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To achieve low‐carbon sustainable energy development, new technologies such as Internet of Energy (IoE), intelligent systems and Internet of Things (IoT) as well as distributed energy generations via smart grids (SG) are gaining attention. The interoperability between intelligent energy systems, realised through the web, enables automatic consumption optimisation and increases network efficiency and intelligent management. IoE is an intriguing topic in close connection with the IoT, communication systems, SG and electrical mobility that contributes to energy efficiency to achieve zero‐carbon technologies and green environments. Furthermore, nowadays, the widespread growth and utilisation of processors for mining digital currency in homes and small warehouses are some other factors to be considered in terms of electric energy consumption and greenhouse gas emission. However, research on the use of the Internet for evaluating the misallocation of energy and the effect it can have on CO2 emissions is often neglected. In this study, the authors present a detailed overview regarding the evolution of SG in conjunction with the employment of IoE systems as well as the essential components of IoE for decarbonisation. Also, mathematical models with simulation are provided to evaluate the role of IoE in reducing CO2 emission. In this study, we present a detailed overview regarding the evolution of smart grids towards modern Internet energy systems. We present the essential components of Internet of Energy (IoE) for decarbonisation in the future of energy sector. Also, some models and equations are provided to evaluate the effects of IoE and the misallocation of energy on CO2 in different regions.

Accurately predicting building energy consumption is essential for optimizing energy management, sustainability strategies, and operational efficiency. This study proposes a novel hybrid forecasting model that integrates wavelet decomposition for feature extraction, Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, and Support Vector Regression (SVR) for refined estimates, with all model parameters optimized via a Developed Henry Gas Solubility Optimization (DHGSO) algorithm. The dataset comprises two years of hourly energy consumption data from seven campuses, providing a robust foundation for validation. The proposed method achieves a 20% reduction in RMSE and a 15% reduction in MAPE compared to standalone LSTM and SVR models. This performance demonstrates the benefits of jointly leveraging decomposition-based feature engineering, deep learning, and advanced metaheuristic optimization. The results emphasize the method’s potential for supporting proactive demand response, accurate budget planning, renewable energy integration, and efficient equipment maintenance in large-scale building energy management systems.

Using blockchain technology as one of the new methods to enhance the cyber and physical security of power systems has grown in importance over the past few years. Blockchain can also be used to improve social welfare and provide sustainable energy for consumers. In this article, the effect of distributed generation (DG) resources on the transmission power lines and consequently fixing its conjunction and reaching the optimal goals and policies of this issue to exploit these resources is investigated. In order to evaluate the system security level, a false data injection attack (FDIA) is launched on the information exchanged between independent system operation (ISO) and under-operating agents. The results are analyzed based on the cyber-attack, wherein the loss of network stability as well as economic losses to the operator would be the outcomes. It is demonstrated that cyber-attacks can cause the operation of distributed production resources to not be carried out correctly and the network conjunction will fall to a large extent; with the elimination of social welfare, the main goals and policies of an independent system operator as an upstream entity are not fulfilled. Besides, the contracts between independent system operators with distributed production resources are not properly closed. In order to stop malicious attacks, a secured policy architecture based on blockchain is developed to keep the security of the data exchanged between ISO and under-operating agents. The obtained results of the simulation confirm the effectiveness of using blockchain to enhance the social welfare for power system users. Besides, it is demonstrated that ISO can modify its polices and use the potential and benefits of distributed generation units to increase social welfare and reduce line density by concluding contracts in accordance with the production values given.

Skin cancer is a type of disease in which malignant cells are formed in skin tissues. However, skin cancer is a dangerous disease, and an early detection of this disease helps the therapists to cure this disease. In the present research, an automatic computer-aided method is presented for the early diagnosis of skin cancer. After image noise reduction based on median filter in the first stage, a new image segmentation based on the convolutional neural network optimized by satin bowerbird optimization (SBO) has been adopted and its efficiency has been indicated by the confusion matrix. Then, feature extraction is performed to extract the useful information from the segmented image. An optimized feature selection based on the SBO algorithm is also applied to prune excessive information. Finally, a support vector machine classifier is used to categorize the processed image into the following two groups: cancerous and healthy cases. Simulations have been performed of the American Cancer Society database, and the results have been compared with ten different methods from the literature to investigate the performance of the system in terms of accuracy, sensitivity, negative predictive value, specificity, and positive predictive value.

One of the most dangerous cancers in humans is Melanoma. However, early detection of melanoma can help us to cure it completely. This paper presents a new efficient method to detect malignancy in melanoma via images. At first, the extra scales are eliminated by using edge detection and smoothing. Afterwards, the proposed method can be utilized to segment the cancer images. Finally, the extra information is eliminated by morphological operations and used to focus on the area which melanoma boundary potentially exists. To do this, World Cup Optimization algorithm is utilized to optimize an MLP neural Networks (ANN). World Cup Optimization algorithm is a new meta-heuristic algorithm which is recently presented and has a good performance in some optimization problems. WCO is a derivative-free, Meta-Heuristic algorithm, mimicking the world’s FIFA competitions. World cup Optimization algorithm is a global search algorithm while gradient-based back propagation method is local search. In this proposed algorithm, multi-layer perceptron network (MLP) employs the problem’s constraints and WCO algorithm attempts to minimize the root mean square error. Experimental results show that the proposed method can develop the performance of the standard MLP algorithm significantly.

There are great deals of consumer photographs which are affected by red-eye artifacts and arise frequently when shooting with flash. In this paper, a new technique is proposed to solve this problem. The proposed technique starts by detecting the skin-like regions using an optimized pixel-based neuro-fuzzy processing; morphological operations are then used to discard the extra areas after crossing the threshold. Once the skin regions are detected, five new features including geometric and color metrics are proposed to enhance the classification accuracy of the red-eye artifacts. After that, another optimized neuro-fuzzy classifier is employed to classify the red-eye regions by using the presented features. Final result is achieved by a definite syntax between skin and red-eye regions, and then, a simple correction method is used to correct the detected regions. Finally, a comparison is performed among the proposed method toward the other popular procedures and also a simple neuro-fuzzy. Final results showed the high performance of the proposed method.

The dramatic increase in the number of IoT devices has dramatically increased the attack surface, and thus it needs intrusion detection systems (IDS) with high-performance and the ability to detect potential threats in real-time with limited resources. This research is aimed primarily to offer a new methodology to apply the artifivial intelligence to place a new format upon intelligent intrusion detection. To solve this, the current paper comes up with a new deep intelligent IDS architecture which combines a lightweight Xception neural network with a Modified Pelican Optimization Algorithm (MPOA) in the tuning of hyperparameters and designing of architectures. The MPOA improves convergence, reduces the local optima entrapment and adapts the Xception model to meet the traffic characteristics in the IoT. The proposed framework, evaluated on three benchmark sets (IoT-23, UNSW-NB15, and CICIDS2017) has average F1-scores of 97.8, 96.1 and 97.7, respectively which consistently outperform state of art baselines such as the Random Forest (F1: 89.5%), SVM (F1: 88.2%), LSTM (F1: 93.1%), CNN (F1: 94.5%) and Autoencoder. It is also worth noting that the model has better identification of IoT-specific attacks (e.g., 98.9% F1 with Brute Force on the IoT-23) with low false positive rates (≤ 2.1%) and low computational performance, making it an appropriate model in terms of real time application in IoT contexts that are limited in resources. The study, therefore, provides the effectiveness of synergizing depthwise-separable CNNs with bio-inspired optimization to provide scalable, accurate, and adaptive IoT security.

This article proposes a new integrated diagnostic system for islanding detection by means of a neuro‐fuzzy approach. Islanding detection and prevention is a mandatory requirement for grid‐connected distributed generation (DG) systems. Several methods based on passive and active detection scheme have been proposed. Although passive schemes have a large non‐detection zone (NDZ), concern has been raised on active method due to its degrading power‐quality effect. Reliably detecting this condition is regarded by many as an ongoing challenge as existing methods are not entirely satisfactory. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. In addition, this technique can also overcome the problem of setting the detection thresholds inherent in the existing techniques. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro‐fuzzy inference system) for islanding detection. This approach utilizes rate of change of frequency (ROCOF) at the target DG location and used as the input sets for a neuro‐fuzzy inference system for intelligent islanding detection. This approach utilizes the ANFIS as a machine learning technology and fuzzy clustering for processing and analyzing the large data sets provided from network simulations using MATLAB software. To validate the feasibility of this approach, the method has been validated through several conditions and different loading, switching operation, and network conditions. The proposed algorithm is compared with the widely used ROCOF relays and found working effectively in the situations where ROCOF fails. Simulation studies showed that the ANFIS‐based algorithm detects islanding situation accurate than other islanding detection algorithms. © 2014 Wiley Periodicals, Inc. Complexity 21: 10–20, 2015

In this article, a new methodology based on fuzzy proportional‐integral‐derivative (PID) controller is proposed to damp low frequency oscillation in multimachine power system where the parameters of proposed controller are optimized offline automatically by hybrid genetic algorithm (GA) and particle swarm optimization (PSO) techniques. This newly proposed method is more efficient because it cope with oscillations and different operating points. In this strategy, the controller is tuned online from the knowledge base and fuzzy interference. In the proposed method, for achieving the desired level of robust performance exact tuning of rule base and membership functions (MF) are very important. The motivation for using the GA and PSO as a hybrid method are to reduce fuzzy effort and take large parametric uncertainties in to account. This newly developed control strategy mixed the advantage of GA and PSO techniques to optimally tune the rule base and MF parameters of fuzzy controller that leads to a flexible controller with simple structure while is easy to implement. The proposed method is tested on three machine nine buses and 16 machine power systems with different operating conditions in present of disturbance and nonlinearity. The effectiveness of proposed controller is compared with robust PSS that tune using PSO and the fuzzy controller which is optimized rule base by GA through figure of demerit and integral of the time multiplied absolute value of the error performance indices. The results evaluation shows that the proposed method achieves good robust performance for a wide range of load change in the presents of disturbance and system nonlinearities and is superior to the other controllers. © 2014 Wiley Periodicals, Inc. Complexity 21: 78–93, 2015

In this paper, a passive neuro-wavelet based islanding detection technique for grid-connected inverter-based distributed generation was developed. The weight parameters of the neural network were optimized by intelligent water drop (IWD) to improve the capability of the proposed technique in the proposed problem. The proposed method utilizes and combines wavelet analysis and artificial neural network (ANN) to detect islanding. Connecting distributed generator to the distribution network has many benefits such as increasing the capacity of the grid and enhancing the power quality. However, it gives rise to many problems. This is mainly due to the fact that distribution networks are designed without any generation units at that level. Hence, integrating distributed generators into the existing distribution network is not problem-free. Unintentional islanding is one of the encountered problems. Discrete wavelet transform (DWT) is capable of decomposing the signals into different frequency bands. It can be utilized in extracting discriminative features from the acquired voltage signals. In passive schemes with a large non-detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. The simulation results from Matlab/Simulink shows that the proposed method has a small non-detection zone, and is capable of detecting islanding accurately within the minimum standard time.