Addressing the associated rise in Carbon Emissions (CE) as smart cities expand becomes paramount. Effective low-carbon urban planning demands robust, precise assessments. This research introduces a cutting-edge solution via an Artificial Intelligence (AI) -driven Carbon Footprint (CF) impact assessment. A detailed dataset, collected over 3 years, was harnessed to gather insights into vital urban factors, including CE, Energy Consumption (EC) patterns, variations in land use, transportation dynamics, and changes in air quality. The cornerstone of this research is developing the Multi-modal Stacked VAR-LSTM model. This model proposes to provide accurate CF predictions for urban environments by merging the capabilities of Vector Autoregression (VAR) with Long Short-Term Memory (LSTM) neural networks. The process encompasses dedicated assessments for each data segment, harnessing VAR to delineate interdependencies and refining these predictions with the LSTM network using the residuals from the VAR analysis. By interweaving AI-driven carbon footprint impact assessments into the urban planning discourse, this study underscores the vast potential in sculpting future urban development strategies that are sustainable and sensitive to carbon impact.

Security and threat identification have always been critical in the interconnected world we live in today, and with emerging and cutting-edge threats, they still remain vital. Consequently, this paper proposes an intrusion detection solution whereby GWO for feature selection to optimize the detection model and a CNN–LSTM for attack detection. This paper describes the potential threat attributed to high dimensionality and non-linearity of network traffic data and aims to improve the overall detection rate and accuracy of different types of cyber-attacks. The GWO algorithm efficiently addresses the selection of relevant features from the data, hence demystifying the data while enhancing the model’s impact. CNN-LSTM architecture adapted from convolution neural networks and long short-term memory nets which performed the best in feature recognizing on networks traffic, allows the detection model to get understanding about complicated patterns in traffic. The described approach is compared with common methods, including Logistic Regression, Gaussian Naive Bayes, Decision Tree, and Random Forest and outperforms each of those regarding precision, recall and F1-score. Evaluation of the results obtained indicates that the proposed system correctly identifies various types of attacks with high precision, and very sensitive to false positive, and scenes the DoS/DDoS, PortScan, BruteForce, Web Attacks, and Botnet attacks correctly and distinctly. This paper shows the efficiency of integrating the state-of-art optimization methods with DL-based models for the development of the sound and highly scalable intrusion detection framework. The proposed method provides a much higher level of security enhancement in the proposed network against the emerging threat of cyber-attacks on existing and new complex networks.

Let H_eff (V_eff, E_eff) be a finite simple connected graph of order m with vertex set V_eff and edge set E_eff. A dominating set $S_{ds}\subseteq V_{eff}$ is called an efficiently dominating set if, for every vertex $u_a\in V_G,\hspace{0.33em}\left|N_G\left[u_a\right]\cap S_{ds}\right|=1$—where NG [u_a] denotes the closed neighborhood of the vertex u_a. Using efficient domination techniques and labelling, we constructed the fuzzy network. An algorithm has been framed to encrypt and decrypt the secret information present in the network, and furthermore, the algorithm has been given in pseudocode. The mathematical modelling of a strong fuzzy network is defined and constructed to elude the burgeoning intruder. Using the study of the efficient domination of fuzzy graphs, this domination parameter plays a nuanced role in encrypting and decrypting the framed network. One of the main purposes of fuzzy networks is encryption, so one of our contributions to this research is to build a novel combinatorial technique to encrypt and decode the built-in fuzzy network with a secret number utilizing effective domination. An illustration with an appropriate secret message is provided along with the encryption and decryption algorithms. Furthermore, we continued this study in intuitionistic fuzzy networks.

This paper proposes a convolutional neural network (CNN) model of the signal distribution control algorithm (SDCA) to maximize the dynamic vehicular traffic signal flow for each junction phase. The aim of the proposed algorithm is to determine the reward value and new state. It deconstructs the routing components of the current multi-directional queuing system (MDQS) architecture to identify optimal policies for every traffic scenario. Initially, the state value is divided into a function value and a parameter value. Combining these two scenarios updates the resulting optimized state value. Ultimately, an analogous criterion is developed for the current dataset. Next, the error or loss value for the present scenario is computed. Furthermore, utilizing the Deep Q-learning methodology with a quad agent enhances previous study discoveries. The recommended method outperforms all other traditional approaches in effectively optimizing traffic signal timing.

Domination plays an indispensable role in graph theory. Various types of domination explore various types of applications. Equal-status people work together and interlace with each other easily. In this paper, the paired equitable domination of a graph, its inflated graph, and its complement of an inflated graph were studied. The relationship between the domination number of the graph, the equitable domination number, and the paired equitable domination number of complements of the inflated graph were established. Furthermore, we proved the Nordhaus–Gaddum-type inequality, that is, ${\gamma }_{pr}^e(H)+{\gamma }_{pr}^e(H)\le 6$ if $H$ is a graph with m nodes where $m\equiv 0, 2(mod 8)$ and $d(a_i)$ = (m/2) for all $a_i$. The challenges and limitations of this parameter of paired equitable and equitable domination depends on the degree of the vertex of the graph. Practical applications are discussed in various fields and illustrated using the studied parameter.