Urban planning faces increasingly complex challenges, marked by rapid urbanization and the need for sustainable development. Recognizing these challenges, our research focuses on semantic segmentation in aerial imagery. In our research, we identify challenges such as inadequate delineation of environmental features and the difficulty of obtaining critical insights from aerial imagery. To tackle these issues, we propose a state-of-the-art solution employing a modified Inception-ResNet-V2 U-Net architecture. Our research encompasses a comprehensive methodology that includes meticulous dataset preparation, bespoke model architecture design, refined loss function optimization, and robust training protocols augmented by data enrichment techniques. Our results showcase an impressive model accuracy of 84.6%, underlining the method's superior performance in accurately delineating environmental features from aerial imagery. This land use and land cover classification empowers urban planners and developers with critical insights, facilitating informed decision-making and sustainable urban development. Our paper also focuses on contour detection performed on natural vegetation which is one of the crucial aspect for urban planning. This approach offers a tool that can be used in real time for urban planning and improved accuracy of environmental feature delineation.

Fuzzy clustering is a common way to divide data into groups. Even though it has been improved a lot, fuzzy clustering still has problems while clustering real high-dimensional Big Data with complicated latent distributions. To solve this problem, this study comes up with a way to represent the data in a feature space that was built from a scalable deep neural network using Apache Spark on HPC. In this paper, we proposed SDnnRSIO-FCM, a Scalable Deep Neural Network Random Sampling Iterative Optimization-FCM clustering algorithm, and the SDnnLFCM, a scalable version of the Deep Neural Network Literal Fuzzy c-Means algorithm. We focus on the design and implementation of the proposed SDnnRSIO-FCM and SDnnLFCM algorithms using the Apache Spark cluster in a High-Performance Computing (HPC) environment by representing the data in a feature space produced by the neural network to handle Big Data. First, data is mapped into new feature space to aid in the reconstruction of the original data by providing a good representation. Second, scalable fuzzy clustering is embedded with neural networks to propose deep fuzzy clustering methods. The experimental results conducted on two huge benchmark datasets show that the SDnnRSIO-FCM algorithm outperforms the SDnnLFCM algorithm in terms of Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and F-score. Furthermore, the proposed SDnnRSIO-FCM applied to huge soybean protein sequences in comparison with SDnnLFCM shows a significant improvement in terms of Silhouette index (SI), Davies-Bouldin index (DBI), and Calinski-Harabasz index (CHI).

In real life, there are many applications where we need to take care of multiple parameters to get the optimized result. Similarly, many scientific and engineering problems require optimization of various parameters to get desired results. Many algorithms work well with a few variables to get optimized results, but on increasing the number of variables, they do not perform well. In this paper, we proposed an advanced quantum-inspired evolutionary algorithm (A-QEAM) to solve optimization problems where the tuning of multiple parameters or variables is required. A-QEAM is characterized by the principle of quantum computing such as superposition and qubit. This algorithm uses a qubit in place of the classical bit. The proposed algorithm is tested on mathematical functions consisting of 2 variables, 10 variables, 30 variables, and 50 variables. The result shows that the proposed algorithm performs well even on increasing the number of variables.

The Internet has resulted in cyber-threats and cyber-crimes, which can occur anywhere at any time. Among various cyber threats, modern malware with applied metamorphosis and polymorphic technology is a concern as it can proliferate to advanced variants from its original shape. The typical malware analysis methods, including signature-based approach, remain vulnerable to such advanced variants. This paper proposes a visualization-based approach for malware analysis using the state-of-the-art Convolution Neural Network (CNN) model such as ResNeXt, which had achieved outstanding performance in image classifications with competitive computational complexity. The proposed method transforms the attributes of raw malware binary executable files to greyscale images for further analysis by well-established deep learning models. The greyscale images, which result of data transformation for visualization, are classified using ResNeXt. The experiment results show that the proposed solution achieves 98.32% and 98.86% of accuracy in malware classification on Malimg dataset and modified Malimg dataset, respectively. The proposed method outperforms other comparable methods in terms of classification accuracy and requires similar level of computational power.

In this paper, a Quantum-Inspired Evolutionary Fuzzy C-Means (QIE-FCM) algorithm is proposed. The proposed approach find the true number of clusters and the appropriate value of weighted exponent (m) which is required to be known in advance to perform clustering using Fuzzy C-Means (FCM) algorithm. However, the selection of inappropriate value of m and C may lead the algorithm to converge to the local optima. To address the issue of selecting the appropriate value of m and corresponding value of C. In QIE-FCM, the quantum concept is used in classical computer where m is represented in terms of quantum bits (qubits). The QIE-FCM is based on generations. At each generation (g), quantum gates are used to generate a new value of m. For each generated value of m, FCM algorithm is executed by varying values of C. Then, corresponding to m value appropriate value of C is identified by evaluating local fitness function for generation g. To achieve the global best value of m and C, the global fitness function is evaluated by comparing the local best fitness value in current generation with the best fitness value obtained among all the previous generations. To judge the efficacy of QIE-FCM algorithm, it is compared with two well-known indices and three evolutionary fuzzy based clustering algorithm and their performance is evaluated on four benchmark datasets. Furthermore, the sensitivity of QIE-FCM is also experimentally investigated in this paper.

In this paper a quantum based binary neural network algorithm is proposed, named as Advance Quantum based Binary Neural Network Learning Algorithm (AQ-BNN). It forms neural network structure constructively by adding neurons at hidden layer. The connection weights and separability parameter are decided using quantum computing concept. Constructive way of deciding network not only eliminates over-fitting and underfitting problem but also saves time. The connection weights have been decided by quantum way, it gives large space to select optimal weights. A new parameter that is quantum separability is introduced here which find optimal separability plane to classify input sample in quantum way. For each connection weights it searches for optimal separability plane. Thus the best separability plane is found out with respect to connection weights. This algorithm is tested with three benchmark data set and produces improved results than existing quantum inspired and other classification approaches.