Reviewerly/exHarmony huggingface dataset:
Evaluating Multi-Core Performance of Machine Learning Models Across Different Computing Environments

Machine learning algorithms are becoming more sophisticated with time, their computational capabilities and resource consumption are also increasing. And with its escalating potential, machine learning enthusiasts constantly look for better and easier ways to run complex algorithms. Often, laptops and CPUs available for personal use have memory ranging from 4/8/12 GB and have comparatively lower computation capability. In such restricted environments, there is a trade-off between the accuracy of machine learning models and resource consumption. This barrier can be overcome by using cloud CPUs offered popularly by Google Colab and Kaggle. These popular platforms provide CPUs capable of training and evaluating machine learning algorithms. In this paper, we illustrate the performance of the CPUs offered by Google Colab and Kaggle and compare it with a local device (ASUS FX505GE) using KNN, Logistic Regression, and Random Forest algorithm. We could find that multithreading provides better performance in the case of machine learning algorithms. Random Forest takes comparatively longer for evaluation and training purposes because of the numerous calculations involved in its different phases. Even though KNN has no training phase, the impact of multi-processing cores is noticeable both in training and evaluation. However, Logistic Regression has the best performance with only a single core both in the training and evaluation period because of the simplicity of the model structure and requires fewer computational resources.
Intelligent Sign Language Recognition Enhancing Accessibility for the Deaf Community

Every human being is unique in their own way while some suffer from disabilities like hearing and speech. It is unfair for individuals to encounter hardships when engaging in a fundamental activity such as having a conversation with others. It is commonly understood that having a conversation with people having such kinds of disabilities is a challenging task. Although various sign language standards exist, many individuals are reluctant to invest the additional effort required to learn these to facilitate effective communication with individuals with disabilities. This is where Sign language detection systems come in, they offer a solution to this problem by empowering individuals with disabilities to actively participate in conversations without necessitating knowledge of sign language. In this study, we propose a multilayer Convolutional Neural Network (CNN) model designed to identify ten different signs of American Sign Language (ASL). The model is trained and evaluated on a comprehensive dataset, achieving a high validation accuracy of 95%. The CNN architecture enables the accurate recognition and classification of hand gestures associated with ASL, empowering individuals with disabilities to effectively communicate with others.
Secured Medical Record Sharing Application Using Blockchain Technology

As a standard operating procedure of the healthcare industry, medical professionals always consult patients' medical histories before diagnosing and giving medications to them. Several organizations or clinics have their own database of every patient that visits them but it is observed that these organizations exchange patient medical records with businesses for research without patient approval. This threatens patient privacy. Using a centralized system to store these data might alleviate the problem, but when privacy is a concern, there are limited solutions. Thus, today's medical community needs a system to organize medical information and allow patients to securely share data with healthcare providers. This paper aims to develop a blockchain technology-based system that can store medical records on a distributed network. Two-way authentication utilizing the RSA-Encryption technique will prevent medical practitioners from tampering with or selling patients' data without consent, this chain of records will be delivered to them after verification.
Secure Watermarking Algorithm for Enhancing Invisibility and Robustness of Medical Images

In contemporary times, within a society that is highly advanced in terms of technology, all data is gathered and archived in a digital format. In the realm of medicine, diagnostic data is also captured and transmitted in a digital format. The paramount considerations when transmitting information through digital means are security and authenticity. Watermarking is a technological approach that guarantees the credibility and genuineness of clinical images, patient data, and diagnostic data.The present study introduces a secure watermarking algorithm that enhances both invisibility and robustness simultaneously. The transform domain scheme is utilized as a primary step to ascertain the suitable coefficient for embedding data. In order to provide a fair balance between robustness and invisibility, this research provides a secure watermarking method using hybrid transformation. Multiple attacks are also included to show the robustness of the images.
A Deep Learning Approach for Bengali News Headline Categorization

A huge amount of information and data is now available in seconds through the internet. Due to the availability of internet in the world, as the amount of online news is increasing on the one hand, people are interested in reading news from online news portals i.e., Facebook, Twitter, WhatsApp, Telegram, Instagram, blogs etc. Along with the increase in the amount of digital data in news portals, the number of readers is also increasing, thus the need for data classification for digital data is also increasing day by day. There are various methods of data classification, such as machine learning, deep learning, etc., as well as various data mining algorithms. Data is classified using these algorithms, so that people can make sense of the news just by reading the news headlines. Natural language processing methods are used to classify data in any language for such problems. In this paper, Bangla news is classified into 6 categories using deep learning algorithm. The categories are International, National, Sports, Entertainment, Politics and IT. In deep learning, BiLSTM and GRU algorithms have been used to classify. BiLSTM has an accuracy of 83.42% and GRU has an accuracy of 80.01%. BiLSTM has been found to have the highest accuracy among deep learning algorithms.
Smart Home Security System Using IoT

We are regularly dealing with security vulnerabilities on all fronts. So, we must use streamlined technology to address these problems. In this project, we'll be taking pictures of people and fingerprint impressions to compare with pictures and impressions from the database that have already been stored. Finding out who enters or leaves the property is the most crucial feature of any home security system. As a person's biometric traits, their distinctive faces and fingerprint impressions may be used to monitor that instead of using passwords or pins. We want to create a smart door that protects the entranceway based on who we are. By integrating facial detection and fingerprint impression identification, this project aims to assist users in enhancing the door security of critical locations. The essential components of the proposed system are face detection, face recognition, fingerprint sensor, and automatic door access control.
Deep Cascaded Broad Learning System with Sparsity based Framework for Image Denoising

Due to Technological advancements lots of images are captured, stored and transmitted every day. Contamination of noise while capturing or transmitting over channel is inevitable. Due to this quality of obtained image needs to be improved by reducing the noise, which has applications like enhancement of images captured using camera, X-Ray, CT imaging, realistic noise removal, etc. Original BLS has already proven its ability for classification and regression tasks. DCBLS has cascaded feature mappings and enhancement nodes which has better feature representation. Original DCBLS based Image denoising is parallel approach which learns weights using analytical pseudo-inverse-based approach and has closed-form solution with adaptive technique to update regularization parameter λ. In this paper, simplified Deep cascaded Broad Learning System (DCBLS) and Sparsity based DCBLS framework for image denoising have been presented. Simplified DCBLS is sequential and has fixed regularization parameter. Second way is to learn the weight matrix using dictionary based sparse representation algorithm, Orthogonal Matching Pursuit(OMP), from training set of patches. Experimental Results obtained over the datasets Set5, Set14 and BSD-500 are presented and compared in detail for AWGN, and Poisson Noise Removal. Proposed denoising methods are compared against state-of-the-art methods which shows need of improvement with possible future directions.
Performance improvement of ball balancer by using SMO Algorithm Over Hybrid membership Fuzzy technique,conventional fuzzy and Classical PI

The objective of this scholarly article is to provide a thorough examination of the efficacy of the Spider Monkey Optimisation Algorithm (SMOA) in comparison to alternative techniques. In contemporary times, Social Multi-objective Optimisation Algorithm (SMOA) has surfaced as a propitious methodology for enhancing intricate systems. This study assesses the benefits and drawbacks of SMOA and scrutinizes its recent progressions in diverse applications. Additionally, the authors present a new hybrid methodology that integrates SMOA with other optimization algorithms in order to tackle the difficulties presented by complex nonlinear systems.The study is centered on the utilization of SMOA in the context of mobile autonomous robots and ball balancer systems. The performance of SMOA is analyzed by the authors and is subsequently compared to traditional optimization methods. The findings indicate that SMOA exhibits superior performance in efficiency, accuracy, and convergence rate when compared to Hybrid membership fuzzy, conventional TriMF fuzzy, and PID. The experimental findings indicate that SMOA outperforms traditional algorithms in terms of optimizing the intended systems.
Streamlining Email Workflow: Empowering Users with Voice Recognition Technology and Website-Email Autometa Solutions

In today’s business-oriented society, email is used to send all official communications. Many institutions, colleges, and IT industries, in addition to the commercial sector, provide official information via email. Manually sending emails to everyone is an extremely time-consuming task. Therefore, a website that uses speech-to-text recognition to send emails and system talks to user about tasks done. The Email Autometa Solutions website is a Streamlit-based web app that allows users to automate email sending using voice commands. The website integrates voice recognition, email composition, and email sending functionalities to provide a streamlined and user-friendly experience. Users can input the email subject and message by speaking, and even attach files if needed. The website leverages libraries such as pyttsx3 for text-to-speech conversion and speech_recognition for speech-to-text conversion. Once the email is composed, it is sent via SMTP using the Gmail server. The website provides audio feedback to notify users about the success or failure of the email sending process. With its intuitive interface and voice-based input, the Email Autometa Solutions website simplifies and accelerates the email sending workflow.
Graph Based Image Segmentation by Dinic Algorithm

In Computer Vision objects are represented by images and analyzing labeling segmenting them is important for processing them. To solve this need image segmentation comes into the picture. There are numerous approaches to implementing image segmentation deep learning, graph-based, threshold-based, etc. Each has its pros and cons and is suited for the particular use case. In this paper, we will be looking at the graph-based approach which is an advanced one and fairly new in digital image processing.As of now, we have a lot of a number of methods present that can extract the required foreground from the background.But, most of these methods are solely based on the boundary or regional information which has limited the segmentation result. Thus graph cut based approaches have been proposed which has obtained a lot of popularity because it utilizes both boundary and regional information. Furthermore, graph cut based method is efficient and accepted throughout the world since it can achieve the globally optimal result for the energy function.Graph-based image segmentation approaches itself has implementation based upon the algorithm used like Ford Fulkerson, Edmond Karp, Dinic Algorithm, etc. We will be focusing on Dinic here as it is the fastest and most efficient among its peers and there is hardly any literature shedding light on it. We will look into comparative analysis, algorithm, and how it can be applied to image segmentation problems.
SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems

Recently proliferated semantic communications (SC) aim at effectively transmitting semantics conveyed by a source, and accurately interpreting the meaning at its destination. While such a paradigm holds the promise of making wireless communications more intelligent, it also suffers from severe semantic security issues - such as eavesdropping, privacy leaking, and spoofing - due to the open nature of wireless channels and the fragility of neural modules. Previous works focus more on the robustness of SC via offline adversarial training of a whole system, while online semantic protection - a more practical setting in the real world - is still largely under-explored. To this end, we present SemProtector, a unified framework that aims to secure an online SC system with three hot-pluggable semantic protection modules. Specifically, these modules are able to encrypt semantics to be transmitted by an encryption method, mitigate privacy risks from wireless channels by a perturbation mechanism, and calibrate distorted semantics at the destination by a semantic signature generation method. Our framework enables an existing online SC system to dynamically assemble the above three pluggable modules to meet customized semantic protection requirements, facilitating the practical deployment in real-world SC systems. Experiments on two public datasets show the effectiveness of our proposed SemProtector, offering some insights of how we reach the goal of secrecy, privacy, and integrity of an SC system. Finally, we discuss a few future directions for semantic protection.
Deep Learning Based Real-Time Indian Sign Language Recognition

for deaf and mute people to communicate with other people and with themselves, sign language is crucial. People frequently overlook the value of sign language, which serves as the sole means of communication for the deaf and mute cultures. Sign language interpreters are one of the services they use for communication. However, it is highly expensive to hire professional translators, thus finding an inexpensive solution to this problem is necessary. To interpret the visual data in textual information, we, therefore, suggested a method using MobileNet and LSTM-CNN model that recognizes alpha-numerical gestures and eight emergency words of Indian Sign Language in real-time.
Institutional Readiness to adopt Block Chain Technology in Higher Education

In the world of big data, information technology and privacy of data are facing many challenges, and blockchain technology is becoming a savior to them. Blockchain brings transparency, efficiency in working systems, and leveraging trust. The benefits of blockchain are multifaceted and applicable in the education industry. Blockchain enables companies dealing in education to secure, share and trade information, restricting control over learning even from remote places. This shift of technology in education institutes will surely bring potential transformation to the ecosystem. The present research attempts to understand the future challenges routing to education institutes while adopting blockchain technology. The research accepted a qualitative method to discover the challenges faced by the private education institutes in the Delhi-NCR region. Authors conducted in-depth interviews with Directors, deans, faculties, and IT staff members of reputed institutes and industries and adopted the Thematic analysis method to get an insight into the probable list of challenges. Results of the analysis recommended a framework encompassing five broader challenges in adopting blockchain technology. The theme extracted was more related to the institutions' operations, operations and regulatory environment, and technology
A Novel Approach for Classification of Metals and Plastics under the methodology of Deep Learning using Convolutional Neural Networks

A purely automated solution for the recycling procedure. In the course of recycling the problem comes up to light, the classification and segregation of materials. The waste consisting of both plastic and metal can easily be now categorized without any complication. The details will be set aside in the form of data and can be used on any occasion. This model will work on the neural network to classify the quality of the product. The model will provide ease in segregating the waste in the right way, this is fruitful for nature as the segregated waste can be disposed of easily and saves time and resources as well. The proposed model uses CNN for image classification under the domain of Deep Learning Neural Networks using Python.
RecipeMate: A Food Media Recommendation System Based on Regional Raw Ingredients

This research paper explores a recipe recommendation system that suggests recipes based on input ingredients, cuisine, and undesired ingredients. RecipiMate uses natural language processing alongside other machine learning techniques to pre-process and analyze a dataset with about 28K recipes. The dataset was cleaned and pre-processed by removing missing data, converting the ingredient lines to a list of ingredients, and lemmatizing the ingredients. The resulting dataset was then analyzed using cosine similarity to calculate pairwise similarities between all recipes. The resulting similarity matrix was then used to recommend the best recipes based on the input ingredients, cuisine, and excluding any unwanted ingredients which might be allergic to a person. The system was developed using Python and several libraries, including Flask, Pandas, NumPy, NLTK, and Scikit-learn. The resulting system was deployed on a Flask web application and manually tested. The results show that the system provides accurate recipe recommendations, improving the user experience of recipe search and discovery.
An Innovative Two-way Recommender System for Students and Teachers

A recommender system utilizes the user's preferences or past behavior as criteria to filter the dataset and generate predictions. The Innovative Two-way Recommender System for Students and Teachers, a novel approach, is specifically designed to cater to the needs of users by offering personalized predictions aligned with their preferences. Traditional recommender systems recommend items from one end like movies recommendation, food recommendations, etc. This research focuses on how a "Two-way" recommendation can be realized efficiently in some social spheres like student-teacher platforms. There is a difficulty with the one-way recommender system that applies the K-Means clustering algorithm on both sides of the student-teacher platform. In this research, a method for solving this issue is proposed by applying the K-Means clustering algorithm just once; however, in a novel way. Additionally, the K-Nearest Neighbor (KNN) algorithm is applied, and the quality of clustering is compared in terms of Mojo Distance.
Real Time Vehicle Tracking using YOLO Algorithm

In this research work, we aim to implement a real-time vehicle-detecting system using the YOLO algorithm. The system processes images and videos to identify the region of interest and detect vehicles using various techniques. The detected vehicles are then classified into different categories such as cars, trucks, and buses using DNN models. The system also tracks the movement of vehicles across consecutive frames of video to count the total number of vehicles passing through a given area. The final results are displayed in a graphical user interface, providing an easy-to-use interface for users. This research has potential applications in traffic management, surveillance, and security systems.
Study and Comparative Analysis of ChatGPT, GPT and DAll-E2

Natural Language Processing (NLP) has evolved greatly over the years, resulting in the introduction of sophisticated models such as GPT, ChatGPT, and Dall-E2 that employ deep learning approaches to evaluate and create humanlike replies to textual material. As a result, they have become indispensable tool for a variety of applications such as language translation, question answering, and text condensation. This research paper compares the aforementioned models. Secondly, the configuration of each model is explained, stressing its particular elements and discrepancies. Second, the study examines the training data which is further used for training the models, examining the dimensions and quality of the data. Lastly, the performance of each model is compared based on its capacity to create reactions similar to those of people. According to the findings, the ChatGPT, GPT, and Dall-E2 models have various strengths and drawbacks when it comes to creating human-like answers to text inputs. Yet, given the enormous quantity of conversational data required to train it, ChatGPT excels GPT and Dall-E2 in providing natural and realistic replies. Furthermore, ChatGPT's design is optimized for dialogue production, giving it an advantage over competing models.
Enhancing 3D Human Pose Estimation through Adversarial Learning and Graph Convolutional Networks

Estimating 3D human pose from a single colored image presents a significant challenge due to depth ambiguity. Existing techniques often treat joint locations independently, leading to potential overfitting and limited structural coherence. To address this, we propose a novel approach for 3D human pose estimation using Generative Adversarial Networks (GANs) that leverage adversarial training to learn realistic representations of the 3D human body. Our method involves regressing a 2D input to a 3D joint location using the generator, while the discriminator distinguishes between 3D ground truth samples and projected samples. To capture spatial relationships effectively, both the generator and discriminator employ Graph Convolutional Networks (GCNs). This integration of GCNs and GANs yields more accurate 3D joint location predictions and coherent human body designs. Our technique outperforms state-of-the-art methods on popular benchmark datasets, including Human3.6M and HumanEva-I, showcasing its efficacy in advancing 3D human pose estimation.
Video Based Fashion Recommendation System

The fashion industry has grown incredibly over the years giving rise to a number of varied styles. This growth in the variety has also led to a change in the choices of the customers. Customers nowadays are mostly inspired by the style statement of the characters of a movie or a web show. They want to recreate the same look for themselves but fall short of knowing the correct terminology to identify the style. Online shopping platforms deal with a lot of data, so for an efficient search, it is needed that we identify the proper keyword to initiate the search. A style comes into existence through the different combinations of the features of a dress. Thus through this paper, we explore how these features can be used in identifying a style. Further, we propose an approach to recommend clothes similar to a reference character in a video.