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Topic Review
Stock Market Prediction Using Deep Reinforcement Learning
Stock market investment, a cornerstone of global business, has experienced unprecedented growth, becoming a lucrative, yet complex field. Predictive models, powered by cutting-edge technologies like artificial intelligence (AI), sentiment analysis, and machine learning algorithms, have emerged to guide investors in their decision-making processes.
  • 1.1K
  • 22 Nov 2023
Topic Review
Computer-Aided Diagnosis Using Gastrointestinal Endoscopy
Computer-aided diagnosis (CAD) system based on deep learning to assist doctors in diagnosis is of great significance, because diagnosing lesions in the stomach, intestines, and esophagus is laborious for doctors. In addition, misdiagnoses can occur based on a subjective judgment.
  • 1.1K
  • 22 Apr 2021
Topic Review
Analysing Hucul Horses by AI
       The neural classification system in form of a multi-layered artificial neural network suggested in this paper was implemented in the programming environment MATLAB. MATLAB is a useful tool focused mainly on scientific and technical calculations. It boasts of a wide spectrum of software solutions/libraries, the so-called Toolboxes that can be used, for example to create and optimize neural networks. It is fully compatible with other programming environments.  Matlab is a tool for rapid prototyping that enables a wide range of learning algorithms, the selection of optimal neural network architecture, the selection of the most efficient neuron activation functions as well as optimal learning parameters.          The design of the network is of key significance both for the learning process, and the quality of its operation in later stages. The set of input data, purpose, and results do have significant impact on the they configuration.  A key assumption is taking cognizance of factual links between the set of explanatory variables (input) and the output.        The artificial neural networks enable the capture of relationships and dependencies between the data in circumstances where the application of traditional analytical methods would not have yielded satisfactory solutions.         The use of ANN enables objective assessments of individual animals by taking into account only factors essential for determining horses’ performance and breeding values.         Preliminary results of the application of artificial neural networks in predicting the utility value of Hucul horses, relying on a specific set of features seem rather promising.        It offers potential possibilities of evaluation, relying on available information about the animals.
  • 1.1K
  • 13 Oct 2020
Topic Review
Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis
Recommendation systems (RSs) are widely used in e-commerce to improve conversion rates by aligning product offerings with customer preferences and interests. While traditional RSs rely solely on numerical ratings to generate recommendations, these ratings alone may not be sufficient to offer personalized and accurate suggestions. To overcome this limitation, additional sources of information, such as reviews, can be utilized. However, analyzing and understanding the information contained within reviews, which are often unstructured data, is a challenging task. To address this issue, sentiment analysis (SA) has attracted considerable attention as a tool to better comprehend a user’s opinions, emotions, and attitudes.
  • 1.1K
  • 21 Aug 2023
Topic Review
Challenges in Agricultural Image Datasets and Filter Algorithms
Smart farming is facilitated by remote sensing because it allows for the inexpensive monitoring of crops, crop classification, stress detection yield forecasting using lightweight sensors over a wide area in a relatively short amount of time. Deep learning (DL)-based computer vision is one of the important aspects of the automatic detection and monitoring of plant stress. Challenges for DL algorithms in the agricultural dataset include size variation in objects, image resolution, background clutter, precise annotation with the expert, high object density or the demand for different spectral images.
  • 1.1K
  • 07 Mar 2024
Topic Review
Automated Stuttering Classification
Speech disfluency, particularly stuttering, can have a significant impact on effective communication. Stuttering is a speech disorder characterized by repetitions, prolongations, and blocks in the flow of speech, which can result in communication difficulties, social isolation, and low self-esteem. Stuttering can also lead to negative reactions from listeners, such as impatience or frustration, which can further exacerbate communication difficulties.
  • 1.1K
  • 07 Oct 2023
Topic Review
Platform for monitoring arboviruses
As part of SDG, the members of the UN aim to end epidemics of neglected tropical diseases by 2030. These include wide range communicable diseases that prevail in tropical and subtropical conditions. These diseases are present in over 149 countries worldwide and are a significant burden on health systems and economies. One major category of neglected tropical disease are arthropod-borne viruses or arboviruses including West Nile virus, yellow fever, dengue, chikungunya and Zika. Arboviruses spread rapidly and as they present very similar symptoms, it is hard to diagnose and select the best treatment. The use of machine learning for the diagnosis and prognosis of these diseases has become increasingly common however there is a paucity of research on deep learning and associated decision support platforms for frontline staff. 
  • 1.1K
  • 25 Dec 2020
Topic Review
Oriented Crossover in Genetic Algorithms
A genetic algorithm is a formula for resolving optimization issues that incorporate a constraint and natural selection similar to the biological process that propels evolution.
  • 1.1K
  • 11 May 2023
Topic Review
Update on Cyber Health Psychology
In recent years, there has been more and more talk of cyber health psychology and the implication that new technologies can have in the diagnosis, treatment, and rehabilitation of psychopathological issues in the field of mental health, ranging from post-traumatic stress disorder (PTSD) to addiction to substances of abuse.
  • 1.1K
  • 25 Mar 2022
Topic Review
Application of Machine Learning in Traumatic Brain Injury
One of the main challenges in traumatic brain injury (TBI) patients is to achieve an early and definite prognosis. Despite the recent development of algorithms based on artificial intelligence for the identification of these prognostic factors relevant for clinical practice, the literature lacks a rigorous comparison among classical regression and machine learning (ML) models. The utility of comparing traditional regression modeling to ML is highlighted here, particularly when using a small number of reliable predictor variables after TBI. The dataset of clinical data used to train ML algorithms will be publicly available to other researchers for future comparisons. 
  • 1.1K
  • 21 Mar 2022
Topic Review
Deep Learning Models for Radiography in Chest Disease
Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases. Most thoracic diseases have very similar patterns, which makes diagnosis prone to human error and leads to misdiagnosis. Machine learning (ML) and deep learning (DL) provided techniques to make this task more efficient and faster. Numerous experiments in the diagnosis of various diseases proved the potential of these techniques.
  • 1.1K
  • 18 Jan 2023
Topic Review
Deep Learning-Based Methods for Crop Disease Estimation
Deep learning methods such as U-Net, SegNet, YOLO, Faster R-CNN, VGG and ResNet have been used extensively for crop disease estimation using Unmanned Aerial Vehicle (UAV)  imagery. The basic building block of the deep learning architecture is basically the success of convolutional neural networks (CNN). The deep learning models implemented for crop disease estimation using UAV imagery can be categorized into classification-based, segmentation-based and detection-based approaches. Segmentation-based models attempt to classify each pixel in an image into different categories such as healthy vs. diseased pixels, whereas classification-based models look into overall images and classify the image into pre-defined disease classes.
  • 1.1K
  • 16 May 2023
Topic Review
Deep Learning in COVID-19
Various deep-learning (DL) methods that utilize a combination of omics data and imaging data have been applied to the diagnosis, prognosis, and treatment options of clinical COVID-19 patients. Even with the emerging deep-learning methods, human intervention is still essential in the clinical diagnosis and treatment of COVID-19 patients.
  • 1.1K
  • 06 Apr 2023
Topic Review
Remote Sensing Object Detection
Remote sensing image object detection holds signifificant research value in resources and the environment. Nevertheless, complex background information and considerable size differences between objects in remote sensing images make it challenging.
  • 1.1K
  • 04 Sep 2023
Topic Review
Deep Learning in Water Leak Detection
The escalating global water usage and the increasing strain on major cities due to water shortages highlights the critical need for efficient water management practices. In water-stressed regions worldwide, significant water wastage is primarily attributed to leakages, inefficient use, and aging infrastructure. Undetected water leakages in buildings’ pipelines contribute to the water waste problem. 
  • 1.1K
  • 22 Nov 2023
Topic Review
Hand Pose Recognition Using Parallel Multi Stream CNN
Recently, several computer applications provided operating mode through pointing fingers, waving hands, and with body movement instead of a mouse, keyboard, audio, or touch input such as sign language recognition, robot control, games, appliances control, and smart surveillance. With the increase of hand-pose-based applications, new challenges in this domain have also emerged. Support vector machines and neural networks have been extensively used in this domain using conventional RGB data, which are not very effective for adequate performance.
  • 1.1K
  • 12 Jan 2022
Topic Review
Salp Swarm Algorithm
The Salp Swarm Algorithm (SSA) is a bio-inspired metaheuristic optimization technique that mimics the collective behavior of Salp chains hunting for food in the ocean. While it demonstrates competitive performance on benchmark problems, the SSA faces challenges with slow convergence and getting trapped in local optima like many population-based algorithms. 
  • 1.1K
  • 26 Jan 2024
Topic Review
Hybrid Digital Food Twin
Food production is highly complex due to the various chemo-physical and biological processes that must be controlled to transform ingredients into final products. Further, production processes must be adapted to the variability of the ingredients, e.g., due to seasonal fluctuations of raw material quality. Digital twins are known from Industry 4.0 as a method to model, simulate, and optimize processes. Such a digital food twin has to consider the changes within the food due to micro-biological, chemical, and physical processes. Consequently, researchers propose the concept of a hybrid digital twin, which integrates simulation and data science (i.e., machine learning) to combine a data-driven perspective, simulations, and scientific models to describe the food product and the food processing process. 
  • 1.1K
  • 16 Sep 2022
Topic Review
Resilience in the Cyberworld
Resilience is a feature that is gaining more and more attention in computer science and computer engineering. However, the definition of resilience for the cyber landscape, especially embedded systems, is not yet clear. 
  • 1.1K
  • 25 Nov 2021
Topic Review
Impact of AI on the Future of Work
Artificial Intelligence (AI) is transforming the way we work, creating new opportunities for efficiency, innovation, and growth. However, it also poses several challenges, including job displacement, skills gaps, and ethical concerns. This research explores the potential impact of AI on the future of work and discusses strategies for addressing these challenges. By embracing AI technology and investing in the development of new skills, we can create a future of work that is more productive, equitable, and sustainable.
  • 1.1K
  • 22 May 2023
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