Improved Deep Learning Performance for Real-Time Traffic Sign Detection and Recognition Applicable to Intelligent Transportation Systems

Barodi, Anass; Bajit, Abderrahim; Zemmouri, Abdelkarim; Benbrahim, Mohammed; Tamtaoui, Ahmed

doi:10.14569/ijacsa.2022.0130582

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The choice of basic embedded peripherals (Microblaze processor, LEDs, switch, and pushbutton) in the hardware part of the XPS environment (Xilinx platform studio) of the EDK software (Xilinx embedded development kit). Using the Xilinx® microprocessor debugger (XMD) to debug programs and verify systems using the Microblaze processor as shown in Figure 1 and Figure 2 [17], [28]. This work involves controlling a stepping motor by the embedded processor MicroBlaze with these devices (Figure 4).…”

Section: The Control System Structure (Design Flow)mentioning

confidence: 99%

Proposal of a reliable embedded circuit to control a stepper motor using microblaze soft-core processor

Zemmouri

Barodi

Satif

et al. 2022

IJRES

View full text Add to dashboard Cite

The vast majority of embedded system designs start with a processor-based system, using a microcontroller or microprocessor as the core element to predict and process the basic control tasks. This paper aims to increase the reliability of controlling a stepper motor by an embedded circuit, which generates four digital signals of pulse width modulation (PWM). The manager of this circuit is an embedded processor licensed from Microblaze, which sits within the FPGA architecture Virtex 5. This processor analyzes the parameters to execute the rotation under the best conditions (energy consumption, speed, precision, reliability). The communication with the PC takes place via the RS232 link to provide feedback. The autonomous processor generates an independent control frequency of the PC. The four digital signals of PWM are simulated on Xilinx's ISim interface and sent to the ULN2803 amplifier circuit to run the stepper motor.

show abstract

Section: The Control System Structure (Design Flow)mentioning

confidence: 99%

Proposal of a reliable embedded circuit to control a stepper motor using microblaze soft-core processor

Zemmouri

Barodi

Satif

et al. 2022

IJRES

View full text Add to dashboard Cite

show abstract

“…Deep-learning model components learn on their own and self-train to allow unsupervised acquisition, avoiding the time-consuming phrase of feature selection and extraction [3]. Many disciplines, including image classification [7], [8], speech recognition [9], intrusion detection [10], natural language processing [11]- [14], and email spam classification [5] have shown promising results using deeplearning approaches. Researchers have not treated deep neural networks in much detail, though, when it comes to classifying Smishing messages.…”

Section: Introductionmentioning

confidence: 99%

Uncovering SMS Spam in Swahili Text Using Deep Learning Approaches

Mambina,

Ndibwile,

Uwimpuhwe

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In today's communications, Short Message Service (SMS) and Internet protocol-based messaging systems are the most widely used channels. These services are currently the target of an unprecedented number of threats due to their rising appeal. Some spammers have more nefarious motives, even though most spam stems from businesses seeking to promote their products. In recent years, as new security threats such as Smishing have emerged, the amount of SMS spam has experienced substantial growth. The scientific community has paid less attention to SMS spam than email spam despite the fact that both are extensively utilized. SMS spam presents extra processing hurdles. These include the use of lexical variants, SMS-like contractions, or sophisticated obfuscations, which degrade the performance of conventional filtering solutions. In this study, we investigate the efficacy of deep-learning models for filtering Swahili SMS spam based on linguistics and behavioral patterns using a real-world dataset from telecommunications companies in Tanzania. To validate the effectiveness of our models we subjected them to the UCI dataset of spam messages written in English. The models were trained and tested with 10 k-fold cross validation. The experimental results show that CNN-LSTM-LSTM hybrid model attained the highest accuracy of 99.98 on Swahili dataset while CNN-BiLSTM performed better on UCI dataset with an accuracy of 98.38.

show abstract

“…Owing to the intricacy of conducting experiments aimed at testing driving behaviors and the significant expenses entailed in data collection, particularly for heavy-duty freight vehicles, the development of a model capable of achieving high performance in accurately discerning driving behavior patterns from a limited dataset presents a formidable challenge [6]. In contemporary times, a growing trend involves the concept of smart cities, which advocate for the integration of sensors in vehicles to enhance intercommunication among various vehicle types [7]. The transportation industry's strategic focus on performance optimization and cost reduction has propelled the integration of advanced technologies, specifically the Internet of Things (IoT) and ML.…”

mentioning

confidence: 99%

Elevating Driver Behavior Understanding With RKnD: A Novel Probabilistic Feature Engineering Approach

Shariful Islam,

Abu Tareq Rony,

Safran

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Early detection of driver behavior is a pivotal aspect in enhancing road safety, focusing on identifying and mitigating risky driving patterns before they lead to accidents. The use of smartphone sensors for data acquisition marks a significant advancement in this field. It allows for continuous, real-time monitoring of driving patterns without the need for specialized equipment. In this study, we leverage a publicly available smartphone motion sensor dataset, utilizing accelerometer and gyroscope data from a Samsung Galaxy S21 to analyze driving behaviors classified as slow, normal, and aggressive. This research introduces a novel feature engineering technique named the RKnD (Random forest, K-nearest classifier, Decision tree) probabilistic feature engineering technique, which integrates three prominent machine learning (ML) models. This blend offers a robust analysis of driver behavior, leveraging the strengths of each algorithm. This paper emphasizes the importance of data balancing in machine learning, employing the Synthetic Minority Oversampling Technique (SMOTE) to enhance the reliability of the predictions. Furthermore, k-fold crossvalidation is used to ensure the model's consistency and accuracy across original features and the proposed RKnD probabilistic features of the data sets. By achieving such high accuracy, the study demonstrates the potential of smartphone-based systems to significantly improve road safety. This paper introduces a novel approach utilizing smartphone motion sensor data to detect driver behaviors with a remarkable accuracy rate of 99.63%. This research stands out for its application of machine learning techniques in a practical, accessible manner. This pioneering approach named RKnD feature engineering sets a new standard in the realm of smart transportation systems, opening avenues for further innovations in the field, and filling a gap in road safety analysis to avoid road accidents. Future research on RKnD should streamline its algorithm for real-time use, diversify datasets, integrate advanced Deep Learning for complex pattern detection, and undertake real-world testing to validate practicality and uncover challenges.

show abstract

Improved Deep Learning Performance for Real-Time Traffic Sign Detection and Recognition Applicable to Intelligent Transportation Systems

Cited by 5 publications

References 0 publications

Proposal of a reliable embedded circuit to control a stepper motor using microblaze soft-core processor

Proposal of a reliable embedded circuit to control a stepper motor using microblaze soft-core processor

Uncovering SMS Spam in Swahili Text Using Deep Learning Approaches

Elevating Driver Behavior Understanding With RKnD: A Novel Probabilistic Feature Engineering Approach

Contact Info

Product

Resources

About