The main problem for both the utility companies and the end-used is to efficiently schedule the home appliances using energy management to optimize energy consumption. The microgrid, macro grid, and Smart Grid (SG) are state-of-the-art technology that is user and environment-friendly, reliable, flexible, and controllable. Both utility companies and end-users are interested in effectively utilizing different heuristic optimization techniques to address demand-supply management efficiently based on consumption patterns. Similarly, the end-user has a greater concern with the electricity bills, how to minimize electricity bills, and how to reduce the Peak to Average Ratio (PAR). The Home Energy Management Controller (HEMC) is integrated into the smart grid, by providing many benefits to the end-user as well to the utility. In this research paper, we design an efficient HEMC system by using different heuristic optimization techniques such as Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO), to address the problem stated above. We consider a typical home, to have a large number of appliances and an on-site renewable energy generation and storage system. As a key contribution, here we focus on incentive-based programs such as Demand Response (DR) and Time of Use (ToU) pricing schemes which restrict the end-user energy consumption during peak demands. From the results figures, it is clear that our HEMC not only schedules all the appliances but also generates optimal patterns for energy consumption based on the ToU pricing scheme. As a secondary contribution, deploying an efficient ToU scheme benefits the end-user by paying minimum electricity bills, while considering user comfort, at the same time benefiting utilities by reducing the peak demand. From the graphs, it is clear that HEMC using GA shows better results than WDO and BPSO, in energy consumption and electricity cost, while BPSO is more prominent than WDO and GA by calculating PAR.

In this paper, a convolved feature vector based adaptive fuzzy filter is proposed for impulse noise removal. The proposed filter follows traditional approach, i.e., detection of noisy pixels based on certain criteria followed by filtering process. In the first step, proposed noise detection mechanism initially selects a small layer of input image pixels, convolves it with a set of weighted kernels to form a convolved feature vector layer. This layer of features is then passed to fuzzy inference system, where fuzzy membership degrees and reduced set of fuzzy rules play an important part to classify the pixel as noise-free, edge or noisy. Noise-free pixels in the filtering phase remain unaffected causing maximum detail preservation whereas noisy pixels are restored using fuzzy filter. This process is carried out traditionally starting from top left corner of the noisy image to the bottom right corner with a stride rate of one for small input layer and a stride rate of two during convolution. Convolved feature vector is very helpful in finding the edge information and hidden patterns in the input image that are affected by noise. The performance of the proposed study is tested on large data set using standard performance measures and the proposed technique outperforms many existing state of the art techniques with excellent detail preservation and effective noise removal capabilities.

The classification of hyperspectral images has become an essential task in agricultural analysis, as it assesses the quality, chemical composition, and overall health of produce. This study concentrates on the analysis of the hyperspectral images of apples to distinguish and classify pure apples and those subjected to fertilizers in different concentration levels. This classification is fundamental to ensuring food quality and optimizing fertilizer input and precision agriculture practices. The dataset did not initially satisfy requirements owing to the small sample size, which rendered it difficult to train any robust generalizable model. A conditional GAN variation (CGAN) has been proposed for data augmentation. This approach effectively handled class imbalance by generating high-quality, category-specific synthetic images that enriched the dataset with realistic and diverse samples. For classification, the ConvNeXt architecture was employed, integrated with the Simple Attention Module (SimAM) to enhance feature refinement and extraction. The SimAM module allowed the model to focus on the most relevant areas of hyperspectral images while suppressing unimportant information, leading to a highly refined representation of features. The proposed classification model achieved an impressive accuracy of 99.75%, showcasing its exceptional ability to distinguish between different categories of apples, including untreated ones and those with varying fertilizer concentrations. This high level of accuracy underscores the model’s robustness and reliability in agricultural image analysis.

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow; however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network.

Developing hyperspectral image classification models has long been hindered by problems regarding spectral redundancy and complexity, as well as the limitations of previously proposed transformer methods. We present AssociativeFormer, an architecture designed to address hyperspectral analysis by merging memory association and efficient spatial-spectral processing. New features of the architecture include: Sparse memory attention with Top-K Routing that reduces computational complexity from quadratic to linear scaling while retaining its ability to discriminate effectively. The versatile convolution tokenization allows the architecture to capture multi-scale spatial-spectral features and filters from dynamically generated convolution tokens. The associative memory provided by a Hopfield network allows real time storage and retrieval of patterns, while the Gompertz Linear Unit (GoLU) activation decreases impediments on the gradient flow to create a more useful representation of the features. The network's global workspace design preserves inter-layer information for independent flow and spatial-spectral dependency tracking. The AssociativeFormer model delivers state-of-the-art accuracy with linear complexity and competitive inference speed, providing a superior accuracy–efficiency trade-off compared to existing hyperspectral classification methods.

With the rapid growth and development of smart cities, many real-time systems have been designed to support the dynamic infrastructure of realistic and time-constrained networks. These systems integrate the Internet of Things (IoT), emerging technologies, and edge computing to meet the demands of diverse applications and fulfill community needs. Researchers have made significant efforts to develop autonomous systems for manipulating network data, providing timely responses in critical environments, and easing communication by enabling seamless device-to-device interaction. However, some significant research challenges still persist, including the lack of intelligence and security over unpredictable communication under malfunctioning conditions. Moreover, uneven load balancing across devices with continuous link interference results in network disconnection and delays in system responsiveness. In addition, the devices' heterogeneity and unreliable links make most existing edge-driven schemes complicate the process of optimal offloading. To address these issues, we introduce a joint optimization framework for energy-efficient, secured computation offloading and dynamic resource allocation in Edge-IoT infrastructure. It explores a Multi-Agent Reinforcement Learning to make adaptive offloading decisions and establishes a secure communication path with effective resource allocation under dynamic network interactions. Furthermore, it enhanced network stability by leveraging trusted, decentralized processing points at the network edge, mitigating energy holes, and improving workload management for distributed IoT ecosystems. Using an extensive set of simulations, the proposed framework is evaluated, and the results reveal substantial improvements over recent state-of-the-art solutions in terms of both security and energy efficiency.