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Topic Review
Channel Correction for Anterior Cruciate Ligament Tear
The anterior cruciate ligament (ACL) is critical for controlling the motion of the knee joint, but it is prone to injury during sports activities and physical work. If left untreated, ACL injuries can lead to various pathologies such as meniscal damage and osteoarthritis. 
  • 312
  • 05 Jun 2023
Topic Review
Hybrid Evolutionary Approaches for Feature Selection
The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on a variety of metaheuristic (or evolutionary) algorithms and trying to suggest cutting-edge hybrid techniques to handle FS issues. The use of hybrid metaheuristic approaches for FS has thus been the subject of numerous research works.
  • 312
  • 26 Jul 2023
Topic Review
Deep Learning Methods for Solving the NLI Problem
Natural language inference (NLI) is one of the most important natural language understanding (NLU) tasks. NLI expresses the ability to infer information during spoken or written communication. The NLI task concerns the determination of the entailment relation of a pair of sentences, called the premise and hypothesis. If the premise entails the hypothesis, the pair is labeled as an “entailment”. If the hypothesis contradicts the premise, the pair is labeled a “contradiction”, and if there is not enough information to infer a relationship, the pair is labeled as “neutral”.
  • 312
  • 26 Apr 2024
Topic Review
Algorithms for Histopathology Image Detection and Segmentation
Histopathology image analysis is considered as a gold standard for the early diagnosis of serious diseases such as cancer. The advancements in the field of computer-aided diagnosis (CAD) have led to the development of several algorithms for accurately segmenting histopathology images.
  • 311
  • 27 Nov 2023
Topic Review
Fire and Smoke Detection
Wildfires are major natural disasters that can cause extensive damage to ecosystems and threaten human lives. It is an uncontrollable and destructive fire that rapidly spreads through vegetation, grasslands, or other flammable areas. Wildfires are typically triggered by a combination of factors, including the presence of abundant dry vegetation and favorable weather conditions like high temperatures, low humidity, and strong winds. The sources of ignition for wildfires are diverse and can range from natural causes like lightning strikes to human activities such as campfires, careless disposal of cigarettes, or even intentional acts of arson. Besides the destructive nature of wildfires, the smoke from wildfires can have severe human health risks and environmental consequences as it can contribute to air quality degradation, disrupt the balance of ecosystems, and even impact the behavior and survival of wildlife. Therefore, early fire and smoke detection are crucial.
  • 310
  • 12 Oct 2023
Topic Review
Generative Adversarial Networks for Computer Vision Tasks
Computer vision tasks have gained a lot of popularity, accompanied by the development of numerous powerful architectures consistently delivering outstanding results when applied to well-annotated datasets. However, acquiring a high-quality dataset remains a challenge, particularly in sensitive domains like medical imaging, where expense and ethical concerns represent a challenge. Generative adversarial networks (GANs) offer a possible solution to artificially expand datasets, providing a basic resource for applications requiring large and diverse data. 
  • 310
  • 23 Feb 2024
Topic Review
Recommendation Systems for e-Shopping
The interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers.
  • 309
  • 21 Dec 2023
Topic Review
Spatio-Temporal Hybrid Neural Network
The prediction of crowd flow in key urban areas is an important basis for city informatization development and management. Timely understanding of crowd flow trends can provide cities with data support in epidemic prevention, public security management, and other aspects. The model uses the Node2Vec graph embedding algorithm combined with LSTM (NDV-LSTM) to predict crowd flow.
  • 308
  • 26 May 2023
Topic Review
Serious Games for Learning Artificial Intelligence Algorithms
Artificial Intelligence (AI) is the technology of the future, as its applications are constantly expanding in every aspect of human life. The spread of the internet has given a great impetus to technologies that apply AI algorithms and make their presence more and more intense in everyday life. However, despite the ubiquitous presence of AI, few people can comprehend its true meaning and reason for its existence, especially the way it is applied. Serious games, that is games with a "serious purpose" other than entertaninment, can play an important role in comprehending and applying AI algorithms.
  • 308
  • 05 Jun 2023
Topic Review
Diagnosis of Monkeypox Disease Using Deep Learning
The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. 
  • 308
  • 03 Aug 2023
Topic Review
Privacy and Security in Sustainable Smart City Applications
Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML).
  • 308
  • 12 Dec 2023
Topic Review
Multimodal Biometric Identification System
In the past two decades, many physical and behavioral biometric modalities have been under extensive research, such as fingerprints, palm prints, palms/Finger Textures, faces, irises, voice, gait and signature. All these modalities are vulnerable to presentation spoof attacks; hence, the level of provided security is compromised. Fingerprint- and palm-print-based biometric systems may be deceived by using gelatin or clay-made artificial fingerprint surfaces or images. Biometric systems using faces as a biometric modality may be attacked by using photographs, 3-D face models and recorded short clips. Iris-based biometric systems may encounter spoof attacks by employing iris images taken from enrolled users. Voice- and gait-based biometric systems may be attacked by feeding prerecorded audio and video to the recognition system, respectively. 
  • 308
  • 21 Dec 2023
Topic Review
Approaches of Automated Heart Disease Prediction
Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. 
  • 307
  • 26 Oct 2023
Topic Review
The Detection of Lanes and Lane Markings
Vision-based identification of lane area and lane marking on the road is an indispensable function for intelligent driving vehicles, especially for localization, mapping and planning tasks. However, due to the increasing complexity of traffic scenes, such as occlusion and discontinuity, detecting lanes and lane markings from an image captured by a monocular camera becomes persistently challenging. The lanes and lane markings have a strong position correlation and are constrained by a spatial geometry prior to the driving scene. Most existing studies only explore a single task, i.e., either lane marking or lane detection, and do not consider the inherent connection or exploit the modeling of this kind of relationship between both elements to improve the detection performance of both tasks.
  • 306
  • 08 Aug 2023
Topic Review
Models for Enhancing Autonomous Driving Accuracy
Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making, which is a result of their poor perceivance of environmental information. In today’s technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context, leading to inadequate decision-making, which may then lead to fatal accidents. To overcome this drawback, effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks, namely data cleaning and data fusion. Since the raw sensory data are complex and exhibit multimodal characteristics, more emphasis is given to data preprocessing. 
  • 306
  • 12 Oct 2023
Topic Review
Deep Learning Methods for Retinal Disease Diagnosis
The advancement of digital medical imaging has brought about a significant change in ophthalmology as it has introduced effective technologies that help in the detection of such diseases. By improving early detection through image analysis and identifying minuscule anomalies, Artificial Intelligence (AI) has considerably coped with retinal diseases. Different Machine Learning (ML) and Convolutional Neural Networks (CNNs) are efficient at analyzing images and are particularly incredible at recognizing complex patterns in medical images.
  • 305
  • 21 Oct 2023
Topic Review
Shaped-Charge Learning Architecture for the Human–Machine Teams
In spite of great progress in recent years, deep learning (DNN) and transformers have strong limitations for supporting human–machine teams due to a lack of explainability, information on what exactly was generalized, and machinery to be integrated with various reasoning techniques, and weak defense against possible adversarial attacks of opponent team members.
  • 305
  • 30 Jun 2023
Topic Review
Infant Cry Signal Diagnostic System
Early diagnosis of medical conditions in infants is crucial for ensuring timely and effective treatment. However, infants are unable to verbalize their symptoms, making it difficult for healthcare professionals to accurately diagnose their conditions. Crying is often the only way for infants to communicate their needs and discomfort. The different combination of the fused features is then fed into multiple machine learning algorithms including random forest (RF), support vector machine (SVM), and deep neural network (DNN) models. The evaluation of the system using the accuracy, precision, recall, F1-score, confusion matrix, and receiver operating characteristic (ROC) curve, showed promising results for the early diagnosis of medical conditions in infants based on the crying signals only, where the system achieved the highest accuracy of 97.50% using the combination of the spectrogram, harmonic ratio (HR), and Gammatone frequency cepstral coefficients (GFCCs) through the deep learning process. 
  • 304
  • 10 Jul 2023
Topic Review
Personalized Advertising Design Based on Individual’s Appearance
Market segmentation is a crucial marketing strategy that involves identifying and defining distinct groups of buyers to target a company’s marketing efforts effectively. Visual elements, such as color and shape, in advertising can effectively communicate the product or service being promoted and influence consumer perceptions of its quality. Similarly, a person’s outward appearance plays a pivotal role in nonverbal communication, significantly impacting human social interactions and providing insights into individuals’ emotional states.
  • 304
  • 13 Sep 2023
Topic Review
Blockchain-Based Federated Learning
Federated Learning (FL) is a distributed Deep Learning (DL) technique that creates a global model through the local training of multiple edge devices. It uses a central server for model communication and the aggregation of post-trained models. The central server orchestrates the training process by sending each participating device an initial or pre-trained model for training. To achieve the learning objective, focused updates from edge devices are sent back to the central server for aggregation. While such an architecture and information flows can support the preservation of the privacy of participating device data, the strong dependence on the central server is a significant drawback of this framework. Having a central server could potentially lead to a single point of failure. Further, a malicious server may be able to successfully reconstruct the original data, which could impact on trust, transparency, fairness, privacy, and security. Decentralizing the FL process can successfully address these issues. Integrating a decentralized protocol such as Blockchain technology into Federated Learning techniques will help to address these issues and ensure secure aggregation.
  • 304
  • 14 Nov 2023
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