Reviewerly/exHarmony huggingface dataset:
Cancer Sight: Illuminating the Hidden-Advancing Breast Cancer Detection with Machine Learning-Based Image Processing Techniques

Breast cancer is a significant global health concern, with early detection being critical for successful treatment and improved patient outcomes. In recent years, machine learning-based image processing techniques have emerged as powerful tools in the field of medical imaging, particularly in breast cancer detection and diagnosis. This research paper explores the application of machine learning algorithms to enhance the accuracy and efficiency of breast cancer detection using various medical imaging modalities, such as mammography, ultrasound, and magnetic resonance imaging (MRI). The study begins by reviewing the current state of breast cancer detection methodologies and highlighting their limitations. It then delves into the utilization of machine learning algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), and deep learning models, for the automated analysis and interpretation of breast cancer images. Various preprocessing steps, feature extraction techniques, and data augmentation methods are discussed to optimize the performance of these algorithms. Furthermore, the paper examines the integration of machine learning models with radiomics, genomics, and clinical data to create comprehensive breast cancer diagnostic systems. These integrated systems aim to provide more accurate risk assessment, personalized treatment recommendations, and improved patient management. The results of several case studies and clinical trials are presented to demonstrate the effectiveness of machine learning-based image processing techniques in breast cancer detection. These studies illustrate how these techniques can improve sensitivity, specificity, and overall diagnostic accuracy compared to traditional methods. This research underscores the promising role of machine learning-based image processing techniques in advancing breast cancer detection. It highlights the potential for early diagnosis and improved patient care, paving the way for more accessible and effective breast cancer screening programs in the future. Moreover, it emphasizes the need for continued research and collaboration among healthcare professionals, computer scientists, and data scientists to harness the full potential of these innovative technologies in the fight against breast cancer.
ViNavUT: Vison Based Navigation System for Unmanned Tractor in Airport

Airplane towing operations in the apron demand seamless cooperation of multiple operators, thus usually face several challenges including high manpower cost, poor safety and low efficiency. To address these problems, ViNavUT, a vision based navigation system for unmanned tow tractor has been proposed, where only one camera is leveraged to perceive environment and derive a low-price but robust autonomous navigation solution. ViNavUT consists of three main modules. Object detection and segmentation: a multi-object detection and tracking has been utilized to perceive the dynamic entities in the apron. And image segmentation is also used to achieve pixel level classification. Vision based object localization: benefiting from the segmentation results, a refined depth estimation method based on the pinhole camera model has been proposed to determine accurate localization of the objects for path planning. Adaptive navigation: motion predictions of surrounding objects are taken into consideration by path planning to realize a robust autonomous navigation adaptively. Extensive experiments have been conducted to verify its efficacy, and the experimental results prove that ViNavUT is a potentially robust and cost affordable navigation solution for autonomous apron towing operation.
Ensembled HAR Approach Using Stacking on Inertial Sensors Data

In the last few years, vast growth has been monitored in the Human Activity Recognition (HAR) domain which includes applications such as surveillance, healthcare monitoring, and smart home. The reason for this widespread growth is the scope of pocket-friendly and multi-modal data collection through already present devices such as smartphones, security cameras in home and public places, and Wi-fi devices at home as well as offices. The inclusion of deep learning (DL) techniques has made the task of activity identification simple and reduced the challenges like manual feature extraction, huge dataset handling, etc. In the proposed study we have proposed three DL models with convolutional neural networks and long short-term recurrent networks. We have further proposed an enhanced performance HAR model Ens-HAR with a stacked ensemble approach. To evaluate the proposed model's performance, we have used the UCI-HAR model which is publicly available for research. We have achieved an accuracy of 92.26%, 93.35%, 90.26%, and 94.79% on CNN, LSTM, CNN-LSTM, and Ens-HAR models respectively.
A Novel XG-Boost Approach for Electricity Load Balancing and Demand Prediction

Cloud computing quickly gains traction in IT because it simplifies computing without requiring physical hardware. Instead, cloud service providers host these services. These companies have many energy-consuming computers and servers, so their design and maintenance rely on a consistent, low-cost power supply. Clouds are voracious consumers of energy. Given recent increases in electricity costs, minimising data centre energy use is one of the primary challenges in planning and implementing appropriate data placement and node scheduling to offload or transfer storage. Present an Extreme Gradient Boosting (XG-Boost) model for unloading or moving storage, forecasting power prices, and lowering data centre energy costs. The proposed method outperforms conventional approaches significantly.
Enhancing Deep Neural Network Corruption Robustness using Evolutionary Algorithm

A robust image classifier can model a classification function well and has subliminal impacts on classification performance when the input data is corrupted in some ways. Many popular classifiers based on deep neural networks (DNN) are trained using back-propagation (BP) that explicitly computes the gradient of a loss function. However, the BP-trained DNN shows poor performance on diverse types of image corruption. In this work, we propose the use of an evolutionary algorithm, differential evolution (DE), to evolve a population of BP pre-trained DNNs and examine whether the evolution improves DNNs’ classification robustness against various types of corruption. Specifically, pre-trained MobileNet and ResNet on CIFAR-100 and ImageNet are evolved using DE. Despite no change in network architecture and loss function, DE empirically enhances DNNs’ corruption robustness, in terms of mean Corruption Error (mCE), in almost all types of corruptions in CIFAR-100-C (18 out of 18 corruptions with ResNet-18) and Imagenet-C (18 out of 19 corruptions with MobileNetV2, except for impulse noise). The preliminary results and findings have implications for refining the robustness of BP-pre-trained DNNs.
A systematic mapping of performance in distributed stream processing systems

Several software systems are built upon stream processing architectures to process large amounts of data in near real-time. Today’s distributed stream processing systems (DSPSs) spread the processing among multiple machines to provide scalable performance. However, high-performance and Quality of Service (QoS) in distributed stream processing are challenging to predict, achieve, and maintain. While many studies focus on evaluating or improving the performance of stream processing, getting a comprehensive view of the current state of DSPSs and their performance in real-world deployments is challenging. In this paper, we present a systematic mapping study of the literature on DSPSs’ performance. We discuss existing challenges, the most used DSPSs, achieved performance, and future trends. Our results demonstrate that performance is still one of the major concerns in stream processing, with several solutions available and different outcomes regarding the metrics, execution environments, and use cases considered. Moreover, there is a need for better benchmarks and workloads as well as for performance improvements by increasing efficiency and utilizing modern hardware. Our study intends to help software engineering practitioners and researchers to understand how to choose the most suitable DSPS to build efficient data-intensive architectures.
Artificial Intelligence based Approach for Virtual Character Recognition

Virtual teaching involves providing educational instruction and content to students using digital methods, predominantly over the internet. In virtual teaching, educators and learners engage in remote interactions, often from separate physical locations, utilizing a range of online tools and technologies. This approach can include real-time, synchronous sessions through video conferencing, as well as asynchronous learning, where students access pre-recorded lectures, assignments, and resources at their own convenience. Here, artificial intelligence techniques such as Convolutional Neural Network (CNN) and Improved Recurrent Neural Network (IRNN) are used for character recognition in virtual teaching. Initially, some virtual teaching videos are collected and it split into frames. Each frame is pre-processed by removing noise using auto-encoder. The de-noised images are given as input to CNN and IRNN for character recognition in virtual teaching videos. The experimental findings confirm that, for character recognition in virtual teaching, the IRNN demonstrates 5.88%, 4.6%, 4.65% and 5.78% better accuracy, precision, recall and F-measure compared to the CNN.
Robust Day and Night Object Detection Based on Heterogeneous Sensors and Information Fusion

Object detection and localization is now an important component in autonomous driving-related applications, in which the technology based on traditional RGB cameras has become increasingly mature. However, the detection ability of RGB cameras is greatly affected by lighting conditions, such as in a dim environment at night, the information available in RGB images may not be rich enough. We find that thermal infrared images and 3D point clouds from LiDAR can make up for the lack of light and capture more information missing from visible light images. Therefore, we propose a method to fuse RGB images, thermal images and 3D point clouds to facilitate accurate detection in both day and night. Experimental results show that this fusion method improves the detection performance.
A Multi-Population Genetic Algorithm for Multiobjective Recommendation System

Nowadays, recommendation systems (RSs) have been widely used in many real-world applications. However, traditional recommendation techniques mainly aim at improving recommendation accuracy, while other metrics to measure the performance of the RSs are not considered. In this paper, a multiobjective recommendation model that considers different metrics, including accuracy, diversity, and novelty of recommendations is established. Compared with recommendation models that only consider accuracy, this model can recommend more different items with higher diversity and more fresh items with higher novelty to enhance the long-term performance of RSs. Moreover, to efficiently solve this multiobjective recommendation model, a multi-population genetic algorithm (MPGA), which follows the multiple populations for multiple objectives (MPMO) framework, is proposed. As far as we know, it is the first time that the advanced MPMO framework is used in RSs. We conduct comparison experiments on three real-world datasets with three state-of-the-art multiobjective recommendation algorithms and two traditional multiobjective evolutionary algorithms. The experimental results indicate that the performance of MPGA is better than all the compared methods.
OmniScorer: Real-Time Shot Spot Analysis for Court View Basketball Videos

We propose a real-time shot spot analysis system specifically designed for basketball videos captured from a court view perspective, even in the presence of camera movements such as panning, zooming-in, and zooming-out. Our method consists of two stages: the first stage focuses on identifying the precise frame of the shot, while the second stage predicts the shot event category (i.e., 3-point shot, 2-point shot, or free-throw) and localizes the shot spot from a top-view perspective. Compared to existing end-to-end methods for shot event prediction, our method offers significant advantages. It effectively mitigates the overfitting problem and demonstrates superior performance in predicting 3-point shot and free-throw events. To the best of our knowledge, this work is the first real-time system capable of accurately localizing shot spots in basketball games captured by a moving camera with a court view.
A Bibliometric Analysis of Publication on Novel as Literacy Source

A novel is not always limited to long works of prose fiction (Hughes, 2002); a novel can inspire anyone in any field. This study employs a bibliometric analysis approach. The data used were 6.516 documents downloaded from scopus.com (see Figure 1). Microsoft Excel 2019 is used to analyze the average publication each year, while VOSviewer is used to create data visualizations and find the number of citations. The results show that the publication trend occurs in 2022 as many as 687. Then, the most researched research theme is “literature, novels, and identity”. In addition, there are several results of citation analysis based on searching the most influential documents, authors, organizations, sources, organizations and countries. It can be concluded that the development of novel publications is increasing to the present day. The researchers suggest a further research can be carried out on a comparative analysis of works of fiction and non-fiction.
Custom Hidden Markov Models for Effective Part-of-Speech Tagging

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain.
Extraction and Classification of Medicines from Handwritten Medical Prescriptions

In the modern world of rapid technological progress and digital transformation, industries are swiftly shifting from paper-based to digital systems. Document digitization, especially for image-based documents lacking machine readability, is gaining global attention. Sectors like real estate, finance, law, healthcare, and education are actively adopting digital strategies. In healthcare, where handwritten prescriptions lead to inefficiencies such as errors in medicine name by pharmacist due to doctor's hand writing lead to catastrophic failure and delays in various processes. Therefore, digital solutions are being developed to enhance patient care. This study proposes an approach to extract and categorize medicines from handwritten prescriptions, improving accuracy and communication with pharmacists. We proposed an approach that uses stroke enhancement apart from pre-processing with comprehensive multi-modal architecture, including a transformer based deep neural network and a sequence-to-sequence model such as DONUT and layoutLMv2 model. Our approach achieves a mean accuracy of about 0.85%. It surpasses models like LayoutLMv2 and DONUT by providing a balance in accuracy and consistency. Furthermore, the extracted features are passed to the various state of art machine learning classification algorithms upon which Gradient Boosting, Random forest and Ridge classifier produced the promising results for medicine type classification.
Neuromorphic Computing with the Plasmonic Microcavity for all Types of Logic Tasks

We propose a new passive-integrated photonic reservoir computer for processing analog signals and study it through detailed numerical simulations. The computer utilizes plasmonic microcavity and silicon arrays to achieve high- dimensional mapping and mixing of the input temporal signal. It can handle all types of sequential logic tasks after simple training. Our reservoir computer has a tiny footprint (60 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sup> 60 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and a high information processing speed (50 GHz). Our research offers a way for on-chip photonic computing with high- dimensional dynamics.
Multi-Target Classification Using Deep Learning Models for Automotive Applications

This paper presents a multi-perspective convolutional neural network (CNN) that extracts the class of objects supporting intelligent transportation systems. The proposed model is the visual geometry group (VGG) backbone network with custom feature extraction blocks, that use multilayer prediction heads. The model addresses both multi-class and multi-object classification tasks utilizing the automotive object detection dataset. The model is designed with multiple prediction heads to classify different objects in an image and enable object count prediction. A publicly available automotive object detection dataset with 19800 images and labels has been utilized. The dataset consists of five primary types of objects: Persons, Trucks, Motorbikes, Cars, and Cyclists. On the dataset, pre-trained models such as VGG, Resenet, EfficientNet, and DenseNet were tested and their classification performance was evaluated. The experimental results illustrate the superiority of the proposed VGG backbone deep learning CNN model in comparison to other pre-trained models.
Internet-of-Things Enabled Automation of E-Logistic Framework for Healthcare Sector in Smart Cities

Strong operations and advancement of a viable and feasible city coordinations framework are vital for building economical cities. Be that as it may, city coordinations confront viable tangles due to the incongruent benefits among various performing artists, and the vague portion or constrained support of the civic division. The concept of a supply chain includes innovations and ideas pointed at making cities additional viable, in fact progressed, ecologically friendly, and casually comprehensive, counting specialized, financial, and communal developments. The IoT empowers organizations to mechanize forms and make strides benefit conveyance through Web innovation and cloud-based information exchange. In this paper, we have effectively accomplished automation of e-logistics applications within the healthcare segment, coming about in fetched proficiency and higher accuracy rates. The objective of the paper is to make a show or gadget for following and overseeing the computerization of e-logistics within the healthcare segment, with diminished operational costs and greatest proficiency. This study improves our understanding of the require for mechanization of IoT-based e-logistics system for viable and proficient improvement of smart cities in healthcare segment and will serve as a premise for pending works/ investigate.
Polyp Detection in Colonoscopy Images Using a Vision Transformer Classifier

Colorectal cancer (CRC) can be detected analysing the presence of polyps, which are abnormal tissue growth with several shapes and sizes presented in colonoscopy images. Detectig polyps can help an early diagnostic for CRC, and contribute with the patient treatment, since all CRC cases originate from polyps. In this paper we propose a Transformer-based classifier to detect polyps in colonoscopy images. The databases addressed here is the CP-CHILD. Our approach presented promising results with an accuracy of 97.86% and with average precision values of 97.51% on the test set, as well as a recall of 91.87% and a F1 score of 94.56%.
Development of an Embedded System for Monitoring a Solar Power Plant in the Amazon Region

The present work deals with the development of an embedded Internet of Things (IoT) system for monitoring the temperature of a photovoltaic system at different points, to enable the analysis of the efficiency of the solar system based on the thermal effect, considering the characteristics climate in the Amazon region. The module created was built and installed in a photovoltaic solar plant located at the Federal University of Para. For the analyses, the regions of interest for monitoring the panels were considered, which are: the surface of the generator module in contact with the sun; the shaded area of the panel and the ambient temperature. Satisfactory results were obtained regarding the operation of the embedded system, which proved to be ideal for monitoring solar panels due to the ease of updating, communication, and data visualization.
TinySeeker: A Network for Seeking Tiny and Fast Moving Object Based on Asymmetric U-Net

To refine strategies and augment skills, both professional athletes and amateur players routinely utilize cameras to document their practice sessions and games. As a result, an increasing number of researchers are exploring this field, aiming to offer comprehensive insights. Object detection is a pivotal task within this field, as identifying object locations can provide valuable insights, such as strategic analysis. However, only a limited number of studies have specifically focused on tracking fast-moving and indistinct objects such as a badminton shuttlecock. The preceding method, TrackNetv2, proposed the use of VGG-16 and U-Net, a heatmap-based approach, for badminton detection. However, the architecture of U-Net demands substantial computational resources in this paper. To tackle this issue, we present a pioneering asymmetric architecture named Tinyseeker inspired by U-Net. This novel model not only assures precise detection of the badminton shuttlecock's location, but it also champions computational efficiency. The reimagined structure strikes an optimal balance between detection accuracy and computational demands, making it a practical and effective solution for real-world applications. Experimental results show that Tinyseeker can reduce calculation up to 26% while remaining the precision. This architecture marks a significant advancement in the field, pushing the boundaries of what is possible within object detection tasks and setting a new benchmark for similar studies in the future.
SAPVAD: An Efficient Voice Activity Detection Model Based on Spectral Attention and Parallel Structure

Recently, the parallel architecture combining global and local features has achieved remarkable performance in speech recognition tasks . Inspired by this, we propose a VAD method to further improve VAD performance under adverse noise conditions with low signal-to-noise ratio (SNR). Specifically, we replace the temporal attention module in STAM [1] with a parallel structure, and introduce adaptive mask convolution to more finely capture vocal characteristics in acoustic signals. With the parallel architecture tailored for VAD tasks, Our SAPVAD method comprehensively captures acoustic environmental and speech features, enabling accurate speech detection in noisy environments.