Reviewerly/exHarmony huggingface dataset:
Deduction of efficient algorithm to anatomize job insights

In this model, freshers, job seekers, as well as other professionals, are given the opportunity to know what is new in the market through a platform that enables them to keep up with the newest technology. We will be extracting data from job portals like Naukri, LinkedIn, etc., which analyzes and optimizes the data using machine learning algorithms. This optimized data will provide better insights and will be helpful in different ways. Beautiful Soup and Selenium web drivers are used to scrape and extract data from an open website. In order to extract the precise data, we are comparing the classification algorithms and choosing the most accurate one to implement. This will help in analyzing web data and providing better insights. We also added an additional section called Find-Jobs, which helps people to find jobs based on their location, experience, and designation.
Cloud Image Masking on Satellite Images Using U-Net Architecture of Deep Learning*

Satellite photos are commonly utilized for many activities such as surveys, map creation, and surveillance, and cloud detection can make using such high-quality images easier. Global yearly mean cloud cover is roughly 66 percent, according to the International Satellite Cloud Climatology. Clouds frequently obscure surfaces and objects, making it harder to extract usable information from photographs. 38-Cloud [1] is the dataset that is utilized in this investigation. In order to segment clouds in satellite images, we used only three input channels from the dataset: Red, Green, Blur, and NIR (near infrared). This prompted us to take an entirely different strategy and create our own U-Net from the ground up and not use the vision libraries for preparing the dataset. The implemented U-Net model is showing 96.59% accuracy. This paper also includes the recent art of work carried out in the Cloud detection application.
Textural Analysis for Breast Cancer from Feature Dataset using CNN

In practically every area of life, deep learning algorithms have accelerated the development of new technology. As a result, its application is also used in the field of medical imaging. One of the worst illnesses in the contemporary era can be treated earlier by creating a tool that detects breast cancer through textural analysis utilizing neural network-based deep learning. The automation of the textural analysis for the identification of malignant tumors is ensured by using CNN. The dataset utilized for the Deep Learning Model's comprehensive training is made available to the public by the University of Wisconsin and contains 569 occurrences
An Evaluation of Denoising Methods for Satellite Imagery: A Comparative Analysis

Satellite images often suffer from noise, which hampers data reliability and analysis accuracy. This study presents a comparative evaluation of denoising algorithms for satellite imagery, with a specific focus on a deep learning method called Deep Convolutional Neural Network (DnCNN). This study includes a comparison of spatial filters such as NLM, and weighted mean filters, frequency domain filters such as gaussian filters, wavelet transform-based filters, and the DnCNN model with an attention module. To assess the effectiveness of these techniques in denoising images, various evaluation metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE) are utilized. Moreover, a novel approach based on the DnCNN network is presented, leveraging multiple layers and attention modules to selectively enhance important image features while suppressing noise. The proposed methodology highlights the DnCNN network's ability to capture intricate details and improve denoising performance. Results demonstrate the superiority of deep learning-based algorithms over alternative methods. The integration of attention modules within the DnCNN model further enhances the denoising process by leveraging image correlations and extracting pertinent information. This study offers valuable findings on effective methods for reducing noise in satellite imagery, highlighting the potential of deep learning and attention mechanisms in denoising satellite images.
Immersive Viewing of Dento-facial Medical Data in Virtual Reality

Virtual reality (VR) requires dynamic and responsive rendering technologies to offer an immersive user experience. Failing, users may experience motion sickness due to motion lag in virtual space. This work aims to create a framework with a higher framerate to decrease the motion sickness in turn increasing the virtual world viewing quality. Essentially, an interactive environment is developed between existing volume rendering engines OpenGL and GPU-based ray casting techniques for rendering the dentofacial data volume. The developed visualization methods are incorporated into Head Mount Device(HMD) to offer immersive effects. The performance of the VR rendering application measured in terms of framerate was observed to be 125 frames per second, significantly higher than the current HMDs framerate. In addition, the adaptation of rendered image quality is activated by adjusting the sampling distance and jitter to complete the rendering process in the allotted time. The achieved result is expected to offer satisfactory comfort to the viewer with reduced motion sickness, particularly in pre-surgical planning for dentofacial surgery.
The Revolutionary Transformation of India's Banking Industry through Artificial Intelligence

Technology has radically altered our way of life. It has changed the way we think, learn, and communicate. Artificial intelligence, or AI, has revealed untapped potential in all economic areas. Banking today is much more advanced than the conventional brick-and-mortar branch banking we were used to up until recently. Customers waiting in line at branches, vouchers, stacks of paperwork, bustle, and confusion—all things that were formerly associated with any bank branch in India— have changed significantly due to the increased usage of technology in the Indian banking industry. Since the arrival of computers, the Indian banking industry has undergone a significant transformation. Today, we carry and manage our banks while having access to all financial activities in the palm of our hands. The existing AI mechanism is poised to become more dynamic, improving the customer experience in a variety of ways, with the addition of robotic process automation, natural and colloquial language processing, and advanced analytics. By improving chatbots for customer support, personalizing services, and even installing robots for self-service at their specialized Digital Banking Branches, banks are experimenting with and putting the technology to use in several ways. AI is increasing back-office productivity and lowering fraud and security threats. This paper investigates how artificial intelligence has been applied in the Indian banking sector. It also aims to examine how Indian banks can take advantage of the new opportunities and technological practices across the world, and deal with the difficulties of adjusting to the new AI culture.
Using Network Analysis to Detect Fake News in Social Media

Predicting patient health outcomes accurately is a key challenge in modern healthcare, significantly impacting treatment planning, patient management, and healthcare resource allocation. In this study, we propose an innovative approach for predicting patient health outcomes using federated machine learning (FedML), a novel machine learning technique that enables collaborative learning from decentralized data sources while ensuring privacy. This research advances current methodologies by enabling data analysis across diverse health institutions without explicit data sharing. The goal is to develop a robust model capable of capturing a wide spectrum of health factors impacting patient outcomes, thus potentially leading to more accurate predictions. Our approach integrates data from multiple healthcare institutions to train a global predictive model, iterating between local model training and global model aggregation until convergence. We also employ a privacy-preserving mechanism to ensure the sensitive health data remains within the original institution, alleviating privacy and data security concerns inherent in traditional centralized learning methods. We compared our federated learning method with traditional machine learning approaches, including deep learning, support vector machines, and random forests, using a set of diverse patient datasets. Results show our approach improves prediction performance, generalizability, and scalability while preserving data privacy. The study concludes by discussing the potential of federated learning in transforming predictive modeling in healthcare and addressing key challenges in implementing this approach. This research contributes to the body of knowledge in predictive health informatics and provides a roadmap for implementing federated machine learning in healthcare applications.
NoHateS: A Transformers-based Approach for Real-Time Hate Speech Detection in Spanish

Hate speech detection is a challenging task, especially in the context of real-time monitoring on the internet. Manual detection is both exhausting and impractical due to the high volume and frequency of online data. This paper proposes a system called NoHateS. This system is made of multiple components, the main one is BETO-CNN, a Transformers-based model trained on a Spanish corpus, which is designed to actually detect whether a text contains hate speech or not. The second component is developed to ensure accessibility. This includes an API to allow seamless integration of the model into various applications, and a Discord Bot developed for easy manipulation of the aforementioned API in order to help users detect hate speech in text channels. This paper also includes tests with imbalanced data and applies data augmentation in order to deal with it and make more robust models. The results demonstrate the effectiveness of NoHateS in detecting hate speech and provide recommendations for future research in this domain as it achieves 72.63% and 72.94% F1-score on the non-augmented and augmented dataset respectively.
Design Adaptability and Flexibility of Information Management System Framework for Graduate School Services

In the digital age, the transformation of a traditional organization into an electronic university has become widespread. One of the resolutions is an adapted information system for analyzing, designing, and developing software to support and manage the complex business logics, functions, and information of the smart university. The practical outcome of this article is to ensure that end users can access and use digital services anywhere and at any time during coronavirus outbreaks. This article aims to describe the information management architecture and design a graduate information management framework. The layered software management framework explains how to develop software-functional information systems and how to manage and implement them. Software functionaries enable the decomposition of software into components for the purpose of increasing software quality. The conclusion of this article is to integrate the graduate school information service and management in order to facilitate smart university processes. The system’s conceptual design can be applied in any department involving stakeholders in a smart university. They are the driving force behind defining system functionality and software scope, addressing concerns about software development and establishing the fundamental design principles for complex systems that allow for decomposition, adaptation, and flexibility.
An Algorithm for Image Transmission Using FM-RDS Communication Modules

This paper introduces a novel approach to multi-band image transmission using the FM-RDS standard. An algorithm has been designed and implemented to enable efficient data packet transmission within the payload of the standard. To accomplish this, coding techniques have been employed to ensure viable time ranges, especially for potential applications in emergency alert monitoring. The test results demonstrate that a 512 x 512 pixels RGB image can be transmitted in 109.06 seconds with a packet loss rate of less than 0.5%. In addition, a basic comparison was made with the LoRa link, revealing a better performance under the same experimental conditions.
An autonomous navigation system for an unmanned surface vehicle for plastic waste collection

This article proposes the design of an autonomous navigation system intended for Unmanned Surface Vehicles (USVs) in the form of catamarans with differential propulsion, whose primary function is the collection of plastic waste in aquatic environments such as seas and lakes. The fundamental goal of this system is to optimize the operational capabilities of the USVs by increasing their navigational autonomy, consequently reducing the costs associated with their operation and production. In this context, the vehicle has been mathematically modeled, and a navigation system composed of four essential components has been established. The first component involves heading control, which utilizes GPS and IMU to estimate the orientation of the USV. The second component is the obstacle avoidance system, which employs a lidar sensor. The third component is a collection system that relies on a computer vision model with convolutional neural networks to detect different types of plastics and manage the collection process. Finally, an architecture has been designed to facilitate the interconnection of all the aforementioned control systems.
Design and Development of a Gesture Recording System for Pakistan Sign Language

Lack of understanding of the Pakistan Sign Language (PSL) among individuals capable of speech poses significant challenges for deaf and mute individuals in Pakistan, impacting their daily communication and ability to secure employment for self-sufficiency. There has been limited research conducted on communication aids to bridge this gap. Therefore, the primary objective of this research is to introduce a gesture recording system that can be incorporated into a PSL recognition system. The gesture recording system is specifically designed to accommodate the mechanics of PSL, enabling the capture of dynamic, dual-handed, and asymmetrical gestures. To validate the effectiveness of this device, we conducted tests by recording and classifying nine gestures of PSL with an accuracy of 71.4%.
Anomaly Detection in Electric Powertrain System Software Behaviour

A software‐in‐loop (SIL) testing is a method of early testing of control software of a car in virtual environment. A system level testing is carried out on regular basis and it is important to see, if system is behaving as expected or unexpected. For unexpected behaviors, which test engineers not easily notice, modern techniques such as machine learning can give an advantage. This paper presents an application of machine learning algorithms that helps in identifying the abnormal patterns in time series data generated from electric powertrain system testing done in SIL environment for a Mercedes Benz Electric Car. Output of the SIL testing, results in time series data that is a collection of observations that are ordered chronologically and can be used to analyze trends, patterns, and changes over time. Anomaly detection in time series data is a process in machine learning that identifies data points, events, and observations that deviate from a dataset’s normal behavior. By monitoring the expected and unexpected behavior of the electric powertrain system, anomaly detection can be a valuable tool for identifying potential issues. This study aims at coming up with an efficient process for anomaly detection in SIL. In order to get this process, various anomaly detection techniques are compared to detect a defined anomaly in time series data. Data pre‐processing methods are also discussed before training the model. At the end, we conclude a best‐fit method for identified anomaly. With finally identified method, a model was trained and used further in application.
Verilog Implementation and Functional Verification of Hybrid Cryptography Algorithm

On today’s platforms, on-chip network communication has become important significance. The security of data has become more significant during the transmission of data between the peripherals. There might be a need of some encryption standards to prevent corruption of data. Data Encryption Standard(DES) and Advanced Encryption Standard(AES) are symmetric encryption and decryption algorithms.This work mainly focuses on understanding the working of both the algorithms and to design a hybrid cryptography algorithm by combining the features in both AES and DES algorithms. Dual Coupled Linear Congruential Generator(DCLCG) has been used for key generation process. After performing all the stages of encryption like key generation, substitution, permutation, key expansion, encryption rounds the cipher text is available. The secret key and the encrypted text is given as input for decryption and all the other process are done in reverse order. After the designing is completed, the design is functionally verified using the System Verilog in QuestaSim and achieved the coverage of 83.33%. Then certain parameters like timing, complexity, efficiency and area requirement has been calculated using the Vivado design suite and avalanche effect algorithm. It was found that proposed algorithm takes only 12 ns for encryption but conventional algorithm takes 18 ns. Then the area requirement is 116 LUTs, which has been increased because the keyed permutation takes more area as it has more number of S-Box permutation stages.
Correlation of BSIT Grades to the Employment Alignment in IT-Related Jobs Using Bayesian Algorithm

every year, there is significant growth in the Number of graduates molded by higher education institutions. the Number of graduates is increasing compared to the job openings in the market. Data mining is one of the numerous types of research from the dataset obtained from the Caraga state university cabadbaran campus. This study aimed to determine the correlation of BSIT grades to the employment alignment in it-related jobs using the bayesian algorithm of Caraga state university cabadbaran campus from 2015-to 2018. the study produces that the Bayesian algorithm gathered 177 graduates from 2015-201S. Only SS.5 or 50% answered that they are working related to their field specialization in information technology and also already permanent employees. 72 or 40.688% of student graduates responded that they are working but not aligned in their field specialization but are permanent employees. There are, 10.17% resigned and still finding a new job, and they are also graduate students taking care of their babies. The study would help the students and university to increase the various aspect of education the student and see to it that they would yield quality education in the succeeding years.
Symmetric Key Cryptography: Review, Algorithmic Insights, and Challenges in the Era of Quantum Computers

In an increasingly digital world, security has become paramount. Whether it's personal information, financial transactions, or sensitive communications, the need to safeguard data from unauthorized access and malicious activities has never been more critical. Security plays a vital role in protecting individuals, organizations, and even nations from potential threats and risks. In this context, cryptography emerges as a fundamental tool for ensuring security in digital systems. Cryptography involves the use of mathematical algorithms and techniques to encode information, making it unintelligible to unauthorized parties. To secure the information symmetric key cryptography algorithms and asymmetric key cryptography algorithms are implemented from last five decades. This review examines symmetric key cryptography algorithms and the challenges they face in the era of quantum computing. Symmetric key cryptography relies on the computational complexity of mathematical problems to secure sensitive information. This review aims to provide valuable insights and awareness to researchers and practitioners in the field of cryptography, enabling them to address these challenges effectively and maintain the security of sensitive information in the quantum era.
A Sentiment Analysis-based Intelligent System for Summarizing the Feedback of Educational Institutions

In the current digital age, we see that reviews from social networks or microblogging significantly influence one’s decision-making. It has been observed that a large number of reviews and comments regarding educational institutions are placed on these sites. As we all know, many new platforms have been formed over time, increasing the activity of users to voice their opinions on these platforms. As a result, users, particularly students, are more likely to become perplexed while trying to conclude. To address this issue, this paper looks at the analysis of a few platforms as a source of getting the reviews/comments of Universities, and with the help of sentiment analysis, we get an overview of a specific universities’ position in the market, which includes certain parameters such as quality of faculty, mess, infrastructure and so on. For this purpose, first, the reviews from known platforms are collected and then they are classified into a few categories like good, average, below average, and bad. At last, the results are shown in terms of an understandable report to the user, helping him/her to get into their dream university. In addition, this paper gives an outlook to further research in the context of an automated analysis of social media content to support the evaluation of universities.
Face Recognition From Video Using Enhanced Social Collie Optimization-based Deep Convolutional Neural Network

The face recognition system is a process which is part of computer vision as a key feature of video surveillance systems. In a face recognition system stands on two main pillar which are object recognition and authentication of object or person who present in a frame with the help of distinctive picture generation like i.e. Cameras. In this field of Computer Vision many researchers work on detection and verification of faces from frames in the subsequent part of video. It is a complex and complicated process to recognize accurate faces by the use of artificial vision algorithms. In machine learning main important concept is Convolutional neural networks (CNNs) in real time system which has video process input for face recognition systems. For such complex problem effective approaches to work on by solution as CNNs have excelled capability to handle, with significant results. Which also including process based voice recognition, face localization, face recognition and picture frame selection and categorization. Recently all the top performing techniques which work on the preprocessed Labelled Face dataset for the Real Time system for convolutional neural networks (CNNs).So here in this research use of deep convolutional neural network (optimized Deep CNN) to getting generated precise outcome of face images from the input video by the Hybrid weighted texture pattern descriptor (HWTP). The help of deep CNN techniques with improved tuned featured extraction and introduce novel ideas about enhanced social collie optimization (ESCO), which result in better solutions with different combinations of strategies. The finest parameters are used to recognise the face of an individual is determined. The proposed model by deep convolutional neural network achieves 91.30% accuracy, 95.07% precision, 95.078% recall and 95.078% f-measures.