The proper handling of waste is one of the biggest challenges of modern society. Municipal Solid Waste (MSW) requires categorization into a number of types, including bio, plastic, glass, metal, paper, etc. The most efficient techniques proposed by researchers so far include neural networks. In this paper, a detailed summarization was made of the existing deep learning techniques that have been proposed to classify waste. This paper proposes an architecture for the classification of litter into the categories specified in the benchmark approaches. The architecture used for classification was EfficientNet-B0. These are compound-scaling based models proposed by Google that are pretrained on ImageNet and have an accuracy of 74% to 84% in top-1 over ImageNet. This research proposes EfficientNet-B0 model tuning for images specific to particular demographic regions for efficient classification. This type of model tuning over transfer learning provides a customized model for classification, highly optimized for a particular region. It was shown that such a model had comparable accuracy to that of EfficientNet-B3, however, with a significantly smaller number of parameters required by the B3 model. Thus, the proposed technique achieved efficiency on the order of 4X in terms of FLOPS. Moreover, it resulted in improvised classifications as a result of fine-tuning over region-wise specific litter images.

Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.

The widespread adoption of advanced technologies may be responsible for the extensive dissemination of forged photographs and videos on the Internet. This could potentially result in the proliferation of fraudulent identities online, raising safety concerns in society. The traditional method for detecting forgery, commonly referred to as the classical forgery method, lacks the capability to accurately identify such fraudulent activities. This limitation arises because these algorithms are trained on publicly available centralized datasets and do not prioritize privacy and security considerations. Consequently, they adversely affect the ability to detect counterfeit content. As a potential solution to this problem, we employed a highly effective deep learning methodology rooted in federated learning. We introduced a novel deep learning approach that combines features to assess the authenticity of photographs and videos shared on social media platforms. The proposed model was trained using three widely recognized forensic datasets: FaceForensics++, Deepforensic-1.0, and WildDeepfake. Visual features were extracted using two widely recognized deep learning approaches, namelyInception and Xception. These features were then combined into a feature vector using Canonical Correlation Analysis, and Convolutional Neural Networks were trained on these features to identify manipulated images and videos. The experiments were carried out with publicly available datasets and involved changing several parameters. Finally, the proposed model’s performance was compared with other deep learning models within federated learning environments to identify forgeries. Our proposed approach demonstrated exceptional performance, achieving an accuracy rate of 98.99% when evaluated on the merged dataset.

This paper presents a well-integrated system combining photovoltaic (PV) energy harvesting and Wireless Power Transfer (WPT) technology to develop a Solar Wireless Electric Vehicle Charging System (SWEVCS). With the growing adoption of Electric Vehicles (EVs), the demand for efficient and sustainable charging infrastructure has become a critical issue. The proposed system utilizes photovoltaic panels as a clean renewable energy source to charge EVs, eliminating the need for physical cables. The system performance is evaluated using MATLAB simulations, considering key parameters, such as solar irradiance, power output, battery State of Charge (SOC), charging current, and voltage. The results indicate a peak power output of approximately 500 W during midday, and a high SOC of up to 100% by late afternoon. The charging current reaches almost 5 A, demonstrating the high system’s efficiency in wireless energy transfer/WPT. The concerns of this study are the prospects of SWEVCS in minimizing reliance on the power grid while promoting Renewable Energy Solutions (RES) for EV charging. Future work will address scalability challenges and further improvement of WPT efficiency to advance this innovative charging technology.

In today’s world, being the easy availability of the internet, almost everyone is connected. Every individual for their daily news relies on online resources, w