Reviewerly/exHarmony huggingface dataset:
Search Space Optimization for Autonomous Mobile Robots Using Meta-Heuristics

Optimizing search space for autonomous mobile robots is a critical problem that affects their ability to efficiently navigate and perform tasks in various environments. Considering the growing level of complexity of robotic systems, the area of search space for finding optimal solutions can be very large and time-consuming. Meta-heuristics are a class of algorithms designed for exploring large search spaces and locating near-optimal solutions. Their application is one of the strategies and approaches to addressing this challenge. This research paper presents metaheuristic techniques such as the firefly algorithm(FA), particle swarm optimization(PSO), and differential evolution(DE) in search space optimization of autonomous mobile robots. Also, it covers an overview of each algorithm, indicating its benefits and drawbacks and demonstrating how it potentially performs in varied search space environments. The study also looks at how various hyper-parameters influence the way each algorithm performs. The findings indicate that these factors significantly affect the algorithm’s functioning and the most appropriate values can be identified through a parameter-tuning technique. We also discuss some challenges and possibilities for future study in this area. Furthermore, for researchers and practitioners interested in using meta-heuristics to address search space optimization problems for robots or any other search space-related applications this study offers insightful guidance.
Blockchain oriented Spectrum Sharing in Intelligent Wireless Networks

By allowing wide-area gathering of information and transmission, intelligent wireless networks will prove advantageous to billions of terminal devices, machines, and humans. To support ultra-reliable low-latency communication (URLCC) and the usage of enhanced mobile broadband (eMBB), which is accessible through over-the-air (OTA) interfaces, it will be necessary to share spectrum resources. Due to these criteria, cellular service providers (CSPs) now have the option to proactively distribute their bandwidth across cross-network and on-network destinations. The terminal users are not yet a part of the centralized dynamic spectrum sharing (DSS) frameworks. Furthermore, since CSPs control these solutions, consumers have worries about the privacy and confidentiality of their terminal information. In this research, we provide a smart contract (SC) solution powered by blockchain that enables safe and DSS across terminal users. We provide a blockchain oriented multiple operators spectrum sharing (BoMOSS) SC that allows for the trustworthy, decentralized, and secure DSS among collaborating terminal users (such as driverless vehicles in smart cities). The suggested SC penalizes the terminal users who don't cooperate. We implement the suggested SC while taking into account the demands for permissionless blockchains, deploy it on the Ethereum network, and evaluate our suggested solution for gas consumption, security, and dependability.
HDR Reconstruction from LDR Scenes Using Basic Image Formation Procedure

High dynamic range (HDR) images are the one’s which includes all the objects from the original scene with correct exposure. The HDR content can be very helpful in analysis purposes as it possess all features of the scene as it is. Although it is useful the creation of HDR is very much expensive. Low dynamic range (LDR) images are less expensive but may include saturation regions leads to the absence of some content. For this reason there is a necessity to think of an alternative. The reconstruction of HDR from LDR content can be the alternative solution for the problem. This process can be done using different methods.The studies mainly represents different kinds of machine learning techniques. The methods include machine learning , deep learning , different kinds of CNN architectures specially designed for particular purposes , GAN models , reverse tone mapping operators etc. This survey also analyses evaluation techniques for assessing quality of images and techniques like image declipping, tone mapping operator etc. Some of the analyzed methods also deals with reconstruction of HDR videos from LDR videos rather than from raw images.Comparison in the survey reveals accuracy of different methods.
OCR based Tapal (Postal Service) System

Tapal are letters that are sent and received for official purposes. They are usually used between various government agencies or among higher authorities. Currently, the entire procedure is carried out physically. A register contains the data linked to Tapal, such as sender, receiver, subject, recipient department, etc. Consequently, the activity becomes tiresome and challenging to handle as a result of manual processing. The information cannot be immediately accessed. Therefore, this process may be digitized to simplify the administration of registers and can also be utilized for data analysis. The texts on the covers of received letters must first be scanned using OCR (Optical Character Recognition) technology for this automated system. Additionally, the automated system will improve data analysis by making the information easily accessible for further analysis. Also, the automated system will improve the speed of processing Tapal letters, ensuring that they are handled in a timely and efficient manner. Finally, by implementing an automated system, government agencies can streamline the process of handling Tapal letters, making it faster, more accurate, and more efficient, leading to better decision-making, improved communication, and more effective governance.
Decentralised Application for Health Insurance management using Blockchain

Insurance fraud is a significant problem in the healthcare industry, with various types of fraudulent activities leading to significant financial losses for insurance providers and higher premiums for customers. Examples of insurance fraud in the healthcare industry include billing for services not rendered, falsifying medical records, and phantom billing. Centralized databases and weak security measures have made it challenging to detect and prevent fraudulent activities in the insurance industry. Blockchain technology offers a potential solution to these challenges by providing a secure and decentralized platform that can enhance transparency and reduce the risk of fraud. This research paper suggests employing blockchain technology for identifying and stopping healthcare insurance fraud. Our proposal involves a system built on blockchain that facilitates the secure exchange of patient information among healthcare and insurance providers. The objective is to enhance the protection and confidentiality of patient data while minimizing the potential for fraudulent practices.
Hate Speech Detection for a low level language (Assamese)

Numerous social media issues like "Cyber conflict" have evolved as a result of the online community’s explosive expansion, and they can negatively impact both individual and group social interactions. Social media networks struggle to control all of their users, necessitating the necessity for an automated hate-speech classifier. We tried to categorize hate words gathered from social media platforms like Facebook, YouTube, and Twitter despite a number of obstacles, including the absence of Unicode, text corpora, and other NLP tools in the Assamese language. The algorithms used in this work to examine hate speech in Assamese are Random Forest and Linear SVC. Additionally, we examine current works that address hate speech and antisocial behavior in all Indian languages. Our results show that both algorithms perform well, with Random Forest surpassing Linear SVC by 3.33 percent and an accuracy rate of 88.33 percent. We compared our suggested techniques to current studies in other Indian languages using both lemmatized and non-lemmatized versions of words, as there has been no study done in the Assamese language for Hate-Speech identification.
Smart Attendance and Attention Monitoring System

Traditionally schools and colleges have been recording and maintaining the attendance of students manually. This task is often burdensome and time-consuming. It lacks efficiency and is susceptible to human errors as well as proxy attendance. Considering recently growing technology, face recognition has developed and has gained application extensively. This paper proposes a Smart Attendance System which makes use of face recognition technology to record attendance of students in schools and colleges. The proposed model performs face detection, recognizes faces from a training set of student faces, and records the attendance of students who are present in the class. Further to this, understanding students’ involvement and attentiveness to the lecture may prove to be fundamental to understanding the effectiveness of a lecture. The system uses this information to track student attentiveness to lectures via classroom cameras, giving insights into the effectiveness and productivity of lectures. YOLO (You Only Look Once) algorithm is employed to detect the students. The Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR) and Gaze Angle are the parameters used to find attentiveness of the student in the class. The proposed model aims to improve attendance management and enhance the learning experience of students by providing real-time insights into their engagement levels.
Person Image Generation To Enhance The Input Training Image For Re-Identification

The process of recognizing a person from photos or videos taken with a different camera is called re-identification (reID). In today’s world, the widespread use of extensive networks of CCTV and security cameras makes it difficult for a human operator to trace a person. This is where re-identification technology comes into play. However, person re-identification in real-world applications is very challenging due to the size of the existing dataset, the lack of human motions, and the inability to supply enough training samples. In this paper, proposed a method for person image generation using Generative Adversarial Networks (GANs). GANs allow us to build additional person images that look as realistic as possible.
A Robust Approach for Malaria Parasite Identification with CNN Based Feature Extraction and Classification using SVM

Malaria is a fatal illness caused by the transmission of the Plasmodium parasite through Anopheles mosquitoes. It causes symptoms like fever, chill, headache, and fatigue. In severe cases, it also leads to organ failure and death. The conventional method of detecting malaria is through microscopy. The development of new tools and algorithms for malaria diagnosis and treatment is crucial. In this study, a CNN-based model has been proposed for the feature extraction from raw thin blood smear images. The extracted features are then classified into one of the four species types of namely Falciparum, Vivax, Ovale and Malariae using an SVM classifier. The proposed model achieves a significantly good accuracy of 98%, demonstrating the effectiveness of deep learning techniques in Malaria diagnosis and species classification. This study has significant implications for the development of new tools for Malaria diagnosis and treatment, which could help to speed up the process of detection and help the medical practitioner make an accurate analysis of infection, particularly in rural areas, where medical facilities are scarce.
Eye-COG: Eye Tracking-Based Deep Learning Model for the Detection of Cognitive Impairments in College Students

It used to be quite challenging to diagnose cognitive impairment simply by visiting a geriatrician. Diagnosing cognitive impairments, particularly in the age group of 18 to 22, has become increasingly important as the prevalence of these conditions continues to rise. Traditional diagnostic methods were often time-consuming and expensive, hindering timely intervention. To address this, we propose a model EYE - COG: utilizing eye-tracking technology to detect cognitive impairments by monitoring real-time eye movement during a Trail Making Test. Our model leverages various machine learning and deep learning techniques to analyze features fixation, saccade, and blink. By employing CNN model, different stages of cognitive impairment can be accurately classified. Model EYE- COG, utilizing early stopping with optimization, achieves an impressive accuracy of 83%
Live Memory Forensics Integrated Reinforcement Learning Model for Optimized Demand Forecasting in Autonomous Supply Chain Management System

Today's highly interconnected global world economy operates in a highly agile manner. Smooth and timely coordination amongst the suppliers and manufacturers ensures a seamless supply chain which serves as the backbone for modern day businesses. Unforeseen demands and slightest fluctuations in demand/supply may lead to a highly unpredictable environment thereby leading to optimized demand forecasting causing a considerable loss for stakeholders throughout the supply chain. Reinforcement learning can help in dynamically adjusting the demand and supply across global value chains leading to optimized delivery efficiency. Moreover, cyberattacks in industrial framework may also lead to considerable losses. In this work, we propose a reinforcement learning based model for optimizing demand forecasting in autonomous supply chain management systems. The proposed model integrates live memory forensics module to enhance the security of autonomous supply chain management systems and to provide an ability to track the alterations made by various actors across the autonomous supply chain management system.
A Comparative Analysis of Deep Learning Based Human Action Recognition Algorithms

Human Action Recognition (HAR) is a crucial and prominent area in computer vision. It recognizes and predicts human actions from videos and has an ample range of applications, such as surveillance, healthcare, sports analytics, virtual reality, gaming, video searching, and human-computer interaction. However, HAR in videos is associated with several challenges, such as occlusions, viewpoint variation, cluttered backgrounds, camera motion, and execution rate. With the aim of recognizing and predicting diverse actions in videos, numerous models have been developed over the years to address the associated challenges. Deep learning techniques have exhibited significant potential in this regard. Researchers have used datasets such as single viewpoint, multi-viewpoint, and RGB-depth videos to explore HAR using deep learning techniques. This paper surveys the deep learning-based human action recognition algorithms on different datasets, highlighting recent advancements and the growing demand for HAR in videos. Furthermore, we present comparative performance evaluations on widely used benchmark datasets for HAR.
Multilingual Chatbot for Indian Languages

Chatbots are user-friendly interfaces that emulate human dialogue. With the rise of technologies such as Artificial Intelligence (AI) and Natural Language Processing (NLP), chatbots have become an effective tool in most conversational applications of companies. India is a multiverse country that demands making the chatbot functional in different languages. We present an effective approach to building a multilingual chatbot in Indian languages for fixed-response questions. This technique omits the expensive machine translation task with a large run-time overhead. We implement the chatbot's functionality to answer the query in context by fine-tuning the transformer model to the downstream task of question-answering. The MuRIL BERT model provides the best results for correct response prediction among major multilingual BERT models.
Recommendation of Independent Music based on Sentiment Analysis

Recently, the use of social media platforms for recommendations has dramatically increased. Today, people frequently use social media websites like Facebook and Twitter, as well as other websites and apps for food and music reviews. User reviews of the products and services may be found on many of these social media websites. Extraction of the emotions from such online user reviews, comments, and other types of content lead to sentiment analysis. Various music apps analyse our music data using cutting-edge data science and machine learning techniques to produce some really cool results. Spotify, for example, is one of the most popular apps and is powered by the music we listen to, the songs we like, and the playlists we create and follow to produce a personalized product for its users and community. Reviews and assessments are crucial in determining how satisfied users are with a particular entity. The polarity is then determined using these. This study discusses a method for sentiment analysis on music apps. To provide relevant results, the factors that led users to rate programs as 1, 5, or other values will be further examined.
Implementable Smart FIR Management

In the current scenario, the Indian policing system handles around 3 million cases per year involving a variety of interstate and personal issues. This paper aims to present an implementable and feasible First Information Report (FIR) management system with regard to the hierarchy and workings of the Indian policing system using blockchain technology. This will help to improve security, transparency, and information and intelligence sharing in the policing network. This blockchain network will allow users to file the report and officers to review it and take action. The aim here is to improve and evolve the efficiency of the policing system by demonstrating a Hyperledger Fabric network that is able to handle multiple organisations and networks, along with the usage of private data transaction facilities for critical cases like prosecutrix, child abuse, etc., in accordance with the policing structure of India. We will use the minifabric to demonstrate and evaluate the network with built-in tools like Hyperledger Explorer and Hyperledger Caliper.
Deep Learning Model to Improve Teaching-Learning Process using Sentiment Analysis

The integration of teacher capabilities and teaching quality has become a crucial concern in today’s Outcome-Based Education (OBE) in higher educational institutes. In addition, there is a requirement to utilize the vast amounts of textual qualitative data obtained through stakeholder’s input, primarily the responses to open-ended inquiries, which pose a significant problem to academics and institutions alike. The present research assesses instructors' interpersonal abilities using sentiment analysis techniques. We evaluated certain skills using aspect-based sentiment severity of the over 4,000 inputs left by Stakeholders for higher learning. The latest deep learning models and visualization frameworks were used to carry out the sentiment assessment system. According to the research presented, certain abilities are brought to light using aspect-driven sentiment analysis by the course’s structure, teacher’s teaching, and student’s learning. Additionally, the results show that stakeholders have an equally monolithic and converging perspective, supporting the legitimacy of their opinions and their significant involvement towards the tracking of the teaching environment generally concerning the ultimate aim of improving learning of the students.
A Comparative Study of Deep Learning Models for American SL Recognition

American SL (ASL) is a natural language employed by the deaf community in the United States as well as in some areas of Canada. ASL is a visual language that makes use of facial expressions, hand signs, and body language as means of communication, effectively conveying meaning to its users. In this research paper, we performed a comparative study of various deep learning approaches with the aim of accurately recognizing hand gestures in ASL. To conduct our comparative study, we created a comprehensive dataset of ASL hand gestures from A to Z, excluding J and Z, and trained each model on this dataset, it also included images from many sources with a range of backgrounds, lighting, and other environmental factors, making the dataset more resilient and adaptable. The models we evaluated included RNN, ConvNeXt, and Vision Transformer, as well as a computer vision-based approach. We assessed the accuracy of each model on each alphabet and the overall accuracy of each model across all alphabets. Our results shows that ConvNeXt achieve the highest overall accuracy of 99.670%, followed by RNN with 96.6664% accuracy and Vision Transformer with 95.2464% accuracy. These findings highlight the importance of using diverse deep-learning models and comprehensive datasets to accurately recognize ASL hand gestures.
AI based parameter estimation of ML model using Hybrid of Genetic Algorithm and Simulated Annealing

In this study, parameter estimation of ML models, here specifically, random forest classifier was conducted on a heart disease dataset. We have suggested a new approach to machine learning that hybridizes genetic algorithm with simulated annealing for estimating the hyperparameters of the random forest classifier. The workings of genetic algorithm are modeled after the process of natural selection to evolve a population of candidate solutions towards a better one and solve complex optimization problems effectively. Simulated Annealing is a probabilistic optimization technique, where the solution is iteratively improved by accepting moves that lead to a worse solution with a certain probability, this avoids getting stuck in local optima, and can handle optimization problems with many variables and noise. This application of hybrid optimization helped in increasing the accuracy of the machine learning model by 10% and is very promising in comparison to various different classification methods for this same problem.
Computer vision approach for motion blur image restoration system

This paper presents a method for removing motion blur using the Inception ResNetv2 architecture. The proposed method combines Laplacian-variance-based classification with Inception ResNetv2 model tuning to achieve excellent inaccuracy. First, the Laplacian variance of the image is calculated, and based on this value, the image is classified into high, medium or low blur categories using quartile classification. The Inception ResNetv2 model is then tuned using hyperparameters to produce a blurred image. The proposed method is assessed using reference materials and compared to cutting-edge methods. Our results shows that it executes well with an average PSNR of 39.84. The proposed method also shows the better visual quality of out-of-focus images with more accurate, detailed edges and textures. The proposed method provides a reliable and efficient solution to eliminate motion inaccuracy. The combination of Laplacian-variance-based classification and Inception ResNetv2 model tuning provides a robust and efficient framework for motion blur removal with promising results that can be applied to different image processing applications