Volume-12 , Issue-1 , Jan - 2024 Go Back

With the rise of technological advancements, various clinical practices have undergone significant transformations. The field of dermatology, in particular, has experienced rapid progress. Skin ailments encompass a spectrum of conditions impacting the human body`s largest organ. These conditions range in severity from mild instances like acne or eczema to more serious cases such as skin cancer or Psoriasis, which is a persistent inflammatory skin disorder affecting a considerable global population. The precise categorization and assessment of the severity of Psoriasis play a pivotal role in its effective treatment and management. Conventional classification methodologies often prove subjective, time-intensive, and susceptible to variations in interpretation amongst different observers. In contrast, machine learning and deep learning, subsets of artificial intelligence, are revolutionizing various domains by addressing diverse challenges autonomously, without the need for human intervention. AI technologies have opened up fresh avenues for the objective and automated classification of Psoriasis. However, these technologies are yet to attain their maximum potential in terms of accuracy. Here, we try to implement a relatively new method i.e., YOLO (You Only Look Once) which is basically an object detection technique, to try to classify psoriasis. A comparison of all the different models of YOLOv8 have been studied here. The study also deploys the Google Colab platform for computational needs and ease.

Psoriasis, Classification, YOLOv8, Artificial Intelligence, Deep Learning, Dermatology, Google Colab

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Anushree Goswami, Nidhi Sharma, "A YOLO-Powered Deep Learning Approach to Psoriasis Classification," International Journal of Computer Sciences and Engineering, Vol.12, Issue.1, pp.1-7, 2024.

Now a days, based on different reasons heart diseases are increasing rapidly. If we find out or identify the heart diseases in human beings at an early stage, it is easy to prevent the disease and help the patients. Even though cardiologists and health centers gather relevant data and information every day, but, not applying the knowledge of machine learning algorithms to retrieve valuable of prediction. The main objective of this research is to predict and classify heart diseases by using proposed convolutional neural network classifier. In this classification of evaluation process, feed forward process and back propagation methods will be applied in between the hidden layers. Due to this, the proposed CNN classifier gives best accuracy. By applying this trained classifier has identified the given data, which are either normal or abnormal. So, the entire research has been implemented in Python which produced good results.

Deep Learning, Classification, Convolutional Neural Network, Heart Disease

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[3] Tsehay Admassu Assegie, “Heart disease prediction model with k-nearest neighbor algorithm”, International Journal of Informatics and Communication Technology (IJ-ICT), ISSN: 2252-8776, DOI: 10.11591/ijict.v10i3, Vol.10, No.3, pp.225-230, December 2021.
[4] An Dinh, Stacey Miertschin, Amber Young, and Somya D Mohanty. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC medical informatics and decision making, Vol.19, Issue.1, pp.2-11, 2019.
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V. Krishnaiah, "Heart Disease Prediction System Using Convolutional Neural Networks," International Journal of Computer Sciences and Engineering, Vol.12, Issue.1, pp.8-15, 2024.

Fast transportation systems and rapid transit play pivotal roles in the economic development of any nation. However, mismanagement and the resulting traffic congestion can lead to prolonged waiting times, increased fuel consumption, and financial losses. Although numerous traffic management techniques exist to address congestion, none is inherently flawless, given the constantly changing real-time situations. The primary cause of today`s traffic problems often lies in the shortcomings of existing traffic management systems. These systems often lack a focus on real-time traffic scenarios, resulting in inefficiencies. Our initiative seeks to bridge this gap by introducing a self adjusting traffic management strategy capable of seamlessly adapting to the ever-changing circumstances on the road. Traffic congestion and road safety are persistent challenges in urban areas, necessitating the development of robust Traffic Management Systems (TMS). This abstract provides an overview of a comprehensive TMS designed to address these challenges and improve overall urban mobility.

Image Processing, Signal Processing, Sensor and Measurement Techniques, YOLO model, Radio Frequency Identification, Convolutional Neural Networks, Blob Detection

[1] Zhao-Xia Yang, Ming-Hua Zhu, "A Dynamic Prediction Model of Real-Time Link Travel Time Based on Traffic Big Data," IEEE Transactions on Intelligent Transportation Systems, Vol.20, Issue 8, pp.2843-2853, 2019.
[2] Ayushi Gupta, Dr. Charu Gandhi, Vartika Katara, Stuti Brar, "Real-time video monitoring of vehicular traffic and adaptive signal change using Raspberry Pi," IEEE Transactions on Vehicular Technology, 2020.
[3] Akanksha Soni, Arun Pratap Singh, "Automatic Motorcyclist Helmet Rule Violation Detection using TensorFlow & Keras in OpenCV," IEEE Access, Vol.8, pp.123456-123465, 2020.
[4] Mihir M. Gandhi, Devansh S. Solanki, Rutwij S. Daptardar, "Smart Control of Traffic Light Using Artificial Intelligence," IEEE Transactions on Intelligent Transportation Systems, 2020.
[5] Mouna Zouari Mehdi, Habib M. Kammoun, "Entropy-Based Traffic Flow Labeling for CNN Based Traffic Congestion Prediction From Meta-Parameters," IEEE Transactions on Intelligent Transportation Systems, 2022.
[6] Indrabayu, Rizki Yusliana Bakti, Intan Sari Areni, A. Ais Prayogi, "Vehicle Detection and Tracking using Gaussian Mixture Model and Kalman Filter," Proceedings of the 2016 International Conference on Computational Intelligence and Cybernetics, Makassar, Indonesia, pp.22-24, November 2016.
[7] Li Xun, Nan Kaikai, Liu Yao, Zuo Tao, "A Real-Time Traffic Detection Method Based on Improved Kalman Filter," Proceedings of the 2018 3rd International Conference on Robotics and Automation Engineering (ICRAE), Guangzhou, China, pp.17-19 November 2018.
[8] Safoora Maqbool, Mehwish Khan, Jawaria Tahir, Abdul Jalil, Ahmad Ali, Javed Ahmad, "Vehicle Detection, Tracking and Counting," Proceedings of the 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP), Shenzhen, China, pp.13-15, July 2018.
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[10] Jess Tyron G. Nodado, Hans Christian P. Morales, Ma Angelica P. Abugan, Jerick L. Olisea, Angelo C. Aralar, Pocholo James M. Loresco, "Intelligent Traffic Light System Using Computer Vision with Android Monitoring and Control," Proceedings of TENCON2018 - IEEE Region 10 Conference, Jeju, 2018.
[11] Sayan Mondal, Alan Yessenbayev, Jahya Burke, Nihar Wahal, "A Survey of Information Acquisition in Neural Object Detection Systems," Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, Canada, 2018.

Arpitha Vasudev, Shilpa P., Shashank K.P., Smitha B.C., Tanzeer H.M., "Exploring the Functionality of Traffic Control Systems: A Brief Review," International Journal of Computer Sciences and Engineering, Vol.12, Issue.1, pp.16-23, 2024.