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Keyword: ProcessingAuto-Encoder based Deep Learning for Surface Electromyography Signal Processing
Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning,…
Read MoreSelf-Organizing Map based Feature Learning in Bio-Signal Processing
Feature extraction is playing a significant role in bio-signal processing. Feature identification and selection has two approaches. The standard method is engineering handcraft which is based on user experience and application area. While the other approach is feature learning that based on making the system identify and select the best features suit the application. The…
Read MoreBeyond Fitness: Revolutionary Exercise Tracker Combining Pose Recognition with Heart Rate Monitoring using Remote Photoplethysmography (rPPG)
Heart rate (HR) is a critical indicator in fitness monitoring, athletic performance evaluation, and injury prevention. However, traditional motion-sensitive wearable devices are highly susceptible to movement artifacts, which degrade measurement accuracy during physical activity. Remote photoplethysmography (rPPG) offers a non-contact alternative for HR measurement, though it too remains sensitive to motion. This study proposes a…
Read MoreFederated Learning with Differential Privacy and Blockchain for Security and Privacy in IoMT A Theoretical Comparison and Review
The growing integration of the Internet of Medical Things (IoMT) into healthcare has amplified the need for secure and privacy-preserving artificial intelligence. Federated Learning (FL) has emerged as a pivotal paradigm for decentralized medical data processing; however, it still faces challenges concerning data confidentiality, trust management, and scalability. This review presents an extended theoretical comparison…
Read MoreTIMeFoRCE: An Identity and Access Management Framework for IoT Devices in A Zero Trust Architecture
Zero Trust Architecture offers a transformative approach to network security by emphasizing ”never trust, always verify.” IoT devices, while increasingly integral to modern ecosystems, pose unique challenges for identity management and access control due to their constrained processing power, memory, and energy capabilities. In a Zero Trust framework, every IoT device is treated as a…
Read MoreCIRB-Edge for Secure, Energy-Efficient, and Real-Time Edge Computing
In this work, we present CIRB-Edge, a novel integer compression method designed specifically to overcome the limitations of traditional techniques such as Huffman coding, Delta encoding, and dictionary-based algorithms. These legacy methods often fall short in meeting the stringent requirements of secure, energy-efficient, and real-time edge computing due to their high computational overhead, memory demands,…
Read MorePredictive Analytics in Marketing: Evaluating its Effectiveness in Driving Customer Engagement
Understanding and responding to customer feedback is critical for business success. Customer response data offers valuable insights into preferences, behaviours, and sentiment. By analysing this data, businesses can optimize strategies, enhance customer experiences, and drive growth. Many analysis have been conducted in this field, while the review covers a broad range of AI and ML…
Read MoreUtilization of Generative Artificial Intelligence to Improve Students’ Visual Literacy Skills
This study aims to examine the impact of Gen AI utilization on students’ visual literacy skills using a quantitative approach and data instruments in the form of post-test scores of the control class and experimental class which are analyzed to measure the effectiveness of GEN AI in improving students’ visual literacy skills at four universities.…
Read MoreGenerative Artificial Intelligence and Prompt Engineering: A Comprehensive Guide to Models, Methods, and Best Practices
This article enhances discussions on Generative Artificial Intelligence (GenAI) and prompt engineering by exploring critical pitfalls and industry-specific advantages. It begins with a foundational overview of AI evolution, emphasizing how generative models such as GANs, VAEs, and Transformers have revolutionized language processing, image generation, and drug discovery. Prompt engineering is highlighted as a key methodology…
Read MoreRevolutionizing Robo-Advisors: Unveiling Global Financial Markets, AI-Driven Innovations, and Technological Landscapes for Enhanced Investment Decisions
Robo-advisors, fundamental to the financial services sector, have undergone substantial technological metamorphosis. Innovations in artificial intelligence, blockchain, cloud technology, augmented reality, and virtual reality have reshaped the financial industry’s landscape. As automated investment solutions, robo-advisors are on the brink of further technological evolution. This comprehensive research amalgamates historical data, behavioral insights, and emerging market trends…
Read MoreEnhancing the Network Anomaly Detection using CNN-Bidirectional LSTM Hybrid Model and Sampling Strategies for Imbalanced Network Traffic Data
The cybercriminal utilized the skills and freely available tools to breach the networks of internet-connected devices by exploiting confidentiality, integrity, and availability. Network anomaly detection is crucial for ensuring the security of information resources. Detecting abnormal network behavior poses challenges because of the extensive data, imbalanced attack class nature, and the abundance of features in…
Read MoreImproved Candidate-Career Matching Using Comparative Semantic Resume Analysis
A resume is a prevalent and generally employed method for individuals to showcase their proficiency and qualifications. It is typically composed using diverse customized, personalized methods in multiple inconsistent formats (such as pdf, txt, doc, etc.). Screening candidates based on the alignment of their resume with a set of job requirements is typically a labori-…
Read MoreMRI Semantic Segmentation based on Optimize V-net with 2D Attention
Over the past ten years, deep learning models have considerably advanced research in artificial intelligence, particularly in the segmentation of medical images. One of the key benefits of medical picture segmentation is that it allows for a more accurate analysis of anatomical data by separating only pertinent areas. Numerous studies revealed that these models could…
Read MoreLocalization of Impulsive Sound Source in ShallowWaters using a Selective Modal Analysis Algorithm
Passive remote monitoring applications of underwater signal processing in a shallow water environment are an impactful area of research for environmental and marine-life monitoring. The majority of the sound source localization techniques require carefully placed synchronized hydrophone arrays, which can be complicated and hard to maintain. In this paper, we utilized the modal dispersions of…
Read MoreImproving License Plate Identification in Morocco: Intelligent Region Segmentation Approach, Multi-Font and Multi-Condition Training
The exponential growth in the number of automobiles over the past few decades has created a pressing need for a robust license plate identification system that can perform effectively under various conditions. In Morocco, as in other regions, local authorities, public organizations, and private companies require a reliable License Plate Recognition (LPR) system that takes…
Read MoreAccuracy Improvement-Based Wireless Sensor Estimation Technique with Machine Learning Algorithms for Volume Estimation on the Sealed Box
Currently, the quality and quantity of product must be inspected before transporting. Currently the popular unsealing box product inspecting is performed by weighing the box where the errors occur according to the tolerance of the weighting machine and tolerance weight of the product. On the other hand, the quantity of product can be inspected automatically…
Read MoreHybrid Discriminant Neural Networks for Performance Job Prediction
Determining the best candidates for a certain job rapidly has been one of the most interesting subjects for recruiters and companies due to high costs and times that takes the process. The accuracy of the models, particularly, is heavily influenced by the discriminant variables that are chosen for predicting the candidates scores. This study aims…
Read MoreHybrid Intrusion Detection Using the AEN Graph Model
The Activity and Event Network (AEN) is a new dynamic knowledge graph that models different network entities and the relationships between them. The graph is generated by processing various network security logs, such as network packets, system logs, and intrusion detection alerts, which allows the graph to capture security-relevant activity and events in the network.…
Read MoreDevelopment of an Intelligent Road Anomaly Detection System for Autonomous Vehicles
Globally, road transportation has become one of the most reliable means of moving goods and services from one place to the other. It has contributed immensely to the standard of living and modern civilization. However, this means of transportation is characterised by some issues which are poised to be harmful to the human population if…
Read MoreDismantle Shilling Attacks in Recommendations Systems
Collaborative filtering of recommended systems (CFRSs) suffers from overrun false rating injections that diverge the system functions for creating accurate recommendations. In this paper, we propose a three-stage unsupervised approach. Starts by defining the mechanism(s) that makes recommendation vulnerable to attack. Second, find the maximum-paths or the associated related items valued by the user. We…
Read MoreAn Ensemble of Voting- based Deep Learning Models with Regularization Functions for Sleep Stage Classification
Sleep stage performs a vital role in people’s daily lives in the detection of sleep-related diseases. Conventional automated sleep stage classifier models are not efficient due to the complexity linked to the design of mathematical models and extraction of hand-engineering features. Further, quick oscillations amongst sleep stages frequently lead to indistinct feature extraction, which might…
Read MoreBirds Images Prediction with Watson Visual Recognition Services from IBM-Cloud and Conventional Neural Network
Bird watchers and people obsessed with raising and taming birds make a kind of motivation about our subject. It consists of the creation of an Android application called ”Birds Images Predictor” which helps users to recognize nearly 210 endemic bird species in the world. The proposed solution compares the performance of the python script, which…
Read MoreDesigning the MIMO SDR-based Antenna Array for 5G Telecommunication
With the significant spread of 5G telecommunication systems, the demand for high quality of service and better coverage is growing rapidly. Multiple-input–multiple-output is planned to be among the key technologies in 5G telecommunication’s field. In this article, a new fully digital 2×2 MIMO testbed is implemented, using USRP B210 and 2 pairs of microstrip antennas…
Read MoreAssociation Rules for Knowledge Discovery From E-News Articles: A Review of Apriori and FP-Growth Algorithms
Owing to technological development, the internet has become the world’s largest platform where an unaccountable amount of e-news information is freely available to use. Most of the time, e-newspaper readers have to examine the massive collection of e-news articles to locate necessary information relevant to them. Massive semi-structured and unstructured texts usually mislead the readers…
Read MoreSensitive Analysis in Holding and Penalty Costs for the Stochastic Sequencing Problem in Agile Manufacturing
In agile manufacturing, due to the desire to meet customer’s requirements, processing times are stochastic because operations could be done by robots or humans. This can cause several problems in scheduling the jobs, and it is necessary to select the dispatch rule with the least change in costs and times, to respond quickly to different…
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