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Topic Review
Social Recommender Systems
Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data.
  • 142
  • 11 Dec 2023
Topic Review
Adversarial Attacks in Camera-Based Vision Systems
Vision-based perception modules are increasingly deployed in many applications, especially autonomous vehicles and intelligent robots. These modules are being used to acquire information about the surroundings and identify obstacles. Hence, accurate detection and classification are essential to reach appropriate decisions and take appropriate and safe actions at all times. Adversarial attacks can be categorized into digital and physical attacks.
  • 185
  • 11 Dec 2023
Topic Review
Enhancing Ransomware Attack Detection on Cloud-Encrypted Data
Ransomware attacks on cloud-encrypted data pose a significant risk to the security and privacy of cloud-based businesses and their consumers. RANSOMNET+, a state-of-the-art hybrid model that combines Convolutional Neural Networks (CNNs) with pre-trained transformers, to efficiently take on the challenging issue of ransomware attack classification. RANSOMNET+ excels over other models because it combines the greatest features of both architectures, allowing it to capture hierarchical features and local patterns. The findings demonstrate the exceptional capabilities of RANSOMNET+. The model had a fantastic precision of 99.5%, recall of 98.5%, and F1 score of 97.64%, and attained a training accuracy of 99.6% and a testing accuracy of 99.1%. The loss values for RANSOMNET+ were impressively low, ranging from 0.0003 to 0.0035 throughout training and testing. RANSOMNET+ excelled over the other two models in terms of F1 score, accuracy, precision, and recall. The algorithm’s decision-making process was also illuminated by RANSOMNET+’s interpretability analysis and graphical representations. The model’s openness and usefulness were improved by the incorporation of feature distributions, outlier detection, and feature importance analysis. Finally, RANSOMNET+ is a huge improvement in cloud safety and ransomware research. As a result of its unrivaled accuracy and resilience, it provides a formidable line of defense against ransomware attacks on cloud-encrypted data, keeping sensitive information secure and ensuring the reliability of cloud-stored data. Cybersecurity professionals and cloud service providers now have a reliable tool to combat ransomware threats thanks to this research.
  • 149
  • 11 Dec 2023
Topic Review
IoT Security Challenges and Intrusion Detection Systems
Cybersecurity finds widespread applications across diverse domains, encompassing intelligent industrial systems, residential environments, personal gadgets, and automobiles. This has spurred groundbreaking advancements while concurrently posing persistent challenges in addressing security concerns tied to IoT devices. IoT intrusion detection involves using sophisticated techniques, including deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and anomaly detection algorithms, to identify unauthorized or malicious activities within IoT ecosystems. These systems continuously monitor and analyze network traffic and device behavior, seeking patterns that deviate from established norms. When anomalies are detected, security measures are triggered to thwart potential threats. IoT intrusion detection is vital for safeguarding data integrity, ensuring users’ privacy, and maintaining critical systems’ reliability and safety.
  • 437
  • 11 Dec 2023
Topic Review
Sign Language Recognition
Recognition of hand motion capture is an interesting topic. Hand motion can represent many gestures. In particular, sign language plays an important role in the daily lives of hearing-impaired people. Sign language recognition is essential in hearing-impaired people’s communication. Wearable data gloves and computer vision are partially complementary solutions. However, sign language recognition using a general monocular camera suffers from occlusion and recognition accuracy issues.
  • 360
  • 08 Dec 2023
Topic Review
Semantic Textual Similarity
Semantic textual similarity (STS) refers to the degree of similarity between two pieces of text based on their meaning or semantics and assesses how closely related or similar two pieces of text are based on the information they convey, regardless of variations in wording or structure. STS has been explored from both linguistic and computational perspectives. It holds significance in various NLP applications, and in recent years, Transformer-based neural language models have emerged as the state-of-the-art solutions for many of these applications.
  • 210
  • 08 Dec 2023
Topic Review
Multi-Label Classification Based on Associations
Associative classification (AC) has been shown to outperform other methods of single-label classification for over 20 years. In order to create rules that are both more precise and simpler to grasp, AC combines the rules of mining associations with the task of classification.
  • 160
  • 08 Dec 2023
Topic Review
Food-Waste-Reduction Based on IoT and Big Data
IoT technology through ICT infrastructure and smart devices combines to gather huge amounts of data in real-time, which is commonly known as big data. The big data generated by IoT devices will be stored in the big data storage system and will be used for analysis. The importance of Food Wastage Reduction (FWR) is related to the loss of all the natural resources in the supply chain, including expenditures related to the use of land, water supply, and energy consumption. The application of IoT to FWR systems is also examined where use RFID sensors as a key tool to monitor food waste for each individual in accordance with the proposed model, while describe the application of IoT-based technologies to agricultural supply chain management in developing countries.
  • 920
  • 08 Dec 2023
Topic Review
Transformer-Based Visual Object Tracking
With the rise of general models, transformers have been adopted in visual object tracking algorithms as feature fusion networks. In these trackers, self-attention is used for global feature enhancement. Cross-attention is applied to fuse the features of the template and the search regions to capture the global information of the object. However, studies have found that the feature information fused by cross-attention does not pay enough attention to the object region. In order to enhance cross-attention for the object region, an enhanced cross-attention (ECA) module is proposed for global feature enhancement.
  • 188
  • 08 Dec 2023
Topic Review
Compensation of Pressure Sensor Drifts
Pressure sensor chips embodied in very tiny packages are deployed in a wide range of advanced applications. Examples of them range from industrial to altitude location services. They are also becoming increasingly pervasive in many other fields, ranging from industrial to military to consumer. However, these sensors, which are very cheap to manufacture in silicon, are strongly affected by thermal, mechanical and environmental stresses, which ultimately affect their measurement accuracy in the form of variations in gain, hysteresis, and nonlinear responses. To compensate induced drift in measurements, several neural networks were devised and be applied to stresses caused by two thermal cycles: 260 C for 10-40 seconds (JEDEC soldering procedure) and 100 C for two hours. These models were characterized in accuracy and deployability on tiny embedded devices and improved accuracy was observed.
  • 278
  • 08 Dec 2023
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