MOCF: A Multi-Objective Clustering Framework using an Improved Particle Swarm Optimization Algorithm

Traditional clustering algorithms, such as K-Means, perform clustering with a single goal in mind. However, in many real-world applications, multiple objective functions must be considered at the same time. Furthermore, traditional clustering algorithms have drawbacks such as centroid selection, local optimal, and convergence. Particle Swarm Optimization (PSO)-based clustering approaches were developed to address these shortcomings. Animals and their social Behaviour, particularly bird flocking and fish schooling, inspire PSO. This paper proposes the Multi-Objective Clustering Framework (MOCF), an improved PSO-based framework. As an algorithm, a Particle Swarm Optimization (PSO) based Multi-Objective Clustering (PSO-MOC) is proposed. It significantly improves clustering efficiency. The proposed framework's performance is evaluated using a variety of real-world datasets. To test the performance of the proposed algorithm, a prototype application was built using the Python data science platform. The empirical results showed that multi-objective clustering outperformed its single-objective counterparts

Solid Waste Supervision System based on Heuristic Algorithmic approach and Internet of Things

This research article emphasises on use of algorithmic approach to activate sensors to optimize waste disposal and internet of things technology to notify the trash collectors when it is time to clean the trash cans. Here, a heuristic algorithmic approach will serve as the universal alarm and an SMS will be sent to the cleaners' registered mobile numbers as the local alert. The registered higher officials will receive an SMS alert if cleaners don't finish cleaning by the deadline. The top and bottom of a trashcan are where the ultrasonic sensors will be placed as part of the research goals. Every second, the value of the sensed ultrasonic sensor will be stored in the cloud. If the trash can is full, the lid will automatically close as a local warning. There will be a global alert sent via SMS to the authorised cleaners and higher officials. The research objectives include placing the ultrasonic at top and bottom of a dustbin. The sensed ultrasonic sensor value will store in a cloud at every second. As a local alert the lid of the garbage will be closed automatically if the dustbin is full. Global alert as a SMS will send to the authorized cleaners and higher officials. To know where the trash can is, attach a GPS sensor there. The existing intelligent dustbin is equipped with a voice controller that is used to classify the garbage but is not connected to the internet. In the existing system, an IoT platform was used with the assistance of a computer terminal, an infrared sensor, and continuous monitoring of the root plan to empty the dustbin. The lead of a dustbin is closed and opened by a vibration switch in smart homes. Whether the trash is full or not, the intelligent trash can will only locally but not worldwide transmit an alarm. The smart waste tank will communicate with smart phones by sending local dustbin values but was not stored in the cloud for every second. In this garbage narrow band IoT module was used but not internet. Accordingly the IoT enabled dustbin by placing the ultrasonic sensors, GPS sensor it is capable to know whether the dustbin is full or empty and the status will be indicated as local alert and global alert. The local alert will be the automatic lid closing of a dustbin and the global alert will be a SMS with the location of a dustbin and the status as full