have become important part in private as well as government sectors to reduce work load as well as to do work in less time. In agriculture field also drones are used to monitor crops, to supply water and to send live video of crops over network. Real-time object detection can play important role in security and surveillance. Drones can fly autonomously and it can capture live video. This video can be used for object detection. Proposed system captures live video frame using Raspberry pi camera module with help of drone. This video data is sent to real-time weapon detection system. If weapon is detected in video, then system give alert/notification on it, so operator can take appropriate action. Object detection module is created at computer side for fast object detection. OpenCV and YOLOv3 is used for weapon detection. Raspberry pi camera module work as a camera for computer. System has shown very good performance for detecting guns and rifle in stored as well as live streaming video.

关键词： Weapons Surveillance Crops Process control Object detection Streaming media Cameras

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Discovering the Location by Intelligent Chatbot Support using NLP

Discovering the Location by Intelligent Chatbot Support usin...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Jayashree J. Palliyamakkal Archana Ghotkar Department of Computer Engineering Pune Institute of Computer Technology (PICT) Pune India

ISBN: (纸本)9781665484527

Discovering the person location through a chatbot within the geographical virtual barrier boundary i.e., called Geofence that allows users to detect when someone enters or departs a location. Geofence main motive is for finding the person in a specific area rather than the entire map area. The polygonal geofence is a type of geofence that helps us to cover precisely the boundaries of a building that we want to cover. The coordinates of a building, i.e., the Latitude and Longitude, can be used to generate a polygonal geofence that covers only that area of the building. Although a circular geofence can be utilized, it will cover an undesirable area, which is why a polygonal geofence is preferable. With the assist of GPS/Wi-Fi technology to track a person's location, geofence will cover the necessary geographical virtual barrier borders, and a chatbot will provide the person's location anytime humans ask it. GPS can send location data to the chatbot whether the person is inside the virtual boundary; otherwise, it will indicate that the person is not in that zone. Similarly, Wi-Fi will assist in tracking the location of people inside the building per floor. The chatbot will assist in determining a person's location so that we can determine exactly where the person is standing. The chatbot is a text-based input system that allows us to communicate as if we were chatting with a human. Natural language understanding and Natural Language Generation are used in Natural Language processing the key objective is that any student who desires to meet with a specific teacher to rectify any doubts or submit work can employ this intelligent chatbot to discover the teacher's current position.

关键词： Automation Buildings Process control Chatbots Wireless fidelity Floors Global Positioning System

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