Reviewerly/exHarmony huggingface dataset:
An Approach of Intelligent Automated Resume Analysis &amp; Recommendations

Finding a qualified candidate for an open position can be a difficult task, especially when there are many applicants. The tedious process of fair screening and brief posting might be made easier by using an automated system for “Resume Grouping and Coordinating.” Physically sorting through the resumes isn't really an option because it would entail a lot of time and money, which the hiring firms just can't afford. This could result in the exclusion of qualified competitors or the finding of ineligible job hopefuls. With all the unnecessary information removed, screening is simplified, and enrollment professionals are better equipped to analyze each resume much more quickly. As the demand for part-time labour grows and there are more job options available, the Indian enlistment market has expanded significantly over the past half-decade. Additionally, as the gig economy grows, so does the enrollment industry, which is a better method for hiring people by reassigning the recruiting process to other businesses whose primary purpose is to provide the appropriate skill set required for the firm.
Analysis of different Machine Learning and Deep Learning Techniques for Malaria Parasite Detection

Malaria is an epizootic illness caused by unicellular parasites. In Two thousand eighteen there were an estimated two hundred twenty-eight million cases of malaria worldwide. Conventional method of diagnosis requires experienced technician and careful perusal to classify between healthy and infected blood cell, which consumes a lot of time and is also prone to human error. With the help of ML and DL we can simulate human intelligence and make better predictions. The main aim of the paper is to compare the machine learning algorithms namely KNN, Decision Tree, Logistic regression and Random forest and implementing transfer learning with deep learning models VGG19, modified Resnet50 to improve the accuracy achieved with machine learning models thus proposing the best model for predicting malaria only by observing by blood cell image rather than doing any staining of blood, thus reducing any expert requirement.
An Examination on Food, Clothes and Books Donation Based Android Application

For people, the three most fundamental and essential need are food, clothes, and training. Mankind's three most key needs should be fulfilled quickly. Despite the fact that there are many individuals in this day and age who can't manage or involve these basics for endurance. Then again, we see a ton of rich and instructed individuals squandering a ton of products like food, dress, and books. Our strategy effectively settles this problem. The fundamental goal of the application is to associate those with surplus assets and liabilities who are anxious to reward the local area with other people who are out of luck and able to acknowledge. With the assistance of our program, clients can uninhibitedly give things like food, attire, and books, and different clients can openly gather the merchandise on a case by case basis. The principal advantage of our program is that it joins clients who have gotten donations with the people who have made comparative donations accessible for thought. Food For Everything is a cell phone application made determined to stress food accessibility for the NGO and forestalling food squander.
A Survey on Machine Learning Techniques Using Quantum Computing

Over the course of the last seven decades the world of computing has taken revolutionary reformations in the scope of classical computing. Classical computers manipulate ones and zeroes to crunch through operations given to them by users. An innovative strategy known as quantum computing leverages the concepts of quantum physics to solve issues that are too complex for conventional computers to handle. Two of the domains of science that are now progressing the fastest are theoretical machine learning and quantum computing theory. In recent years, researchers have begun examining how classic machine learning techniques may be improved by quantum computing. Hybrid techniques that integrate conventional and quantum algorithms are part of quantum machine learning. Instead of using standard data, quantum techniques may be utilized to examine quantum states. However, quantum algorithms have the potential to greatly expand current data science techniques. In this paper we review the contribution carried out by various researchers in the field of Quantum Machine Learning and later we look at certain techniques associated with it for its use.
Intelligent Wardrobe Clothing Recommendation System Based on ZigBee Network and CLO Value Measurement

At present, smart home can improve people's production and life style. In order to intelligent wardrobe, this paper designs and develops an intelligent clothing recommendation system based on ZigBee network and CLO value algorithm, clothing thermal resistance meter and memory technology. This system can realize the identification and recommendation of clothing, recent record of user's wear and manufacture, improve user satisfaction.
An End to End Solution For Automated Hiring

Automation enables organizations to manage complicated workloads and processes with ease which increases efficiency and saves time. One such tool is automated hiring which accelerates the process by eliminating the requirement for the recruiter to be present in person. This study proposes an innovative approach that includes all steps of a standard interview with proper monitoring, providing the candidate with an experience similar to a true face-to-face interview while ensuring no cheating occurs. The resume short lister uses natural language processing (NLP) to rate resumes based on job requirements and stores candidate data in a database for future communication. The interview bot uses deepfake technology to provide the user with a realistic experience. Using similarity metrics, questions are asked based on data retrieved from the resume as well as user responses to prior questions. The software would finally analyze the data collected to determine the right choice for the position offered. The entire procedure is monitored by extracting information from the camera during the interview to prevent cheating, and the candidate is disqualified in case of any malpractice.
Survey on implementation of TinyML for real-time sign language recognition using smart gloves

Communication is a major part of life. For people who are deaf and dumb, sign language is imperative. All sign languages contain various hand movements and finger placement which in turn form different gestures. Learning any sign language can be challenging. With the current number of different sign languages available around the world, it makes it very hard for people to use sign languages for communication without the help of a translator. Out of all the sign languages available around the world American Sign Language is used by many. Engineers and researchers around the world have been working with different machine learning algorithms to build a better system which can either be only software or along with hardware. The smart gloves have so far been the most effective hardware with machine learning software developed till now as compared to a camera. These smart gloves can be developed using TinyML with the Convolutional Neural Network (CNN) algorithm for best accuracy with low footprint. This comprehensive literature review goes into the details of the different algorithms explored for sign language recognition, the existing systems, and the proposed system using Raspberry Pi Pico.
Charging Station for E-Vehicle Using Solar with IoT

In the recent years, the globe has changed dramatically. is moving towards the usage of electric vehicles which is more comfortable as well as more economical than the normal petrol/diesel operated vehicles. This study has created a battery monitoring system based on the internet of things (IoT) to track the functionality and performance of batteries in a smart E vehicle system. The main concern of the electric car is battery charging time, which consumes more demand from the supply network. An intelligent electronic device (IED), PV system, battery pack, grid connection, and electrical load are all components of this smart microgrid. The Internet of Things (IoT) established in this study consists of an IED communication channel, a data collection mechanism, a cloud system, and a human-machine interface (HMI). The main idea behind this work creates a charging station with solar and implement the IoT concept to monitor the performance and to cut the greenhouse gases and the usage of fossil fuels.
Hate Speech Detection using CSO based Polynomial Network using Twitter

The power of social media as a catalyst for societal transformation is now unrivalled. What happens in one part of the world has repercussions in other parts of the world. This is because the vast quantities of data produced by these platforms may be instantly disseminated to any part of the globe. To make cyber space as welcoming and productive as feasible for all users, developers of these platforms must overcome several obstacles. However, provocative speech and hate speech have emerged as major problems in recent years. The scale of this issue is so large that it cannot be solved by coordinated teamwork alone, no matter how hard people try. Actually, there is a need for the development of an automated approach that can identify and eliminate nasty and insulting remarks before they can do any damage. This paper offers a novel Deep Learning-based Hate Speech Detection Scheme (DL-HSDS) to identify hate speech in Twitter data. Even though there are a lot of HSDS methods available, many of them suffer from insufficient feature learning and poor dataset management, both of which negatively impact attack detection precision. Therefore, to improve detection accuracy, the suggested module integrates the Cuckoo Search Optimization algorithm (CSO) with the (SDPN); CSO picks the optimum features in the datasets, and SDPN categorises the data as hate or normal. The suggested model, which employs the tweet text with CSO to imprisonment the tweets' outperforms the previous models.
Analysis of Face Mask Detection through Machine Learning Techniques in Spread of COIVD-19

To distinguish individuals wearing face masks in observation settings like banks and ATMs, this work will give a profound learning model to face mask recognition. Hoodlums and offenders perpetrate wrongdoings by disguising their elements behind face masks, which is contrary to the standard in checking environmental factors. To recognize and secure offenders and lawbreakers, the face mask locator model set forth in this study can be joined with observation cameras in independent reconnaissance frameworks. The COVID-19 pandemic has in short order disturbed worldwide exchange and transportation, influencing our everyday lives. The act of utilizing a defensive face mask has changed. Coming soon from now on, a few public specialist co-ops will expect that clients utilize the legitimate masks while utilizing their administrations. Face mask ID is turning into a significant obligation to help the worldwide civilization. This paper frames a dense strategy for accomplishing this objective using specific essential AI instruments, including Tensor Stream, Keras, OpenCV, and Scikit-Learn. The proposed procedure effectively perceives the face in the picture and afterward decides if it is covered by a mask.
Recursive Feature Elimination based Multi-variate Naïve Bayes Classification for Product Recommendation

Today's most important E-commerce apps are those that use the latest AI and machine learning technologies. Unlike humans, computers are unable to comprehend non-digital alternatives. Therefore, it is vital that consumers learn about things like food, clothing, and medicine. Human perceptions have certain limits, but expanding them is no easy feat. Because of the rapid development has quickly become a common way for people to make purchases and get content. Performing an efficient way to increase consumer happiness. Because of shifts in sequence length, textual order, and complex logic, imagining the true polarity of user evaluations remains a formidable challenge. It is recommended that SA work be broken down into four distinct phases: I data gathering (DC), (ii) pre-processing, (iii) features extraction (FE) or term weighting-(TW), (iv) feature-selection-(FS), and (v) polarity or sentiment classifications (SC). To begin, the data collection process begins with the Web Arguing Tool (WST) being used to scrape product reviews from E-commerce sites. The data acquired from the web scrape is then preprocessed. These cleaned data are further processed using Term Frequency-based inverse class frequency by passing them via TW and FS. The purpose of this study is to use two common Naive Bayes approaches—Classification—to determine if a review of a product is positive or negative. The optimal candidate feature subset is chosen by using a recursive feature elimination strategy on the data. The study also seeks to determine which of the two methods is more effective for the provided data. A ‘2’ yardstick dataset is used for comparing the proposed classifier to the current state of the art. Consequences show that, associated to state-of-the-art procedures, the suggested model provides the highest performance in SC
Monitoring and Facilitating Students Programming Skill Development using Robotic Process Automation (RPA) and Artificial Intelligence (AI)–A Case Study

Students are provided with an online training program Programming Skill Development. The Program conducts programming tests every day by which the students have to solve programming problems within the given time duration. Based on the solutions given by students, a daily report is generated by the online training program. Robotic Process Automation (RPA) can be effectively used to generate more reports (based on the above data) daily focused on students' attendance, problems solved and other details categorized as the students' mentors. As each mentor has about 20 to 25 students, the mentor can use the reports further to analyze the performance and individually mentor and guide the students. The paper describes an RPA- based reporting tool developed using UiPath and implemented successfully in the Department of Electronics and Communication (ECE) of R.M.K. Engineering College for about 540 students and 35 faculty members as mentors. Currently, further enhancements are planned in the tool to provide more useful reports based on Artificial Intelligence (AI) principles. Reports will include the students' cumulative past performance in the programming, areas of his/her strengths and weaknesses in the programming skills (like the type of problems solved: arithmetic, logical, etc., type of errors: logic or programming language-related, etc.). The RPA- based tool will also enable such analysis to be sent to each student's email automatically using a software bot so that he/she can plan for improvement.
Enhancing the Security of Aadhar Cards using Blockchain and Steganography

In this paper we propose a novel approach to enhance the security of Aadhar cards generated by the unique identification authority of India (UIDAI) using blockchain technology implemented with steganography. Here we use steganographic techniques to embed a certain immutable, encrypted secret image on the pre-existing Aadhar card template with the Aadhar data that is collected while first issuing the card to produce a new “Steg-Aadhar” (steganographic Aadhar) which can then be converted into an NFT which will be uploaded onto the IPFS for storage. On doing so, a unique identifier, content identifier (CID) is generated which can be used to verify the Aadhar card later on. This method will help tackle the issues of fake Aadhar cards and malicious people trying to impersonate Aadhar individuals thus ensuring the smooth implementation of banking and government welfare schemes for the masses.
IPFS Enabled Robust Mechanism for File Storage and Retrieval Using Block Chain

In the current technology development scenario numerous data are handled by users for various purposes. Data become an important asset of the company. Data are utilized for various improvements of the organization full stop keeping the data in a secured way is crucial in current Internet world. Data are scrambled into various pieces in distributed computing systems for various reasons. In spite of distributed peer to peer storage and messaging systems such as ethereum swarm and ethereum Whisper and block chain Technology and efficient Framework is implemented. The storage need to be adaptive and effective during the centralized access. Comparing with the cloud based storage systems decentralized storage mechanism are economically good for all industries. Storing files in a decentralized system need robust security Framework such as block chain. The security of the file is ensured by making Complex encryption and decryption process. The proposed model considers these constraints an implemented and robust securing method using block chain Technology (RFSR). Here the files are stored and retrieved using interplanetary file systems (IPFS). Distributor application tap enabled secure computing system is implemented here.
Devanagari Characters Recognition using Deep Neural Networks

Use of machines for human functions like reading, is always a dream for humans. Over the last few decades, machine reading has become a reality. Optical Character Recognition (OCR) is the most successful technology in this field. Handwritten or printed text recognition by the machine is termed as Optical Character Recognition (OCR). The demand for automatic character recognition systems has been increasing day by day and it can be seen in various forms like printed text or typed text as well as the handwritten one. This information can be further reprocessed for various applications and can be embedded with visual clues to enhance its usability in various applications. The optical character recognition is one such way, which can extract the information from printed documents. However, there are various technical challenges in accessing the data that is much worn out, old or specific to an object. A system trained on a particular model cannot inter-operate with other data, as there is no uniformity. The font size to writing style are specific to a person. Therefore, it is imperative to find a model that is universally accepted. In this paper, the Convolution Neural Network (CNN) as a machine-learning tool to classify the Devanagari characters is proposed. The automation provided by the deep learning models is utilized to eliminate the feature extraction step. The experimentation is done with various architectures of CNN with varying depth and structures and comparison has been done with various state of art methods like VGG16, VGG19, InceptionV3, MobileNet, ResNet50 and Xception etc. using transfer learning of pre-trained weights. The training set and validation set accuracy are found to be 99.90% and 99.09% respectively, which shows the prowess of proposed model.
Recognition of Human Action using RCNN Technique

Action detection is still closelyrelated to the computer vision problems of pose estimation, frame tagging in movies,object identification, picture retrieval and video activity recognition. Recognizing someone's behavior or conduct from justone frame is the goal of this assignment. Formotionless photos, there are no spatial-temporal properties, which makes the issue more difficult. In contrast to action detection in movies, a similarly established field of study where they are utilized, stillimage action detection is a much more recent field. The current work only considers object-related activities. Complexactions can be dissected into their constituent elements thanks to semantics. Each of these factors' contributions to therecognition of actions are carefullyinvestigated.
Managing Reputation Scores in a Blockchain-based Decentralized Marketplace

The goal of a trust and reputation system is to provide buyers with a robust framework that allows them to select a seller based on accumulated evidence of the seller’s past behavior in the marketplace. The quality of such a trust and reputation system depends on the quality of the feedback provided by buyers. Being a subjective measure, the quality of buyer feedback is notoriously hard to assess. When feedback is provided by buyers from around the world, who may value different aspects of the same transaction differently, it is very hard to know when a buyer provides truthful feedback.In this work we assume a blockchain-based decentralized marketplace where a Smart Contract is associated with each transaction. In an attempt to reduce the uncertainty associated with buyer feedback, the novelty that we introduce in this work is that at the end of the transaction, the Smart Contract is responsible for providing feedback, replacing notoriously unreliable buyer feedback by a more objective assessment of how well the buyer and the seller have fulfilled their contractual obligations towards each other. While Smart Contracts are naturally associated with transactions, to the best of our knowledge, this is the first time Smart Contracts are empowered with providing transaction feedback.Our second main contribution is to show how to manage efficiently reputation scores in such a blockchain-based marketplace.
Last Mile of Blockchains: RPC and Node-as-a-service

While much research focuses on different methods to secure blockchain, information on the chain needs to be accessed by end-users to be useful. This position paper surveys different ways that end-users may access blockchains. We observe that between the two extremes of running a full node and fully utilizing a trusted third-party service, many solutions regarding light nodes are emerging. We analyze these solutions based on three basic properties of web communication: integrity, availability and privacy. We conclude that currently, the best way to access a blockchain while maintaining these three properties is still to run a full node. We consider it essential that future blockchain accessibility services should be built while considering these three expectations.
Automated Question Answering based on Improved TF-IDF and Cosine Similarity

This paper proposes an automated question answering system based on improved Term Frequency- Inverse Document Frequency (TF-IDF) and cosine similarity. The main purpose of this research study is to provide an effective question answering system that retrieves precise and relevant answers to the users' queries with high confidence. The existing studies in the relevant literature show that several techniques have been proposed for automatic question answering systems. The rule-based techniques depend on inference rules and take less time to respond to the user query. However, generations of pattern and inference rules are difficult as natural languages lack to follow a fixed pattern. The content-based similarity method pre-computes similarity with all the repository questions for a given query. In this research study, firstly all repository questions are pre-processed and a matrix using the improved TF -IDF model is generated. Then, we find the similarity of each user query with the matrix after query pre-processing. For the proposed approach, we remove stop-words and apply lemmatization and POS tagging techniques for pre-processing. The proposed framework is implemented using the standard datasets used in the existing studies. The empirical analysis-based results show that the systems adopting the proposed technique takes less than five seconds to respond to user queries with maximum similarity. The proposed framework attains up to 84% accuracy.