Reviewerly/exHarmony huggingface dataset:
Approach to Predict Early Stage of Breast Cancer using Machine Learning

In the world, breast cancer is regarded as one of the main factors that cause death for females between the ages of 20 and 59. Early detection and treatment can enable patients to receive appropriate care, hence lowering the rate of breast cancer morbidity. In line with research, most experienced doctors can correctly identify cancer with 79% accuracy, whereas deep learning algorithms can do so with 91% accuracy. This research study analyzes the most recent deep learning-based breast cancer models detection and classification and present them through a comparative study. Additionally, to make it easier for any future experiments and comparisons, the datasets that are used and available to usage are listed in this research work. Models based on decision trees, random forests, logistic regression, and support vector machines are the most up-to-date, most accurate models used in machine learning techniques
Marketing Gamification in Metaverse Web 3.0 with Artificial Intelligence (AI)

Metaverse has attained global attention since its announcement, and several discussions and analyses have been actively going on in the current research for the past year. The author discussed the marketing frameworks through Gamification in the Metaverse World Wide Web 3.0. Metaverse attempts to bring human connections to the Virtual World with an immersive experience. The author discussed the emerging presence of Artificial Intelligence (AI) in the Metaverse, which could be changing the environment for user experience. Gamification offers different methods to engage the user types in environments where Marketing tries to attain and attract customers in Metaverse. With the growing expansion since the Information technology revolution, the World wide web has grown into a different version of Web 3.0, which is still evolving. The research discusses the newly imminent area in the Metaverse as Web 3.0 evolves with Artificial Intelligence.
Blockchain Technology Changing Landscape of Banking Industry

Digital banking and the transformation of banking system due to new technologies like Blockchain and Big data is the revolutionary advancement in Banking industry all over the world. Blockchain is the new buzz word which is expected to revolutionize the banking industry in India as well. The main purpose of this study is to investigate the application of Blockchain technology for improving and enhancing customer experience in banking industry in India. Secondary data has been used for this study. This study highlights the role of new technology especially Blockchain in redefining the Indian banking industry scenario. This study analyzes how Blockchain technology is going to change banking landscape for good.
A Comparative Analysis of Activation Function, Evaluating their Accuracy and Efficiency when Applied to Miscellaneous Datasets

Numerous deep learning architectures have been developed as a result of activation functions (AFs), which are crucial for allowing deep neural networks to deal with intricate real-world problems. In order to achieve cutting-edge performance, AFs play a crucial role by facilitating diverse computations between the hidden and output layers. This paper presents a comparison between various activation function like sigmoid, tanh, ReLU, Softmax on thedatasetMNIST, CIFAR-10 and IRIS and their accuracy on these datasets with minimum errors. These observations offer valuable insights into determining the most suitable activation function for diverse scenarios and datasets, thereby providing a comprehensive understanding of the optimal activation function for distinct situations.
MSTLA: Multi-Stage Transfer Learning Approach for Breast Carcinoma Diagnosis

In the recent era, Breast Carcinoma has been observed as a fatal disease in women. In India and other developing countries, survival rates are very low because detection occurs very late. Computer-aided diagnosis(CAD) has emerged as a tool for helping doctors and experts with early detection and diagnosis. Medical Image Processing relies upon processing medical images to segment and categorize several diseases. Machine Learning(ML) and Deep Learning(DL) have shown tremendous success in recent years in various real-life applications, including speech recognition, face recognition, autonomous vehicles, etc. Transfer Learning is a technique where features learned from one domain are transferred to another. This approach is generally followed when the data required for training a model is unavailable in abundance. In this work, we have designed a model for the early diagnosis and categorization of breast malignancy using the Multi-Stage Transfer Learning Approach(MSTLA). DenseNet169 and ResNetl52 are utilized for three stage transfer learning strategy. The results show that both DenseNet169 and ResNetl52 performed remarkably with an accuracy of 100% and 99% in the third stage of transfer learning.
Hybrid Model to Address Class Imbalance Problems in Software Defect Prediction using Advanced Computing Technique

One of the most well-known study areas in computer science is software defect prediction. It aims to find defects occurring from the method level code, so it can be used to better prioritize software quality assurance work. This research work has gone through different methods for predicting the software defect from the code, applied different class balancing techniques for imbalanced class data, and removed multi-collinearity from the data which helped in giving better results. The best trained model can predict the defect with 92% Accuracy, 96% Recall, 89% Precision, and a 0.92 ROC AUC Score.
Multipurpose Immersive Virtual Reality Environment

Immersive virtual environments offer a vast, un-tapped potential for facilitating various forms of learning and interaction, which could lead to significant improvements in learning outcomes. The proposed immersive environment enables a collaborative environment for people from across the globe to interact in a meaningful way, be it for education or business. Learning would be more interesting and students would be much more focused in such a virtual environment. Students would be able to inspect models up close altogether at once and would also have an environment where they could interact unlike online classes where there is little to no interactivity. Effective use of VR could also be the way forward for major multinational companies that has their presence felt across the globe and can now establish a common ground for people from multiple backgrounds to interact with one another. Achieving this involves creating a virtual environment with a central hub that connects to different rooms. The central hub will act as a control point for the rest of the rooms to be implemented.
Deep Learning Model based Face Mask Detection for Automated Mandation

SARS COVID19- virus has been a serious threat to human life, the best way to protect oneself is by wearing a face mask. As per World Health organization (WHO), wearing a mask can cut down the risk of infection by 85%. This study sought to offer an automated solution using several state-of-art Deep Convolution Neural Network (CNN) face recognition models to classify an individual’s face is wearing a mask or not. Determining the same and regulating the same is a difficult task that involves great human efforts. Nine various CNN models are trained, thereafter tested and found that the architecture of NasNet outperforms the other eight state-of-the-art models with respect to accuracy, on both, Real-World Masked Face Dataset (RMFD) and Simulated Masked Face Dataset (SMFD). The proposed approach achieved 100% testing accuracy on SMFD and 99.872% testing accuracy on RMFD.
Analysis Of Inflectional Behaviour In Indian Languages Using Features Extraction Techniques

Compared to English, Indian languages are highly inflectional in nature. Features extraction is a challenging task for Indian languages due to multiple reasons. First, alphabets called ‘Akshara’ are coded with Unicode unlike English, which is coded with ASCII. Second, upon inflection, the structure of the root word gets changed to a different format. Words get modified according to their added features as per tense, aspect, and modality. Thirdly, composite characters called ‘yuktAkshara’ are influenced with additive vowels or consonants. With these three prospects, this paper is aimed to address some practical difficulties for stemming root words with experimental reviews. Feature extraction is used in hidden information retrieval, root word stemming, text-to-speech conversion, and semantic analysis of Natural Language Processing. Analyzing features from an inflected word of Unicode language, one can recover the semantic and pragmatic meaning of the written text and further can be used in text-to-speech conversion. This paper discusses various techniques of feature extraction and its applications in some Indian languages.
Blockchain Application in Banking System: Saudi Arabian Perspective

Blockchain is an innovative technology in the age of digitalization capturing the attention of all sectors which allows value to be exchanged and transactions are completed digitally. It manages transactions through Internet via a decentralized distributed network. Financial sector is one the most important sector that needs to be converted quickly from the traditional way of the transaction to Blockchain technology. Banks are not quick to build new technologies because of their sensitive system of security attacks so, banks need to provide new technology to enhance their security and provide services that are appropriate to an interconnected world. One of the famous hypotheses regarding Blockchain is related to cash money, as there are many studies that assume that cash money will disappear in the near future, and depend on government digital currencies. This study draws its importance from the growing interest in Blockchain technology, according to Google analyses, the search for this technology from 2013 to date has increased by 1900%. This paper aims to highlight Blockchain and its assertions that the Saudi banking system needs to consider. Furthermore, it provides summaries of the challenges and opportunities of applying this technology in the financial domain. The study found that this technology is in its early stage of growth, it is too early to say what the Blockchain will exactly enable but absolute. Blockchain could be able to handle financial processes in a more efficient way than under the current system.
Supply Chain Strategies Supported By Blockchain Technology: Open Problems and Challenges

The most prevalent issue in the supply chain ecosystem is introducing transparency to the system. Another issue is fostering trust among various stakeholders to support successful process execution. Depending on the domain and the stakeholders, the supply chain process may change. Thus, solving the issue is challenging. Blockchain technology, sometimes referred to as distributed ledger technology, is beneficial in many ways in a variety of industries, including finance and healthcare. Blockchain based model can resolve the issues of traditional supply chain ecosystem. It creates a decentralized and transparent system where every decision is taken with the help of consensus of the associated stakeholders. This supports an immutable ledger so unauthorized modification is not supported. In this article, we address the issues that the supply chain ecosystem faces as well as the benefits of blockchain technology in resolving these issues. As well as some of the remaining challenges and opportunities, other aspects of blockchain technology are explored that will enable it flourish in future supply chains.
Identity-based Encryption to Implement Anti-Collusion Information Sharing Schemes in Cloud Computing

A comprehensive overview of Identity-Based Encryption (IBE) and its applications in cloud computing has been provided in this study. It covers the basic concept of IBE and its advantages over traditional public-key cryptography. Additionally, how IBE is used to secure data in cloud computing and its benefits, such as improved privacy and security are explained. The security issues in cloud computing, including data privacy and confidentiality, data security, compliance, malicious insiders, lack of control, and interception and eavesdropping, are highlighted. It is important to note that IBE can enhance data security in cloud computing. This study is a helpful tool for those interested in learning about Identity-Based Encryption and its applications in cloud computing.
Underwater image re-enhancement with blend of Simplest Colour Balance and Contrast Limited Adaptive Histogram Equalization Algorithm

A trial has been made for a technique for the enhancement and restoration of underwater images. To resolve this a fusion algorithm that consists of a simple color balance algorithm and Contrast Limited Adaptive Histogram Equalization (CLAHE) contrast enhancements algorithm is carried out. To increase the effect of R, G, and B values in the image’s color spectrum, we have used a simple color balance algorithm. This simple color balance algorithm observes the image’s brightest and darkest values and stretches as much as it can between maximum and minimum [0,255]. To increase the depths in the images we perform a contrast enhancement. The projected underwater image processing output is contrasted with a number of other current models. A comparison of our model shows an advantage over others in some results
Computer Vision &amp; Deep Learning based Realtime and Pre-Recorded Human Pose Estimation

This research study utilizes computer vision to estimate multi-person human pose from real-time and pre-recorded video. Computer vision examines human posture detection from RGB images. The proposed method works well for gesture control, gaming, human tracking, action detection, and action tracking. Tracking semantic important points is pose estimation. Two-dimensional human pose estimation predicts the spatial placement of key human body points from images and videos. Several anatomical areas use hand-crafted feature extraction methods to estimate two-dimensional human position. Visual input data and human body component locations are used to estimate human pose. OpenCV and Mediapipe detected 33 posture landmarks in our research. Estimating human body state requires modeling. Model-based methods are used to describe and infer human posture in 2D or 3D. This research uses the BlazePose GHUM 3D Pose Landmark Model for 2D human pose estimation. Output poses include x, y, z, and visibility. Image width and height are X and Y landmark coordinates. landmark depth Z Image shows landmark. Image or video resolution, the number of persons in the scene, and anomalies might impact visibility point accuracy, which was 0.969%.
Optimized Arithmetic and Logical Unit Design using Reversible Logic Gates

The modern world, Digital electronics systems are compact and faster. But, the major problem of these systems are power dissipation. The Power dissipation have different variants such as a static power, dynamic power, short circuit and leakage current dissipation. In VLSI Design, the power consumption plays an important role. In order to minimize the power dissipation there are many different low power methodologies are used such as a multi-Vth method, clock gating and reversible logic gate method. The major advantages of a circuit designing using a reversible logic gates will be compatible with an obtainable resources and the reversible Gates have a zero heat dissipation. The Arithmetic and Logical Unit is fundamental part of a computing systems. This paper, presents a Design of low garbage Reversible Arithmetic and logical unit design for computing system and the design includes Adder, subtractor and Multiplier blocks. The functionality of a design performance, trash outputs, Quantum cost are analysed. The proposed design has a 11 trash outputs and 57 quantum costs. The design is coded on Verilog HDL and synthesized, simulated by a Xilinx software.
Health Monitoring with Smartphone Sensors and Machine Learning Techniques

Over the past few decades, there has been a noticeable increase in life expectancy globally. However, this increase is anticipated to result in a significant rise in the aging population, in conjunction with a declining birth rate. Conventional approaches for estimating calorie expenditure necessitate the usage of wearable devices, such as smartwatches, pedometers, or smart bracelets, that track user activity continuously and estimate the amount of energy expended. In this work, accelerometer and gyroscope data from smartphones are utilized to provide a way to estimate calories burned while performing certain activities. Human activity recognition (HAR) techniques are employed to classify activities performed based on accelerometer and gyroscope data, and the resulting activity labels are used along with the MET (Metabolic Equivalent of Task) score of these activities and BMI (Body Mass Index) to estimate calories burned during a particular activity. HUman Activity Recognition models achieve an accuracy of about 94% with random forest and 97% with Convolutional Neural Networks.
Detection, Characterization, and Evaluation of Unidentified Anomalous Phenomena

The AIAA Unidentified Anomalous Phenomena (UAP) Integration and Outreach Committee (IOC) is working to improve aviation safety by enhancing scientific knowledge of, and mitigating barriers to, the study of UAP. The IOC is organized around three subcommittees chartered to perform outreach activities, address human factors issues, and support stakeholders with hardware factors analysis related to the detection, characterization, and evaluation of UAP. This paper provides an overview of the approach being implemented to further the understanding of objects of unknown origin operating in controlled airspaces.
Video Surveillance Device Failure Detection by IoT

The main objective of Video Surveillance is to traverse back for any untoward incident and to investigate for the actual cause or reason. In the current scenario, CCTVs (Closed Circuit Televisions) are placed all over for recording the events but it is very difficult to monitor the working of these CCTV (Closed Circuit Televisions) cameras in real time. It requires constant manual monitoring to realize if a camera is not working by looking at the recording screen. This research study proposes a novel method to find faulty cameras by analyzing whether a camera is connected to the internet network or not by using the ICMP (Internet Control Message Protocol) echo command method to a given server and its reply packets can be used to identify the faulty devices connected to a given network. The turnaround time for manual checking of faulty cameras and raising concern can be reduced drastically by saving more time and most importantly the video surveillance keeps tracking events all the time.
Detection of Cyberbullying on User Tweets and Wikipedia Text using Machine Learning

On the internet, cyberbullying is a widespread issue that affects both adults and teenagers. Tragedies like suicide and sadness have resulted. On social media platforms, content regulation is becoming increasingly important. Based on hate speech tweets from Twitter and personal assault comments from Wikipedia forums, an effective model for identifying cyberbullying in text data is created. Data integration is performed to construct both tweets and Wikipedia datasets from multiple data sources separately. Feature extraction is performed using BoW and TF-IDF. SVM, Logistic Regressor, and Naive Bayes classifiers performance are evaluated on the integrated dataset. Out of the three classifiers, the SVM model is around 98.73% accurate on Twitter data and 95.46 % accurate on Wikipedia data.
Low-cost angle of arrival-based auxiliary navigation system for UAV using signals of opportunity

Many modern devices rely on satellite navigation for reliable, precise and ubiquitous localization. While this enables technological advances that have been unthinkable a few decades ago, it also has clear implications on safety and resilience. There have been many real-world incidents where through deliberate or accidental interference, as well as simply by design, satellite navigation has been shown to be unreliable. Consequences of encountering interference can range from anywhere between minor inconveniences to catastrophic failure with loss of life. For unmanned aerial vehicles (UAVs), the consequences will often be significant economical damages or injury, but the risk is not high enough to justify expensive backup navigation systems that are rarely used. This paper investigates a sensor build on low-cost commercial off-the-shelf (COTS) hardware that can be used in costal areas if localized global navigation satellite systems (GNSS) interference occurs. This sensor works by measuring the incoming angles of radio signals sent by maritime vessels as part of the Automatic Identification System (AIS) and triangulating the receiver's position. The measurement precision of the hardware combination is evaluated from experiments with real-world signals. It is then shown in simulations that this achievable performance can lead to a significant improvement of the vehicle's state estimation compared to pure dead reckoning in GNSS-outage scenarios.
The Art and Algorithms for Segmentation of Ultrasound Images of Breast Cancer Using Artificial Life

Segmentation of tumours in ultrasound (US) images of the breast is a critical problem in medical imaging. Due to the poor quality of US images and varying specifications of the US machines, the segmentation and classification of the abnormalities presents difficulties even for trained radiologists. Nevertheless, the breast US(BUS) remains one of the most reliable and inexpensive tests for early detection of the breast cancer. Recent publications show that the automated systems help radiologists to increase the accuracy of the diagnosis. The paper offers an algorithm based on Artificial Life (AL) for breast tumour segmentation. The paper demonstrates that the AL agents are able to process complex-shaped tumours. The numerical experiments and tests against preceding models prove the advantage of the AL.
Intangible Cultural Heritage Based Augmented Reality Mobile Learning: Application Development and Heuristics Evaluation

The popularity of Mobile has led to the rapid development of mobile learning. Especially for applications in Intangible Cultural Heritage education, many mobile learning systems have been produced. However, few systems have been evaluated for usability. In this paper, we evaluated the Intangible Cultural Heritage Based Augmented Reality Mobile Learning Application developed, using heuristic evaluation conducted by five evaluators to examine 10 heuristics. The results show that the minimalist design is more accessible for users. Finally, we have the system for our users' testing. They all think that this system can increase their knowledge of Intangible Cultural Heritage.
Free Opensource Mobile GIS Applications for Land Survey

Geography location and object report is a key aspect in field survey, nevertheless of the spatial level. This research shown the request of land survey by using no code software. The study process collects data in the first part, mainly using forms and brief descriptions. Later on technologies are applied with increasing the availability of smartphone and improvement of features of the smartphones. Detection expose that smartphone GIS technique has a high adeptness, precision and consistency as a data collection technique for land survey development. Additional the study can be developed to use more advanced equipment.