DroneOptiNet: A Framework for Optimal Drone-based Load Redistribution Mechanism for 5G and Beyond Solar Small Cell Networks

The power requirements posed by the fifth-generation and beyond cellular networks are an important constraint in network deployment and require energy-efficient solutions. In this work, we propose a novel user load transfer approach using airborne base stations (BS), mounted on drones, for reliable and secure power redistribution across the micro-grid network comprising green small cell BSs. Depending on the user density and the availability of an aerial BS, the energy requirement of a cell with an energy deficit is accommodated by migrating the aerial BS from a high-energy to a low-energy cell. The proposed hybrid drone-based framework integrates long short-term memory with unique cost functions using an evolutionary neural network for drones and BSs, and efficiently manages energy and load redistribution. The proposed algorithm reduces power outages at BSs and maintains consistent throughput stability, thereby demonstrating its capability to boost the reliability and robustness of wireless communication systems.

Image contrast enhancement using fuzzy logic

Image enhancement is a method of improving the quality of an image and contrast is a major aspect. Traditional methods of contrast enhancement like histogram equalization results in over/under enhancement of the image especially a lower resolution one. This paper aims at developing a new Fuzzy Inference System to enhance the contrast of the low resolution images overcoming the shortcomings of the traditional methods. Results obtained using both the approaches are compared.

Various Approaches to Aspect-based Sentiment Analysis

The problem of aspect-based sentiment analysis deals with classifying sentiments (negative, neutral, positive) for a given aspect in a sentence. A traditional sentiment classification task involves treating the entire sentence as a text document and classifying sentiments based on all the words. Let us assume, we have a sentence such as "the acceleration of this car is fast, but the reliability is horrible". This can be a difficult sentence because it has two aspects with conflicting sentiments about the same entity. Considering machine learning techniques (or deep learning), how do we encode the information that we are interested in one aspect and its sentiment but not the other? Let us explore various pre-processing steps, features, and methods used to facilitate in solving this task.