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Topic Review
Peripheral Blood Smears Examination by Artificial Intelligence
Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PBS). In university medical centers, hematologists routinely examine hundreds of PBS slides daily to validate or correct outcomes produced by advanced hematology analyzers assessing samples from potentially problematic patients. This process may logically lead to erroneous PBC readings, posing risks to patient health. AI functions as a transformative tool, significantly improving the accuracy and precision of readings and diagnoses.
  • 371
  • 27 Dec 2023
Topic Review
NLP- and API-Sequence-Based Malware Detection and Classification Methodologies
The surge in malware threats propelled by the rapid evolution of the internet and smart device technology necessitates effective automatic malware classification for robust system security.
  • 371
  • 24 Jan 2024
Topic Review
Algorithms for Facial Expression Recognition in the Wild
Facial expression recognition (FER) in the wild has attracted much attention due to its wide range of applications. Approaches use deep learning models trained on relatively large images, which significantly reduces their accuracy when they have to infer low-resolution images.
  • 370
  • 22 Sep 2023
Topic Review
Proliferative Diabetic Retinopathy Diagnosis
Diabetic retinopathy is one of the abnormalities of the retina in which a diabetic patient suffers from severe vision loss due to an affected retina. Proliferative diabetic retinopathy (PDR) is the final and most critical stage of diabetic retinopathy. Abnormal and fragile blood vessels start to grow on the surface of the retina at this stage. It causes retinal detachment, which may lead to complete blindness in severe cases. 
  • 369
  • 21 Jul 2023
Topic Review
Approaches of Landslide Detection
Landslide detection can generally be categorized into two approaches: traditional methods of landslide identification and automatic identification methods based on machine learning algorithms. Traditional methods of landslide detection often rely on field surveys conducted by experienced geologists, complemented by instrumental imaging techniques for analysis. The second category predominantly utilizes pre-existing datasets of landslides and facilitates automatic identification through the construction of algorithmic models.
  • 369
  • 15 Aug 2023
Topic Review
Compression of Bio-Signals for IoMT Systems
Bio-signals are records of biological events inside the human body, such as a heartbeat or muscle contraction. These signals are used to detect whether there is a problem or disorder in a human organ.
  • 369
  • 17 Nov 2023
Topic Review
Arabic Sentiment Analysis of YouTube Comments
Arabic sentiment analysis is a challenging task due to a variety of challenges with the language. In Arabic, the same word might have a variety of meanings depending on the context. Arabic also has a rich morphology, with verb forms that are difficult to understand and elaborate syntactic patterns. The wide range of dialects spoken in Arabic is a significant barrier to sentiment analysis. In the region of the Middle East and North Africa, Arabic is spoken in a number of dialects, with substantial variations in vocabulary, syntax, and pronunciation. These factors make it challenging to develop accurate sentiment analysis models for Arabic texts. Despite the challenges, there have been successful research studies within the framework of sentiment analysis applied to the Arabic language.
  • 367
  • 28 Jul 2023
Topic Review
Stereo Disparity Estimation for Mobile Robots
Stereo cameras allow mobile robots to perceive depth in their surroundings by capturing two separate images from slightly different perspectives. This is necessary for tasks such as obstacle avoidance, navigation, and spatial mapping.
  • 367
  • 16 Oct 2023
Topic Review
Construction Method of Knowledge Graph for Image Recognition
With the continuous development of artificial intelligence technology and the exponential growth in the number of images, image detection and recognition technology is becoming more widely used. Image knowledge management is extremely urgent. The data source of a knowledge graph is not only the text and structured data but also the visual or auditory data such as images, video, and audio.
  • 367
  • 16 Oct 2023
Topic Review
Hubble Meets Webb
Researchers explore the generation of James Webb Space Telescope (JWSP) imagery via image-to-image translation from the available Hubble Space Telescope (HST) data.
  • 367
  • 26 Feb 2024
Topic Review
ADX (File Format)
CRI ADX is a lossy proprietary audio storage and compression format developed by CRI Middleware specifically for use in video games; it is derived from ADPCM. Its most notable feature is a looping function that has proved useful for background sounds in various games that have adopted the format, including many games for the Sega Dreamcast as well as some PlayStation 2, GameCube and Wii games. One of the first games to use ADX was Burning Rangers, on the Sega Saturn. Notably, the Sonic the Hedgehog series from the Dreamcast generation up to at least Shadow the Hedgehog have used this format for sound and voice recordings. Jet Set Radio Future for original Xbox also used this format. On top of the main ADPCM encoding, the ADX toolkit also includes a sibling format, AHX, which uses a variant of MPEG-2 audio intended specifically for voice recordings and a packaging archive, AFS, for bundling multiple CRI ADX and AHX tracks into a single container file. Version 2 of the format (ADX2) uses the HCA and HCA-MX extension, which are usually bundled into a container file with the extensions ACB and AWB. The AWB extension is not to be confused with the Audio format with the same extension and mostly contains the binary data for the HCA files.
  • 366
  • 29 Sep 2022
Topic Review
Greylisting
Greylisting is a method of defending e-mail users against spam. A mail transfer agent (MTA) using greylisting will "temporarily reject" any email from a sender it does not recognize. If the mail is legitimate, the originating server will try again after a delay, and if sufficient time has elapsed, the email will be accepted.
  • 365
  • 14 Oct 2022
Topic Review
Wrist-Based Electrodermal Activity Monitoring for Stress Detection
With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track health status using adequate user data. Insufficient data is accessible.
  • 365
  • 22 Dec 2023
Topic Review
Artificial Intelligence and Sustainability
Artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI’s potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This Systematic Mapping Study (SMS) study accomplishes a comprehensive analysis of "AI Sustainability," integrating both the sustainability of AI and AI for sustainability across environmental, social, and economic dimensions. The field exhibits a dynamic landscape, maturing significantly since 2019 with a surge in publications and diverse contributions. The study reveals a balanced perspective, emphasizing both sustainability perspectives equally. Recent papers indicate a trend towards holistic studies, yet the economic dimension remains relatively underexplored. Future research is encouraged to delve into the economic dimension, align with the United Nations’ Sustainable Development Goals (SDGs), and address stakeholder influence, ensuring a sustainable and inclusive AI future.
  • 364
  • 05 Mar 2024
Topic Review
Existing Approaches for Single-Image Super-Resolution
Deep learning has been introduced to single-image super-resolution (SISR). These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment.
  • 363
  • 04 Jul 2023
Topic Review
Sequence-Based Recommendation System
Sequence-based models have various applications in recommendation systems; these models recommend the interested items of the user according to the user’s behavioral sequence. However, sequence-based models have a limitation of length. When the length of the user’s behavioral sequence exceeds the limitation of the model, the model cannot take advantage of the complete behavioral sequence of the user and cannot know the user’s holistic interests. The accuracy of the model then goes down. Meanwhile, sequence-based models only pay attention to the sequential signals of the data but do not pay attention to the spatial signals of the data, which will also affect the model’s accuracy.
  • 363
  • 12 Jan 2024
Topic Review
Explainable Artificial Intelligence in Healthcare
Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI can be applied in many different areas, such as econometrics, biometry, e-commerce, and the automotive industry. AI has found its way into healthcare as well, helping doctors make better decisions, localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a “black box”, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans.
  • 362
  • 23 Aug 2023
Topic Review
Deep Reinforcement Learning for Vision-Based Navigation of UAVs
Unmanned Aerial Vehicles (UAVs), also known as drones, have advanced greatly in recent years. There are many ways in which drones can be used, including transportation, photography, climate monitoring, and disaster relief. The reason for this is their high level of efficiency and safety in all operations. While the design of drones strives for perfection, it is not yet flawless. When it comes to detecting and preventing collisions, drones still face many challenges. In this context, this research describes a methodology for developing a drone system that operates autonomously without the need for human intervention. This research applies reinforcement learning algorithms to train a drone to avoid obstacles autonomously in discrete and continuous action spaces based solely on image data. The research compare three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in avoiding obstacles, both stationary and moving The novelty of this research lies in its comprehensive assessment of the advantages, limitations, and future research directions of obstacle detection and avoidance for drones, using different reinforcement learning techniques. The findings could have practical implications for the development of safer and more efficient drones in the future.
  • 362
  • 13 Dec 2023
Topic Review
An Improved Modulation Recognition Algorithm
Modulation recognition is an important technology in wireless communication systems. Deep learning-based modulation recognition algorithms, which can autonomously learn deep features and achieve superior recognition performance compared with traditional algorithms.
  • 361
  • 19 May 2023
Topic Review
Commonsense Causal Reasoning
Commonsense causal reasoning is the process of understanding the causal dependency between common events or actions. Traditionally, it was framed as a selection problem. However, it cannot obtain enough candidates and needs more flexible causes (or effects) in many scenarios, such as causal-based QA problems. Thus, the ability to generate causes (or effects) is an important problem.
  • 361
  • 13 Dec 2023
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