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Topic Review
Blockchain-Based Authentication in Internet of Vehicles
Internet of Vehicles (IoV) is capable of providing various intelligent services and supporting different applications for the drivers and passengers on roads. The IoV to be able to offer beneficial road services, huge amounts of data are generated and exchanged among the different communicated entities wirelessly via open channels, which could attract the adversaries and threaten the network with several possible types of security attacks. In this survey, the authentication part of security system is targeted while highlighting the efficiency of blockchains in the IoV environments.
  • 2.0K
  • 10 Dec 2021
Topic Review
Image-Based Malware Detection
Image conversion of malicious binaries, or binary visualisation, is a relevant approach in the security community. It has exceeded the role of a single-file malware analysis tool and has become a part of Intrusion Detection Systems (IDSs) thanks to the adoption of Convolutional Neural Networks (CNNs).
  • 2.0K
  • 16 Nov 2023
Topic Review
Singing Voice Detection
Singing voice detection or vocal detection is a classification task that determines whether there is a singing voice in a given audio segment. This process is a crucial preprocessing step that can improve the performance of other tasks such as automatic lyrics alignment, singing melody transcription, singing voice separation, vocal melody extraction, and many more.
  • 2.0K
  • 25 Jan 2022
Topic Review
Deep Learning for Robotic Vision Methods
Robotic vision algorithms serve three primary functions in visual perception. Pattern recognition in machine vision is the process of identifying and classifying objects or patterns in images or videos using machine learning algorithms. Deep learning in robotic vision reveals a plethora of promising approaches, each with its own unique strengths and characteristics. Robotic vision systems can leverage the strengths of each architecture to improve object detection, tracking, and the understanding of complex visual scenes in dynamic environments. Big data and federated learning play significant roles in advancing the field of computer vision. Big data provides a wealth of diverse visual information, which is essential for training deep learning models that power computer vision applications. These datasets enable more accurate object recognition, image segmentation, and scene understanding.
  • 2.0K
  • 07 Feb 2024
Topic Review
AI and Time Management: Boosting Productivity and Efficiency
In today's fast-paced world, time is a precious resource that needs to be managed efficiently to achieve maximum productivity. Artificial intelligence (AI) has emerged as a game-changer in this regard, providing individuals and institutions with powerful tools to optimize their time management. The research explores the various ways in which AI is helping individuals and institutions to boost their productivity and efficiency through better time management. The AI-based productivity tools, automated time tracking, predictive analytics, and personalized time management, highlighting the benefits and potential limitations of each approach were discussed.
  • 2.0K
  • 22 May 2023
Topic Review
Distributed Deep Learning: From Single-Node to Multi-Node Architecture
During the last years, deep learning (DL) models have been used in several applications with large datasets and complex models. These applications require methods to train models faster, such as distributed deep learning (DDL). Local parallelism is considered quite important in the design of a time-performing multi-node architecture because DDL depends on the time required by all the nodes. 
  • 2.0K
  • 08 Jun 2022
Topic Review
Quantum Generative Adversarial Networks
Quantum mechanics studies nature and its behavior at the scale of atoms and subatomic particles. By applying quantum mechanics, a lot of problems can be solved in a more convenient way thanks to its special quantum properties, such as superposition and entanglement. In the current noisy intermediate-scale quantum era, quantum mechanics finds its use in various fields of life. Following this trend, researchers seek to augment machine learning in a quantum way. The generative adversarial network (GAN), an important machine learning invention that excellently solves generative tasks, has also been extended with quantum versions. Since the first publication of a quantum GAN (QuGAN) in 2018, many QuGAN proposals have been suggested. A QuGAN may have a fully quantum or a hybrid quantum–classical architecture, which may need additional data processing in the quantum–classical interface. Similarly to classical GANs, QuGANs are trained using a loss function in the form of max likelihood, Wasserstein distance, or total variation. The gradients of the loss function can be calculated by applying the parameter-shift method or a linear combination of unitaries in order to update the parameters of the networks. 
  • 2.0K
  • 15 Mar 2023
Topic Review
Predicting Students’ Performance by ML
Predicting students' performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students' activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students' knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students' performance.
  • 2.0K
  • 02 Feb 2021
Topic Review
Computational Diagnostic Techniques in Electrocardiogram
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient’s ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.
  • 2.0K
  • 18 Feb 2021
Topic Review
Full-Reference Image Quality Assessment
To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). Having a metric to quantify the image quality to the HVS is always a tough task. Image quality assessment (IQA) can be subjective or objective.
  • 2.0K
  • 09 Jan 2024
Topic Review
Digital Face Manipulation Creation and Detection
Deepfake refers to the sophisticated manipulation of audiovisual content using deep learning techniques, particularly generative adversarial networks (GANs). It enables the creation of hyper-realistic fake videos or images by seamlessly superimposing one person's face or voice onto another's. These manipulated media raise significant concerns about misinformation, privacy invasion, and the potential to deceive audiences. Deepfakes have sparked discussions about the ethical implications of digital media manipulation and the challenges of distinguishing between genuine and fabricated content in the digital age. Efforts to counter deepfake technology involve developing advanced detection methods and raising awareness about the prevalence of manipulated media.
  • 1.9K
  • 25 Aug 2023
Topic Review
Opportunities and Challenges in Quantum Computing for Business
Quantum computing is emerging as a groundbreaking force, promising to redefine the boundaries of technology and business. 
  • 1.9K
  • 14 Nov 2023
Topic Review
Artificial Intelligence Techniques in Surveillance Video Anomaly Detection
The Surveillance Video Anomaly Detection (SVAD) system is a sophisticated technology designed to detect unusual or suspicious behavior in video surveillance footage without human intervention. The system operates by analyzing the video frames and identifying deviations from normal patterns of movement or activity. This is achieved through advanced algorithms and machine learning techniques that can detect and analyze the position of pixels in the video frame at the time of an event.
  • 1.9K
  • 10 May 2023
Topic Review Peer Reviewed
Large Language Models and Logical Reasoning
In deep learning, large language models are typically trained on data from a corpus as representative of current knowledge. However, natural language is not an ideal form for the reliable communication of concepts. Instead, formal logical statements are preferable since they are subject to verifiability, reliability, and applicability. Another reason for this preference is that natural language is not designed for an efficient and reliable flow of information and knowledge, but is instead designed as an evolutionary adaptation as formed from a prior set of natural constraints. As a formally structured language, logical statements are also more interpretable. They may be informally constructed in the form of a natural language statement, but a formalized logical statement is expected to follow a stricter set of rules, such as with the use of symbols for representing the logic-based operators that connect multiple simple statements and form verifiable propositions.
  • 1.9K
  • 31 May 2023
Topic Review
Machine Learning and Artificial Intelligence
Machine learning (ML) is a type of artificial intelligence (AI) consisting of algorithmic approaches that enable machines to solve problems deprived of explicit computer programming. 
  • 1.9K
  • 28 Apr 2023
Topic Review
Multi-Omics Model for Cancer Genetics
In the coming age of omics technologies, next gen sequencing, proteomics, metabolomics, and other high throughput techniques will become the usual tools in biomedical cancer research. However, their integrative approach is not trivial due to the broad diversity of data types, dynamic ranges and sources of experimental and analytical errors characteristic of each omics.
  • 1.9K
  • 02 Jun 2021
Topic Review
Avalanche (Protocol)
Avalanche is a protocol for solving consensus in a network of unreliable machines, where failures may be crash-fault or Byzantine. The protocol was anonymously introduced on IPFS on May 16, 2018 and was formalized in more detail by Cornell University researchers in 2019. Protocol currently provides system operation of the Avalanche (platform) and his platform. The protocol has four basic interrelated mechanisms that compose structural support of the consensus tool. These four mechanisms are Slush, Snowflake, Snowball, and Avalanche. By using randomized sampling and metastability to ascertain and persist transactions, It represents a new protocol family. Although the original paper focused on a single protocol, namely Avalanche, it implicitly introduced a broad spectrum of voting-based, or quorum-based consensus protocols, called the Snow family. While Avalanche is a single instantiation, the Snow family seems to be able to generalize all quorum-based voting protocols for replica control. Unlike prior quorum-based work, the Snow family enables arbitrarily parametrizable failure probability at the quorum intersection level. Standard quorum-based protocols define this failure probability to be precisely zero, but by introducing errors in the quorum intersection, a larger set of consensus protocol design is available.
  • 1.9K
  • 02 Dec 2022
Topic Review
Multimedia Steganalysis
Steganography techniques aim to hide the existence of secret messages in an innocent-looking medium, where the medium before and after embedding looks symmetric. Steganalysis techniques aim to breach steganography techniques and detect the presence of invisible messages. 
  • 1.9K
  • 08 Feb 2022
Topic Review
Artificial Intelligence in Edge-Based IoT Applications
Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. 
  • 1.9K
  • 15 Feb 2023
Topic Review
AI for Addressing Algorithmic Bias in Job Hiring
More businesses are using artificial intelligence (AI) in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.
  • 1.9K
  • 20 Feb 2024
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