Reviewerly/exHarmony huggingface dataset:
Machine Learning Based Classification and Grading of Breast Cancer: Review

The classification of the excised breast cancer cells and the attribution of a score based on how similar they are to healthy cells are both done in the laboratory. The final grade provides an indication of the prognosis and aids in selecting the best treatments. To categorize and grade cancer, machine learning algorithms have been developed, allowing hospitals to offer the best care. Breast cancer has been diagnosed and graded using a variety of learning methods, perhaps slowing its progression. This review covers earlier research on relevant methods that has been published by academics.
Deep Learning for Detecting Malaria Parasites of Infected Red Blood Cells in Thin Blood Smear Images

Malaria is a significant global health issue, with 241 million people infected and resulting in 627,000 deaths in 2020, officially reported by the World Health organization (WHO). In addition, during the Covid-19 pandemic, 47,000 people died because of a reluctance to receive treatment. In Thailand, Malaria still spreads in distant communities where restrictions are in place for military deployments due to the high risk of infection. Therefore, the 8,000 or so military personnel who deploy on missions close to the country’s borders are actively monitored by the Armed Forces Research Institute of Medical Sciences (AFRIMS). The lack of medical personnel in these remote settlements, however, slows detection and adversely impacts the health and lives of military soldiers working in these locations. Because of their comparative effectiveness to traditional learning algorithms, deep learning technologies are used as a tool for medical screenings. In this study, the YOLOv3 and the DenseNetl21 are used to diagnose malaria infection using thin film blood smears. The results show that testing on normal slide datasets can distinguish between normal red blood cells and malaria-infected red blood cells in four species, including Falciparum, Vivax, Malariae, and Ovale, with accuracy for infection classification at 98.08%, sensitivity at 98.05%, and specificity at 99.73%. Furthermore, when the hard slide dataset is examined, the infection classification’s accuracy, sensitivity, and specificity are 98.48%, 90%, and 99.24%, respectively. In normal slide datasets, this detection method yields a positive hit rate for malaria-infected red blood cells and normal red blood cells of 98.05% for the former and 92.65% for the latter.
Virtual Fencing To Prevent Human Wild Conflict

The conflict between humans and animals grows old as animals roam the land of civilization, eating crops carefully planted by humans. Wild animals such as elephants and wild boars will perish once they enter the fields, so people must make sure farming is safe. Once their food is exhausted in the forest, animals will gradually enter lands ordered by humans. So this has an impact on human-wildlife interventions. Keeping animals off the field is easy enough, but majority of the people will not consider safety, they immediately choose the easier option, even if it harms the animals. The virtual fence mentioned in this project does not make life. Various techniques are used to detect the presence of animals, including motion detection and image recognition. The process of entering and defining and using custom measures. It uses loud enough sound and high-intensity strobe light to warn everything around, threatening animals to advance.
VARSew Benchmarking: A Visual Action Recognition on Garment Sewing using Various Deep learning models

The method of analyzing human action using computer and machine vision technologies is known as human action recognition (HAR). In recent years, research in this field has made substantial strides in a variety of application domains. However, HAR is still constrained in a number of fields because of the lack of sufficient, sizable training datasets. Many factors impact the availability of suitable HAR data in specific domains. Garment sewing is one of those interesting industrial fields. To demonstrate the growth of the global garment sewing business, several international reports were made public. As far as we know, HAR data for the garment sewing industry is limited. Thus, in this work, we benchmark and validate VARSew, a new dataset covering human garment sewing actions. VARSew is designed to capture multiple research challenges in HAR. We benchmark VARSew on two types of video classification tasks: binary and multi-class. VARSew contains 3,121 video segments of 49,936 frames. We conducted extensive benchmark experiments with 32 state-of-the-art HAR models and compare their performance. The results on both binary and multi-class reveal that the best accuracy scores are 80% and 95% for binary and multi-class classification, respectively. As a result, deep learning vision transformer (Vit) models outperformed deep learning convolutional and ResNet models on both the VARSew multi-class and VARSew binary-class datasets.
Supporting Class Infrastructure through Task Management Mobile Application during Pandemic

During the pandemic, most university students have difficulties interacting with lecturers to follow and update their project progress periodically.Procrastination is a problem that often occurs in this kind of circumstance.Developing the sustainable infrastructure that may be implemented for this situation needs further consideration, and several factors contribute.Digital technology scientifically can optimize or improve performance also interaction between lecturers and students.Utilizing mobile apps as tools to support the infrastructure of class activities can help break down their projects, and it also ensures the sequence stays in the proper order.Constructing tools for students using the app Task Management may improve their productivity in running tasks assigned by their lecturers.The methodology used is in-depth interviews (qualitative) with students and lecturers, supported by a literature review based on Design Thinking for educators by IDEO design by Tim Brown.The authors also analyze and visualize based on the data obtained from the research methodology.The results show that there is potential for both students and lecturers to use this app in their classrooms as they are likely to use it to manage their tasks and for lecturers to keep track of their student's progress and give feedback.
Dense D2C-Net: dense connection network for display-to-camera communications

In this paper, we introduce a novel Dense D2C-Net, an unobtrusive display-to-camera (D2C) communication scheme that embeds and extracts additional data via visual content through a deep convolutional neural network (DCNN). The encoding process of Dense D2C-Net establishes connections among all layers of the cover image, and fosters feature reuse to maintain the visual quality of the image. The Y channel is employed to embed binary data owing to its resilience against distortion from image compression and its lower sensitivity to color transformations. The encoder structure integrates hybrid layers that combine feature maps from the cover image and input binary data to efficiently hide the embedded data, while the addition of multiple noise layers effectively mitigates distortions caused by the optical wireless channel on the transmitted data. At the decoder, a series of 2D convolutional layers is used for extracting output binary data from the captured image. We conducted experiments in a real-world setting using a smartphone camera and a digital display, demonstrating superior performance from the proposed scheme compared to conventional DCNN-based D2C schemes across varying parameters such as transmission distance, capture angle, display brightness, and camera resolution.
PBSNet: pseudo bilateral segmentation network for real-time semantic segmentation

Achieving real-time performance while maintaining high accuracy in semantic segmentation can be a challenging task. Many existing methods adopt multi-branch architectures to extract both spatial and semantic information, resulting in increased computational complexity and a lack of communication between branches. We propose a pseudo bilateral segmentation network (PBSNet) that can extract rich spatial and semantic features from a single path, without incurring additional computational cost or time consumption. Our proposed PBSNet utilizes a semantic enhancement module to explore the relationship between high-level semantic features, an interchange module to enhance feature representation through bi-directional vertical propagation and adaptive spatial attention, and an attention fusion module to aggregate multi-scale features to produce the final segmentation prediction. Our results on the Cityscapes dataset demonstrate the superiority of PBSNet over state-of-the-art methods, achieving a balance of accuracy and efficiency with 74.52% mean intersection over union and 82.5 frames per second.
Theory and Application of Two (2) Iterative Imputation Approaches to Nigeria Annual Rainfall Data Reported

This research work is based on missing data statistics. Missing data occur where one or more of the observations in a dataset are completely not available. This work focuses on two (2) iterative imputation approaches. These are the Regression approach and the Expectation Maximization iterative imputation. These approaches were used to analyze the secondary data of the thirty-six (36) states in Nigeria on the rainfall data collected from the Annual Abstract of Statistics 2016. The evaluation criteria and comparison of these two approaches were done based on the error efficiency using the Raw Bias (RB), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and variance. The analysis of the result showed that the Expectation Maximization (EM) method was better for this specific data as reported in the Annual Abstract of Statistics 2016, compared to the other approaches. This was seen in the smaller errors values from the computed cases. It is therefore recommended that this approach should be used for obtaining missing data like other rainfall data in Nigeria. These two imputation approaches are good for making available missing data in observations.
Spatiotemporal Convolutions and Video Vision Transformers for Signer-Independent Sign Language Recognition

Sign language is a vital tool of communication for individuals who are deaf or hard of hearing. Sign language recognition (SLR) technology can assist in bridging the communication gap between deaf and hearing individuals. However, existing SLR systems are typically signer-dependent, requiring training data from the specific signer for accurate recognition. This presents a significant challenge for practical use, as collecting data from every possible signer is not feasible. This research focuses on developing a signer-independent isolated SLR system to address this challenge. The system implements two model variants on the signer-independent datasets: an R(2+ I)D spatiotemporal convolutional block and a Video Vision transformer. These models learn to extract features from raw sign language videos from the LSA64 dataset and classify signs without needing handcrafted features, explicit segmentation or pose estimation. Overall, the R(2+1)D model architecture significantly outperformed the ViViT architecture for signer-independent SLR on the LSA64 dataset. The R(2+1)D model achieved a near-perfect accuracy of 99.53% on the unseen test set, with the ViViT model yielding an accuracy of 72.19 %. Proving that spatiotemporal convolutions are effective at signer-independent SLR.
Blockchain Electoral Vote Counting Solutions: A Comparative Analysis of Methods, Constraints, and Approaches

Blockchain technology in electronic voting has emerged as an alternative to other electronic and paper-based voting systems to minimize inconsistencies and redundancies. However, past experiences indicate limited success due to scalability, speed, and privacy issues. This systematic literature review examines the methods, constraints, and approaches in the existing literature on blockchain-based electoral vote-counting solutions. A thorough search of pertinent databases was performed, and selected studies were assessed based on predefined inclusion and exclusion criteria. The review's findings reveal that most existing solutions employ smart contracts and various cryptographic algorithms to create secure and transparent voting systems. However, the study also pinpoints areas that require improvement, such as scalability, privacy, and accessibility. The review recommends exploring different combinations of blockchain platforms, cryptographic algorithms, and programming languages to develop secure and transparent voting systems. Additionally, future research could investigate the potential benefits and challenges of incorporating Internet of Things (IoT) devices, consensus mechanisms, and other technologies into the voting process. The review concludes that more research is needed to enhance the security and transparency of blockchain-based voting systems.
Multi-category rotated SAR ship generation with multi-input Generative Adversarial Networks

Deep learning-based Synthetic Aperture Radar (SAR) ship detection is a research hotspot at present due to its wide application in both military and civil fields. However, the lack of the labelled SAR ship data makes it a challenging task. To alleviate this problem, an improved multi-input Generative Adversarial Networks (MGAN) is proposed to generate the rotated SAR ship targets with its category and location labels. The proposed MGAN mainly consists of multi-input preprocessing, improved generator and self-supervised discriminator. To restrict the location, category and pixel value, the proposed MGAN takes the location, category and the pixel value constraints as input. In this way, plentiful SAR ship images with its rotated location and category labels can be obtained. Furthermore, we design category encoder and self-supervised constructor to improve the quality of the SAR ships generated by MGAN. The Intersection over Union (IoU) between the generated and the manual location labels reaches 0.83, which demonstrates the accuracy of the generated location label. The results of the experiment on classical oriented target detectors indicate that the SAR ships generated by MGAN can significantly improve the detection performance of oriented SAR ship targets.
Factors Affecting the use of Smartphones for Learning: A Proposed Model

Increased functionalities of smartphones, such as providing easy access to the internet, have offered multiple learning opportunities, especially in a world surrounded by unprecedented periods like COVID'19. Despite the benefits of smartphones mentioned above, academics still have significant concerns about the effective utilisation of these technological devices by students for learning purposes. This paper aims to examine the factors affecting the use of smartphones for learning. The study utilised a quantitative method to pursue its aim and objectives. Data were gathered from 80 academic staff members from five Departments under the Faculty of Accounting & Informatics. A stratified sampling approach was applied to ensure a more realistic and accurate estimation of the population had been used. After applying the above approach, a simple random sampling method was used for this population according to the number of academic staff members in the above-mentioned departments. The data were analysed to ensure reliability and validity, and descriptive statistics were applied, and correlations identified to develop the proposed model. The outcomes indicate that academic staff members believe that Attitudes towards Smartphones, Facilitating Conditions, Perceived Ease of Use, Perceived Usefulness, and Performance Expectations significantly impact the use of smartphones for learning. This study was limited to academic staff from five departments of a single faculty at a South African University of Technology.
A Secured Technique for Executing Queries over Multidimensional Encrypted Data

In new computing architectures, data is outsourced and sensitive data becomes vulnerable in various areas. Owners must guarantee that their data are secure from breaches and unauthorized users and available anytime. Various techniques have been proposed to maintain a certain degree of privacy in the database and to allow efficient execution of queries on cloud computing. These methods cannot securely support queries over encrypted data without leaking private data. Therefore, in this paper, a secure technique was proposed for executing queries over encrypted data, and the proposed technique was based on the design of CryptDB. This technique eliminates the limitations of CryptDB to perform server-side computations on cryptographic data of different principles. The proposed technique attempts to overcome CryptDB’s limitations by encrypting with different keys in multidimensional mode and running each query on the corresponding data encrypted by the same key.
Personalized Car Recommendations Using Knowledge-Based Methods

The process of selecting a new car can be a complex and challenging task due to the wide range of cars available in the market. To address this issue, this paper proposes a car recommendation system that employs a knowledge-based recommender approach. The system utilizes a variety of car features, including brand, color, year, gear type, number of seats, and price, to provide personalized recommendations for the user. Several models were implemented, such as item-item and user-user models. However, the knowledge-based model outperformed the other models, resolving the cold start problem and producing more accurate recommendations. Overall, the proposed system provides an effective and personalized solution to the car selection process, making it easier for users to find the cars that best match their preferences and needs. The system contributes to the field of recommender systems and could be applied to other domains where personalized recommendations are required.
Convolutional Neural Networks Based Classification of Segmented Breast Ultrasound Images – A Comparative Preliminary Study

Computer-aided diagnosis (CAD) helps physicians in tumors’ identification into various biomedical tissues, breast is one. Through the last few years, image classification techniques based on deep learning (DL), have obtained noticeable success in differentiating breast ultrasound images automatically. More researches, concerned with breast cancer detection, are representing the output as a black and white segmented image (mask) or a selected region on the breast ultrasound image which will in role submitted to physicians to help them in making a better diagnosis decision. Our paper represents the second step. In this paper: ten pre-trained Convolutional neural networks (CNNs) classification models (ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, GoogleNet, MobilenetV2, SqueezeNet, DenseNet201, and Xception) have been utilized to classify segmented breast ultrasound images by transfer learning (TL). A dataset of 375 breast ultrasound images' masks (ground truths), divided as 125 normal, 125 benign, and 125 malignant, has been utilized in training and validation. A dataset of 255 breast ultrasound images' ground truths (masks), divided as 85 normal, 85 benign, and 85 malignant, has been utilized to evaluate the classification accuracy of each CNN model after TL process. Each CNN model's evaluation (over the 255 breast ultrasound images' masks) has represented different accuracy values evaluated. The best accuracy value was for ResNet50 with an accuracy of 97.25 %. The proposed classification scheme for segmented ultrasound images can be regarded as a step that may help in an automatic breast cancer diagnosis system. The produced trained ten CNNs models in this study (input image size: 128 by 128 by 3) are not in their optimum case but it can be considered as a start to any researcher interests in studying the classification of segmented binary images of breast cancers, the ten produced CNNs models are available to researchers at: https://www.kaggle.com/mohammedtgadallah/ten-cnns-classify-segmented-breast-ultrasound.
Enhancing Textual Relatedness Assessment with Combined Transformers-Embedding Similarity Techniques and Machine Learning Regressors

In natural language processing (NLP), accurately calculating relatedness scores between pairs of sentences has significant implications for various tasks, such as information retrieval and document clustering. To improve relatedness score calculation, this study employed a two-layered approach. Firstly, embedding similarity techniques were utilized, leveraging seven different transformers to obtain vectors for each pair of sentences. Secondly, a classical machine learning regressor was trained on these seven vectors. The combination of these methods resulted in state-of-the-art performance on a standard dataset named SICK, with a mean square error of 0.0481, and Pearson’s and Spearman’s correlations of 0.978 and 0.9696, respectively. This research highlights the potential of combining embedding similarity techniques with machine learning methods to enhance relatedness score assessment and other NLP tasks, ultimately contributing to the advancement of NLP algorithms and their applications.
News Presenter skills evaluation using multi-modality and machine learning

Assessing television presenters is a challenging yet essential task, as it requires considering numerous characteristics for their evaluation. A multi-modal approach is employed, utilizing various data sources such as eye gaze, gestures, and facial expressions. Automation of this process is crucial due to the exhaustive nature of presenter evaluation, where assessors need to evaluate the presenter based on all the aforementioned features. This paper proposes a system that assesses the presenter based on four key features, namely posture, eye contact, facial expression, and voice. Each feature is assigned a weight, and the presenter receives a grade based on their performance on each feature. The present study focused on facial emotion, eye tracking, and physical posture. The presenter’s elbow, shoulder, and nose joints were extracted, and they served as inputs for classifiers that were divided into three categories: machine learning algorithms, template-based algorithms, and deep learning algorithms to classify the presenter’s posture. For the eye gaze distance algorithms such as Euclidean distance and Manhattan distance were employed to analyze eye gaze, while facial expression analysis was conducted using the DeepFace library. The system proposed in this research paper achieved an accuracy of 92% utilizing SVM in the machine learning algorithms, 75% using dollarpy in the distance algorithm, besides 79% utilizing BiLSTM for the deep learning model. The data set used in this study was collected from faculty of Mass communication, MSA University.
Dynamic Neighborhood-Based Grey Wolf Optimizer With Dimension Learning-Based Hunting And Lévy Flight

In this study, a modified variant of the Grey Wolf Optimizer (GWO) algorithm, integrating Lévy flight, Distance-Based Dynamic Neighborhood Strategy (DNDNS), and Dimension Learning-based Hunting (DLH) is proposed. This enhanced algorithm aims to address the inherent limitations of the traditional GWO, including its slow convergence rate, loss of population diversity, and predisposition towards local optima. The amalgamation of these strategies endows the proposed algorithm with improved exploration and exploitation capabilities, thereby bolstering its convergence towards global optima and streamlining the search process. A rigorous assessment carried out on a set of 23 commonly utilized benchmark functions evinces that the proposed variant of GWO consistently outperforms not only the just the traditional GWO but also other powerful modern optimization algorithms. The noteworthy performance of the enhanced GWO reinforces its potential as a promising solution for intricate optimization problems spanning diverse domains.