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A Review on Machine Learning Assisted Handover Mechanisms for Future Generation Wireless Networks

A Review on Machine Learning Assisted Handover Mechanisms for Future Generation Wireless Networks/strong>
Authors:-Priyanka Vishwakarma, Dr. Kamlesh Ahuja

Abstract-Machine Learning and Deep Learning Algorithms have been explored widely to identifyy potential avenues to optimize wireless networks. One such area happens to be a data driven model for initiating handover among multiple access techniques such as OFDM and NOMA. With increasing number of users and multimedia applications, bandwidth efficiency in cellular networks has become a critical aspect for system design. Bandwidth is a vital resource shared by wireless networks. Hence its in critical to enhance bandwidth efficiency. Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple access (NOMA) have been the leading contenders for modern wireless networks. NOMA is a technique in which multiple users data is separated in the power domain. A typical wireless system generally has the capability of automatic fall back or handover. In such cases, there can be a switching from one of the technologies to another parallel or co-existing technology in case of changes in system parameters such as Bit Error Rate (BER) etc. This paper presents a review on existing machine learning based approaches for handover prediction in future generation wireless networks. The salient features of each of the approaches has been highlighted along with identifying potential research gaps.

DOI: 10.61137/ijsret.vol.10.issue5.255

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Design of Cross Level Automatic Railway Gate Control System Using Arduino UNO 328

Design of Cross Level Automatic Railway Gate Control System Using Arduino UNO 328
Authors:-Ayodele J, Barakur C.A, Joel O.O

Abstract-This paper presents the design and construction of an obstacle detection system for railway level crossings. The focus of this research is on reducing accident rates attributed to obstructions between the gates of the level crossing. Research indicates that approximately 30% of railway accidents at level crossings are resulting from obstacles blocking the tracks. To address this issue, we developed a system utilizing an Arduino Uno microcontroller, along with ultrasonic and reed switch sensors, and a GSM module for real-time alerts. While the ultrasonic sensors are deployed to monitor the gate crossing arena, the reed switches are positioned 3km away from each gate to detect the arrival/departure of the train. Such that when there is any obstacle detected the GSM triggers sms alert to the train operators for a possible halt to create room for evacuation of the obstacle. By facilitating timely responses, this system aims to decrease the likelihood of accidents, thereby enhancing safety for both rail and road users. This innovative solution highlights the potential for improved safety measures within railway infrastructure.

DOI: 10.61137/ijsret.vol.10.issue5.254

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Malaysian Noodle Images Classification System Using CNN and Transfer Learning

Malaysian Noodle Images Classification System Using CNN and Transfer Learning/strong>
Authors:-Ibrahim Abba, Ubaid Mohammed Dahir, Mohammed Shettima

Abstract-Image Recognition is a term used to describe a set of algorithms and technologies that attempt to analyze images and understand the hidden representations of features behind them and apply these learned representations for different tasks like classifying images into distinct categories automatically, understanding which objects are present and where in an image, etc. These technologies leverage various traditional computer vision methods as well as machine learning and deep learning algorithms to achieve the required results for solving such problems. This paper shows a recognition model for classifying Malaysian Noodle images. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition were used for this task. The model uses a deep learning process that was trained on natural images (AlexNet and SqueezeNet dataset) and was fine-tuned to generate the predictive Noodle model, which comprised approximately 4308 images. The dataset was divided into ten groups/categories of Noodles images which include the following: Mee Bee Hoon Goreng, Mee Bee Hoon Sup, Mee Goreng, Mee Koay Teow Goreng, Mee Koay Teow Sup, Mee Laksa Goreng, Mee Laksa Sup, Mee Maggi Goreng, Mee Maggi Sup, Mee Sup. The trained model achieved high accuracy on the test set, demonstrating the feasibility of this approach.

DOI: 10.61137/ijsret.vol.10.issue5.253

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