Reviewerly/exHarmony huggingface dataset:
Real Time Face Mask Detection to Prevent Viral Respiratory Infection

The COVID-19 epidemic has interfered with people’s daily lives as well as with international commerce and shipping. The World Health Organization mandated protective clothing against the COVID-19 virus during the epidemic. Guarding one’s face with a mask is now expected. Customers of many different types of public service providers will be encouraged to dress appropriately for the future. On the other side, standing in one place and witnessing many people fail at the same time. To reduce the spread of the Corona virus across public networks, this project presents a deep learning solution for public-facing identity verification.The proposed work’s main contribution is the creation of a real-time system to detect the presence of a hidden camera. The detection rate is dramatically increased, and the success of excessive accuracy is simplified, thanks to the ensemble approach. The incorporation of transfer learning into the provided models and thorough goal testing on the dataset led to the creation of a somewhat dependable and inexpensive option. The results demonstrate the software’s readiness for widespread worldwide deployment to help curb the spread of pandemics. The suggested approach provides better accuracy, specificity, precision, recall, and estimate of the F degree within the three output categories than existing methods. Accuracy, specificity, precision, and memory are just examples of these indicators. There is a reasonable equilibrium between the number of available parameters, the time needed to finish each model, and the total time spent on all of them.
Parasite.ai – An Automated Parasitic Egg Detection Model from Microscopic Images of Fecal Smears using Deep Learning Techniques

Intestinal parasitic infestations are one of the leading reasons of morbidity worldwide and has been recognized as one of the most significant causes of diseases by World Health Organization (WHO). The mainstay of diagnosis is through direct manual examination of fecal smears using microscope in the laboratory. However, there are several shortcomings in the manual parasite egg detection like its time-consuming nature, low sensitivity and mandatory requirement of skilled technicians. Utilization of Artificial Intelligence in automation of the laboratory identification of parasite eggs has been proposed in this work. Various Deep Learning models like Yolov8, Detectron2, Inception v3 and YOLOs have been utilized for parasite egg detection task and their performance has been evaluated. Our results indicate that YOLOv8 with SGD optimizer has yielded superior performance, mAP of 0.92 and F1-score of 98%, compared to the other models evaluated and the existing literature. Its high accuracy and fast convergence make it an ideal choice for the parasitic egg detection tasks.
AES Algorithm for the Next Generation of 5G Network Encryption Standards

The Advanced Encryption Standard (AES) symmetric key encryption algorithm is the industry standard for 5G communication encryption. Technology advancements nowadays are mostly aimed at improving the security, speed, and efficiency of processors. The main expansion phase is made easier by the study’s pipeline structure. This method simplifies the process of creating subkeys in AES encryption for use in 5G communication networks. There is a 37% increase in throughput between the simulated and implemented versions of the original design on FPGA Virtex4 XC4VLX200, and this difference may be attributed to the reduction in propagation latency while producing subkeys.
Imaging Description Production by Means of Deeper Neural Networks

Natural language processing (NLP) and computer vision (CV) methods may be used to provide a textual interpretation and explanation of an image's meaning. The human brain can provide a detailed description of a picture, but can a computer do the same? Captioning images is considered difficult work in the realm of artificial intelligence. Contextualizing an image and converting it into grammatically correct text requires the use of both natural language processing and computer vision methods. Improved deep learning techniques and a wealth of publicly available datasets have made it possible to construct a variety of models for automatically generating picture descriptions. Picture classification based on the greatest number of items in the image is the first step in creating an acceptable description of an image provided as input. We can do this with the help of a neural network and certain NLP principles. In this study, we explain in depth how a "Convolutional Neural Network" and Long Short-Term Memory were used to build a visual description.Both pictures and texts may be classified with the help of "Convolutional Neural Networks" (or "CNNs") and "Recurrent Neural Networks" (or "RNNs") (RNNs). We trained the model to draw from a bigger lexical set when describing the images it has seen in order to increase the precision of its predictions. We conducted a number of trials across many picture datasets, and found that visual description is the single most important factor in determining a model's accuracy. In general, the outcome improves as the amount of the dataset grows.
System Design for Financial and Economic Monitoring Using Big Data Clustering

Economic data executives are becoming increasingly important for the longevity and improvement of ventures due to the constant expansion in the influence of data innovation. This study lays out an undertaking economic data the executive’s structure for the intricate internal undertaking economic data the board business. It also includes the application of web-based big data technology to understand the fairness, reliability, and security of system database calculations, mainly to improve office capabilities and solve daily project management problems. used in the project. The aim is to evaluate the suitability of transfer clustering computation (DCA) for managing large amounts of data in energy systems and the suitability of data economics dispatch methods for harnessing new energies. Then, combine day-ahead shipping plans with continuous shipping plans to create a multi-period, data-economic shipping model. Consider how the calculations are performed using a case study on the use of new energies. This will enable new energy in multi-period data economics shipping models while meeting his DR requirements on the customer side.
The Application of Machine Learning to the Task of Poverty Classification

For many societal and economic issues, poverty has become a stubborn underlying cause. The high rate of reproduction is a contributing factor to India's poverty. The government must implement a small number of effective programmes if poverty is to be reduced. Most of the time, a survey is conducted prior to the creation of policies, and only direct factors like income or consumption levels are considered. However, direct measures alone are insufficient for classifying households into poverty brackets. Therefore, we divide the poor into categories according to factors such as education, number of people in the home, housing conditions, and many more. (i) we extract a subset of features that are critical for identifying poverty; (ii)We investigate the effects of the extracted features on the category; and (iii)To choose the most reliable machine learning model, we utilize numerous models and evaluate their results. Instead of utilizing the traditional one-dimensional lens to study poverty levels, we employ the "Proxy Means Test" ("PMT") to look at different levels of poverty in a broader context.
Posture Similarity Index (PSI) for Ergonomics Using Joint Angle and Neural Network

Ergonomics is the efficiency underlining in the subjective environment. They help in leverage sound health. It is important to maintain the correct postures for safer, healthier, and more efficient result in the subjective environment. After the evolution of virtual reality aided by Artificial Intelligence, there is high preference among users towards remote learning. Tutoring and evaluating the postures in the fields like dance or yoga remotely requires measures to indicate the correctness of the learner. The proposed model helps in identifying this measure of posture of subjected person with that of the tutors and thus helps in automating the events. This leads to effective and remote evaluation of large observations with help of Image Processing. With a performance of around 0.77 as F1 score the object detection model shows promising results followed by the object tracking and skeleton extraction.
Detection and classification of tumor cells from bone x-ray imagery using SVM classifier with Naïve Bayes classifier

The primary objective of this research article is to employ detection and classification of tumour cells from bone x-ray imagery by utilising the Support Vector Machine (SVM) classifier in comparison with the Naive Bayes (NB) classifier. This comparison will be made between the two classification methods. Components and Techniques: The dataset that is used in this paper makes use of the database that is housed in the computer vision lab at National Tsing Hua University (NTHU), which is open to the public. The detection and classification of tumour cells using bone x-ray images required a sample size of 280 (Group 1 = 140 and Group 2 =140), and the calculation was carried out using G-power 0.8, with alpha and beta qualities of 0.05 and 0.2, and a confidence interval of 95%. The sample size was determined by the number of tumour cells in each of the two groups. The Support Vector Machine (SVM) classifier and the Naive Bayes (NB) classifier were used to perform the detection and classification of tumour cells extracted from bone x-ray images. The number of samples used for each classification method was ten. The results show that the accuracy rate of the Support Vector Machine (SVM) classifier is 95.9034 times higher than the accuracy rate of the Naive Bayes (NB) classifier, which is 92.0934 times higher. The significance level of the study is determined to be p = 0.021. When it comes to the detection and classification of tumour cells using bone x-ray images, the Support Vector Machine (SVM) classifier yields superior results in terms of its accuracy rate when compared to the Naive Bayes (NB) classifier.
Breast Cancer Tissue Identification Using Deep Learning in Mammogram Images

Breast cancer, the most common cancer in women, is best detected through screening programs. Mammography is the most common screening test, yet human error necessitates computer-assisted diagnosis. Convolutional networks, a machine learning technique, can assist detect breast masses and improve microcalcification identification in mammograms. Automatic breast cancer detection in mammography using convolutional networks has the potential to improve both the precision and timeliness of diagnosis, hence increasing survival rates. This method utilizes a single, easily-learned step of picture segmentation in order to detect breast masses and microcalcification clusters, both of which are strong indicators of breast cancer. The application of deep learning algorithms to the analysis of mammograms has the potential to greatly improve the diagnosis and treatment of breast cancer.
Theoretical and Methodological Foundations of Virtualization of Social Services

This article explains the definition of the term "virtuality." This article discusses the effects of computer-generated imagery and virtual reality on the contemporary information society using research-based data. The purpose of this section is to shed light on the origins of the concept of virtuality by providing some historical context. Today's views of social life, i.e. virtualized society, virtual realities, virtual images, and their impact on today's social processes are widely explained. The obstacles faced by today's youth when faced with virtual realities are discussed. The issue of the effect of the development of science and technology on the process of turning virtual images into reality on people's worldview was studied. It was deeply analyzed that virtualistics is a new philosophical direction, a new scientific paradigm. The article philosophically analyzes the concept of the combination of virtuality and real life to create a healthy social environment
Enhancing the Robustness of Deep Neural Networks using Deep Neural Rejection

Adversarial examples are inputs that have been intentionally generated in order to deceive deep neural networks (DNNs) into generating inaccurate predictions. These instances endanger the security and safety of DNNs in real-world applications. To solve this problem, we present a new defense against adversarial instances based on Deep Neural Rejection (DNR). The DNR approach involves training a secondary model, referred to as the rejector model, to identify and reject inputs that are unlikely to produce correct predictions. The rejector model can be trained using adversarial examples as well as benign samples to learn the difference between the two types of inputs. If an input is rejected by the rejector model, it is assumed to be adversarial, and the primary model will not make a prediction on it. The experimental findings show that the DNR technique improves DNN resilience against hostile cases while retaining excellent accuracy on benign samples. Furthermore, by lowering the amount of samples that must be processed, the DNR technique can minimize the computational cost of DNNs.
Whole Campaign Emulation with Reinforcement Learning for Cyber Test

Cyber-attacks pose existential, nation-level threats and directly challenge societal stability. The breadth of targets (small businesses to nation-states) and continuous nature of cyber-attacks make automated cyber test and evaluation (T&E) crucial to national security and domestic prosperity. Importantly, automation lowers the cost and increases the frequency of cyber T&E, thereby simultaneously increasing cyber test availability and coverage. Spurred by market demand as well as advancements in artificial intelligence (AI), automated approaches to penetration testing have seen a resurgence of interest in the academic literature. Yet to date, this burgeoning research community lacks a shared, long-term vision. Recently, we proposed a concept of whole campaign emulation (WCE) as a challenge problem and framework for automated penetration testing with reinforcement learning (RL) [1]. In this article, we review the state-of-the-art in RL-based automated penetration testing, assess its relation to WCE, and provide a case study using the open-source Network Attack Simulator (NASim) [2].
Deep Key Generation: End to End Encryption for Generic Communication

Steganography is the process of concealing a message's real nature by enclosing it in other information. Due to their ubiquitous use on the internet, digital photographs are the most often utilised sort of carrier document.There are various steganography techniques for hiding secret information within video frames, ranging from simple to complex, each with its own set of pros and cons. The message is concealed within the visual aspect of the frame. To increase security, this proposed system allows for specifying which bits to change rather than relying solely on LSB replacement. The sender chooses a cover image that contains the secret text or document and hides it within the image using the bit substitution method, resulting in a secure steganographic image. The image is then transmitted to the recipient through a personal or public communication network. The recipient receives the image and uses the decoder to extract the secret information
Utilising LSTM Based Network for Image Forgery Detection

Image Forgery refers to doing various kinds of modifications to the original image such that the actual meaning of it gets changed, due to which false information is spread everywhere. Moreover in this era where social media is at huge heights and images have become one of the most integral part of everyones life ,people tend to believe what they see and hence it’s a very serous concern which needs a solution. There are many techniques present to detect image forgeries .In our research we have shown two major techniques to detect forgeries using resampling techniques. As Resampling is a key indicator of altered images, two approaches for detecting and localising picture modifications, employing resampling (by forming different samples) varied integral mechanisms. First approach, the Radon convert of these features is computed on all those image parts which are overlapping to each other, then a heatmap generated using classifiers of deep learning appproach and also gaussian random variable ,which is model for conditional probability values . To locate tampered sections, the Random Walker segmentation approach is utilised. For categorisation and identification of local tampered regions, the second method includes transmitting resampling characteristics computed on intersecting picture patches through an LSTM-based network. We have compared both approaches detection/localization performance and it shows that both strategies are effective at detecting and localising image forgeries of all levels at a very efficient level, according to the results of our research.
An Innovative Method for Encrypting Pictures without a Key

In order to circumvent the constraints imposed by picture size and key records, as well as the prohibitive computing costs associated with key-oriented strategies, several researchers have turned to brute-force assaults. This study proposes a better keyless method of encrypting lossless RGB photos. For the purpose of picture encryption, two methods are in use: image splitting and multiple sharing. By scattering the pixel bit randomly over the picture, we hope to improve the degree of security. SST is also a great way to increase storage space. For this keyless method, we'll use reversible encryption to protect the original file without sacrificing quality.
HPC OpenStack Cloud with Optimized Software-Defined Storage

High Performance Computing (HPC) clouds offer researchers access to powerful computing resources with the ability to handle a wide range of workloads without compromising the performance. As these workloads are diverse in nature, the traditional Lustre parallel file system does not suit mostly for a scalable storage solution. Ceph, the software defined storage is a distributed storage system that is designed to provide scalable, highly available storage for a variety of use cases, including HPC. One of the challenges with using Ceph in HPC environments is that HPC workloads often have very high performance and bandwidth requirements, and Ceph's performance can depend heavily on the underlying hardware and network infrastructure. We propose an optimization method for HPC Cloud ecosystem with OpenStack and optimized storage deployment using Ceph software-defined storage as the best option for their needs and to offer an efficient persistent storage for both VMs and container workloads.
Improving High-Demand VDATS TPS Performance Through More Effective ATE Interface Design Using Example of AMR Application

In the current era of ubiquitous communication signals coming from various sources and tremendous proliferation of personal, commercial and military devices it becomes increasingly difficult to manage these systems and appropriately allocate the necessary resources. The concept of Internet of Things (IoT), or Internet of Everything (IoE), puts a great strain on the modern communications networks and increasingly complex and clever methods must be implemented to allow for constantly growing data rate, bandwidth, capacity and speed requirements. The newest 5th Generation (5G) New Radio communication systems may be one of the best examples.
Yolo Convolutional Neural Network Algorithm for Recognition of Indian Sign Language Gestures

People with hearing and speech impairments can communicate through sign language. It is a thorough and sophisticated language that makes use of hand gestures as well as other motions including body postures and facial emotions. Others who do not have speech or hearing impairments are less likely to be familiar with sign language because they do not care about those who are hard of hearing or who have trouble speaking. As a result, there is a communication gap between them. Around the world, sign language has different varieties, including American, Indian, and Chinese Sign Language. Additionally, there are other dialects of Indian Sign Language (ISL) that are frequently used in nearby nations including Pakistan, Bangladesh, Sri Lanka, and Nepal.These individuals benefit from professional sign language interpreters, yet they are limited. With the help of the YOLO (You Only Look Once) Convolutional Neural Network (CNN) based object identification system, which can be specially trained to recognize sign language gestures, we aim to balance this social inequity in our project. This offers a distinctive method for classifying gestures in Indian Sign Language. It is preferred over other Deep Learning models since it can produce quick and precise results regardless of background or attire. The network's design is decided by Darknet, which is used as the training foundation for YOLO. In recognizing sign language gestures in real time, this network can be modified and trained.
Implementation of Discrete Wavelet Transform for Image Compression

It presents discrete wavelet transform (DWT) for image compression. The image compression system was built or not or more or more or now, the special image will be reconstructed and the image is assembled. Redundancy that can be used to compress is pixel redundancy and reflection. The simulation penalty is for a strong contract in discrete wavelet transform (DWT) with a Scroll of corruption. This effect is also notorious and the degree of corruption will explode seen a close picture, but the level of agreement can also be high. An experimental shows the problem the Wavelet transform has been successfully used in the field of embedded, embedded and pure computing for the applied domain. Most of the research done on Wavelet transform has supported the static image and the computed summation. Modern multiplication has become a built-in alphabetic system of numerical abbreviations. Software performance decisions of clean software programs are repetitive, but, turned out to be as important as the usual global reduction in overall performance. real time series. overall performance improvement provided against the limitations of software program implementation. An example is given for building a software of Discrete Wavelet transform. The address is designed as an extension of the custom platform and can be used to speed up media such as JPEG2000.