One of the key features of mobile networks in this age of mobile communication is seamless communication. Handover (HO) is a critical component of next-generation (NG) cellular communication networks, which requires careful management since it poses several risks to quality-of-service (QoS), including a decrease in average throughput and service disruptions. Due to the dramatic rise in base stations (BSs) and connections per unit area brought about by new fifth-generation (5G) network enablers, such as Internet of things (IoT), network densification, and mm-wave communications, HO management has become more challenging. The degree of difficulty is increased in light of the strict criteria that were recently published in the specifications of 5G networks. In order to address these issues more successfully and efficiently, this study has explored and examined intelligent HO optimization strategies using machine learning models. Furthermore, the significant goal of this review is to present the state of cellular networks as they are now, as well as to talk about mobility and home office administration in 5G alongside the overall features of 5G networks. This work presents an overview of machine learning methods in handover optimization and of the various data availability for evaluations. In the final section, the challenges and future research directions are also detailed.

Tumors are a deadly condition often triggered by a range of abnormal modifications and genetic abnormalities. Early tumor diagnosis is essential due to the highly concerned nature of the disease. Early detection and treatment of tumors can significantly reduce mortality rates. This paper presents a model for tumor segmentation in medical imaging that uses the U-NET architecture to increase precision. The model’s encoding and decoding processes have been applied with skip connections to boost performance while simplifying model training. Images were cropped around the lower abdominal regions, and all images used in the study were then resized to 256*256 pixels for standardization. The proposed model deals with the class imbalance using data augmentation and oversampling. The experiments achieved a dice score of 0.853±0.02; F-score of 0.905±0.02; and a sensitivity of 0.897±0.02, compared with various existing models. As part of the model’s application, the pytorch-lightning library is used to successfully identify lung cancer scans, thereby proving to be a precise and efficient method of tumor identification. Accordingly, the study emphasizes the accuracy and speed of the applied model as a useful instrument for the earliest detection of tumors. The proposed approach helps to achieve more relevant and accurate segmentation and thus provides enhancements in medical images analysis if such challenges as an imbalance data set are well handled.

KSII Transactions on Internet and Information Systems TIIS Paper Details

Smart systems and services are designed to support a variety of goals, including modern living, integrated societies, gig economic growth, knowledge-based workforces, manufacturing automation, expanding urban populations, and virtually real social behaviors. Smart services and systems must have a sophisticated feature set, including security, control, scaling, flexibility, intelligent behavior, and personalization, to meet these goals. Therefore, the secret to creating automated, intelligent, and smart systems that meet today's demands is modeling based on AI. Different forms of AI, including logical, working, dynamic, written, and graphical AI, can be used to improve an application's intelligence and capabilities to solve problems in the real world. However, because real-world issues and information are dynamic and varied, creating an efficient AI model is a difficult undertaking. In this work, we provide a thorough understanding of "AI-based Modelling," including the fundamentals and potential applications of AI techniques that can be crucial to the development of intelligent systems in a wide range of real-world application domains, such as security, smart cities, finance, business, medical care, and farming. We regard this effort as a first step towards drawing the interest of numerous academics to a new field of application, where wireless devices can improve the quality of life for a large number of people living in smart cities.