Estimating a person's personality is essential in understanding how they manage stress, lead, communicate, collaborate, and influence others. One of the critical factors that affect how people interact with their ...
Estimating a person's personality is essential in understanding how they manage stress, lead, communicate, collaborate, and influence others. One of the critical factors that affect how people interact with their environment is their personality. This project aims to utilize machine learning and also natural language processing techniques to predict the personality of students based on a questionnaire. The system analyzes the user's personality in the database, which already records personality traits. The system then identifies the user's personality based on the MBTI model, a widely used personality model. This project's significance lies in the capability of the system to aid students, teachers, and businesses in identifying students' personalities to plan their actions better. The ML and NLP techniques implemented in this system can categorize or anticipate students' personalities, thus assisting businesses in their hiring process.
Doping-less (DL) underlapped dielectric modulating bio-tunnel field-effect transistor (DU-DM-TFET) is proposed in this manuscript. The impact on ON-current (Ion), and the consequent sensitivity owing to deviations of ...
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Breast cancer is a prevalent and deadly disease that primarily affects women. Breast cancer has claimed the lives of many people all over the world. Early breast cancer detection, on the other hand, can significantly ...
Breast cancer is a prevalent and deadly disease that primarily affects women. Breast cancer has claimed the lives of many people all over the world. Early breast cancer detection, on the other hand, can significantly improve a woman's chances of survival. There are various approaches and procedures for detecting cancer in breast tissue. Breast cancer may be detected using Image Processing, Machine Learning, and Deep Learning methodologies and techniques. People who use trustworthy techniques and make better selections will be able to discover breast cancer in its early stages and save a woman's life. This research aims to contemplate and make inferences from diverse studies on the subject of classification using Machine Learning approaches. The researchers and physicians who devote their time and expertise to enhancing breast cancer screening methods will definitely profit from this research study.
Test automation is crucial for agile software projects to enable frequent delivery of working software with cost and time and minimal bugs. However, selecting the right automated testing tool is considered challenging...
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ISBN:
(数字)9798350354133
ISBN:
(纸本)9798350354140
Test automation is crucial for agile software projects to enable frequent delivery of working software with cost and time and minimal bugs. However, selecting the right automated testing tool is considered challenging due to the wide range of such existing tools. Additionally, the challenges occur clearly due to several issues such as the programming code language, the categorization of the developed system, and the tester’s knowledge and skills. This paper aims to address this gap by proposing an evaluation framework for comparing and classifying the existing automated testing tools used in agile projects. The framework is developed based on an extensive literature review of existing agile testing methodologies and common commercial automation testing techniques. The key criteria for tool evaluation are identified to cover the main testing objective aspects such as test design support, testing interfaces, reporting capabilities, etc. These criteria are considered the core methodology for this study used to analyze and compare the popular open-source and commercial tools. The proposed evaluation framework provides agile practitioners with guidelines to assist in selecting the appropriate tools based on their specific project needs such as budget, timelines, and technical expertise. This study is considered a comparative evaluation of existing agile testing tools to highlight their key strengths and limitations. The findings of this study categorized the testing tools based on the interface, code, design, and report features. This research contributes to assisting the project developer and tester in selecting suitable tools for the adoption of automated testing tools in their agile software projects. It also identifies the direction for future work, such as integrations with modern development methodologies and technologies.
Gesture recognition, specifically the identification of question signs, plays a pivotal role in advancing communication for the hearing-impaired population. However, this aspect of sign language classification has rec...
Gesture recognition, specifically the identification of question signs, plays a pivotal role in advancing communication for the hearing-impaired population. However, this aspect of sign language classification has received limited attention, underscoring the need for comprehensive exploration. The recognition of question signs is instrumental in facilitating inclusive and accessible communication, which, in turn, promotes social integration and empowers individuals with hearing impairments. This study aims to bridge this gap by introducing an effective method for recognizing question signs in Indian Sign Language (ISL) from video sequences, utilizing the proposed DCDW-LSTM Attention model. The approach is tested on a collected Indian Question Word Video dataset (IQWVd) and INCLUDE50 benchmark dataset, and the results are encouraging.
In this paper, we propose a novel pseudonymous decentralized reputation system that aims to establish trust within decentralized networks. The system allows users to maintain pseudonymous identities while ensuring the...
In this paper, we propose a novel pseudonymous decentralized reputation system that aims to establish trust within decentralized networks. The system allows users to maintain pseudonymous identities while ensuring their reputation scores are transparent, trustworthy, and resistant to manipulation. We review existing reputation systems and discuss their limitations. Our proposed system incorporates cryptographic techniques, a consensus algorithm, and distributed data storage to ensure data integrity, privacy, and scalability, specifically designed for the Decentralized Finance (DeFi) ecosystem. We present an implementation of our system, focusing on its application in DeFi platforms, and evaluate its performance against various attack vectors. Finally, we discuss the implications of our system on decentralized financial applications, such as lending and borrowing protocols, and future research directions. Lastly, we explore the broader implications of our decentralized reputation system for the DeFi ecosystem, identifying potential applications in areas such as decentralized exchanges, insurance, and asset management.
Paper review and publishing is an important part of a researcher’s life. It is where a researcher’s time and resources that he has spent bears fruit. And this is enabled by getting his work reviewed by different pee...
Paper review and publishing is an important part of a researcher’s life. It is where a researcher’s time and resources that he has spent bears fruit. And this is enabled by getting his work reviewed by different peers. In our current state of things, researchers face a lot of issues while they try to publish their research manuscript. These issues include quality of peer-reviews, rise of predatory journals, bias between reviewers, and other issues regarding academic integrity. These issues in the traditional system are what make the system inefficient. Therefore, we propose a more efficient blockchain-based system that will try to tackle these issues. Hyperledger Fabric is one of the most popular Blockchain platforms which provides a permissioned blockchain. Our proposed system would leverage the permissioned blockchain. This is to ensure that not everyone from outside the system has access to the data present in the system and only the members of the blockchain are able to access the data. The workflow of the peer-review system would be stored as transactions. The blockchain would be used to keep track of these transactions and order them properly in order to maintain provenance. This system would prove to be a lot more efficient than the traditional existing systems.
The growth of Food Delivery Application (FDA), helps customers to get variety of food items in their door steps. This helps restaurant to serve their customers with different food items, and to increase their business...
The growth of Food Delivery Application (FDA), helps customers to get variety of food items in their door steps. This helps restaurant to serve their customers with different food items, and to increase their business. FDA also provides jobs to millions of food delivery persons. But the major drawback of FDA is high commission charges for food orders. This article presents zero cost online food delivery system with ML prediction models. Zero cost online food delivery system provides user friendly interface to restaurant, home makers, customers and delivery persons. In this system, the restaurant or home makers can showcase their food items. The customers would be able to place the order. Delivery person can show interest towards delivery of the order. The customers have an option to select the delivery person for delivery and pay the delivery charges. All the payments are done through UPI payment. Advantage of the system is, it will not collect commission from home makers (or) restaurant owners and customers for the orders and also Machine Learning models is used to suggest food items based on different factors. Random Forest, Decision Tree Regression, K-Nearest Neighbor, and Extra Tree Classifier models are used to suggest food items to the customer.
An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect atta...
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An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect attacks using conventional techniques like a virus detection system, misuse detection software i.e. the database of attack signatures that it uses to compare packets. Despite the researchers' significant efforts, IDS still struggles to identify new intrusions, to improve detection accuracy, and to reduce false alarm rates. To overcome the problems mentioned above this paper proposes an unique model named Intrusion Detection System using Machine Learning Analytics (IDSMLA), which uses SMOTE oversampling technique to deal with class imbalance problem, it also uses Minimum Redundancy Maximum Relevance (mRMR) to perform feature selection as feature selection reduces time complexity by eliminating irrelevant features and hence increasing the accuracy of the model and finally to perform classification task, the proposed model IDSMLA uses Extra Trees(ET) bagging ensemble technique. The performance of the proposed model IDSMLA is measured using accuracy and F1-score using 10-folds cross validation. Experimental results have demonstrated that the proposed model IDSMLA greatly outperforms different single-classifier based models, different ensemble models as well as different models present in literature.
Mental health concerns are on the rise worldwide, and the stigma and lack of confidentiality surrounding therapy make it difficult for many people to seek the help they need. In response to this problem, my research p...
Mental health concerns are on the rise worldwide, and the stigma and lack of confidentiality surrounding therapy make it difficult for many people to seek the help they need. In response to this problem, my research proposes the development of an AI chatbot that acts as a virtual psychologist, providing accessible and confidential mental health support. Using NLP technology, the chatbot will be able to understand and respond to user queries, offering personalized therapy sessions based on individual input. My paper explores various NLP models and techniques that can be used for the chatbot's development, as well as ethical and privacy considerations. We are truly passionate about the potential of this chatbot to revolutionize the mental health industry, providing affordable and accessible therapy while breaking down the stigma associated with seeking help. While there is still much work to be done to ensure the chatbot's reliability and effectiveness, We are excited to be at the forefront of this innovative new field, working towards a brighter future for mental health support.
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