With the advancement of technology and time, people have always sought to solve problems in the most efficient and quickest way possible. Since the introduction of the cloud computing environment along with many different sub-substructures such as task schedulers, resource allocators, resource monitors, and others, various algorithms have been proposed to improve the performance of the individual unit or structure used in the cloud environment. The cloud is a vast virtual environment with the capability to solve any task provided by the user. Therefore, new algorithms are introduced with the aim to improve the process and consume less time to evaluate the process. One of the most important sections of cloud computing is that of the task scheduler, which is responsible for scheduling tasks to each of the virtual machines in such a way that the time taken to execute the process is less and the efficiency of the execution is high. Thus, this paper plans to propose an ideal and optimal task scheduling algorithm that is tested and compared with other existing algorithms in terms of efficiency, makespan, and cost parameters, that is, this paper tries to explain and solves the scheduling problem using an improved meta-heuristic algorithm called the Hybrid Weighted Ant Colony Optimization (HWACO) algorithm, which is an advanced form of the already present Ant Colony Optimization Algorithm. The outcomes found by using the proposed HWACO has more benefits, that is, the objective for reaching the convergence in a short period of time was accomplished; thus, the projected model outdid the other orthodox algorithms such as Ant Colony Optimization (ACO), Quantum-Based Avian Navigation Optimizer Algorithm (QANA), Modified-Transfer-Function-Based Binary Particle Swarm Optimization (MTF-BPSO), MIN-MIN Algorithm (MM), and First-Come-First-Serve (FCFS), making the proposed algorithm an optimal task scheduling algorithm.

The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. This model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning techniques. The data loader also builds mini-batches for training the models to reduce training time. Finally, optimization approaches help increase the overall model’s efficiency and lower computing costs. MDCN employed on the MangoLeafBD Dataset consists of a total of 4,000 images. Following the experimental results, the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput.

Cloud computing provides a diverse and adaptable resource pool over the internet, allowing users to tap into various resources as needed. It has been seen as a robust solution to relevant challenges. A significant delay can hamper the performance of IoT-enabled cloud platforms. However, efficient task scheduling can lower the cloud infrastructure’s energy consumption, thus maximizing the service provider’s revenue by decreasing user job processing times. The proposed Modified Chimp-Whale Optimization Algorithm called Modified ChimpWhale Optimization Algorithm (MCWOA), combines elements of the Chimp Optimization Algorithm (COA) and the Whale Optimization Algorithm (WOA). To enhance MCWOA’s identification precision, the Sobol sequence is used in the population initialization phase, ensuring an even distribution of the population across the solution space. Moreover, the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s positionupdating process. This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model. Simulated outcomes reveal that the new method outperforms the original MCWOA, especially in multi-damage detection scenarios. MCWOA excels in avoiding false positives and enhancing computational speed, making it an optimal choice for structural damage detection. The efficiency of the proposed MCWOA is assessed against metrics such as energy usage, computational expense, task duration, and delay. The simulated data indicates that the new MCWOA outpaces other methods across all metrics. The study also references the Whale Optimization Algorithm (WOA), Chimp Algorithm (CA), Ant Lion Optimizer (ALO), Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO).

Machine learning algorithms to identify and categorize cyber hazards have garnered attention recently. Malicious URLs, IP addresses, and files provide the greatest cybersecurity risks. This abstract describes how machine learning-based classification of dangerous URLs, IP addresses, and files identifies and mitigates threats. The article begins with hazardous URL categorization, which uses machine learning to identify if a URL is risky depending on its attributes. URL classification is challenging since there are many legitimate URLs. Researchers have developed many machine learning algorithms that assess URL domain names, length, and keywords to identify fraudulent URLs. Second, machine learning-based IP address classification is discussed. Classifying IP addresses helps identify and combat DDoS and botnet attacks. To discover rogue IPs, machine learning techniques like clustering, classification, and regression examine IP reputation, geolocation, and traffic patterns. File categorization—using machine learning to identify and categorize potentially hazardous files—concludes the study. If users open harmful files, viruses, Trojans, and other malware may infect their systems and steal personal data. Using file type, size, and behavior, supervised and unsupervised machine learning systems may identify dangerous files. This study discusses machine learning methods for detecting and banning risky websites and files. Machine learning algorithms can detect and mitigate cyber risks through analysis and categorization. These algorithms have false positives and negatives, overlook dangers, and require a lot of training data. Thus, machine learning must be employed with intrusion detection systems and firewalls to guard against cyberattacks.

Accurate weather forecasting is essential for sectors like agriculture, aviation, and disaster management. However, deep learning algorithms face challenges in prediction accuracy due to issues like vanishing gradients, overfitting, and high computational demands. This research proposes a novel U-Net based architecture utilizing a Convolutional Neural Network (CNN) bottleneck layer to improve weather forecasting. Key features include a skip-connection mechanism, modified weight update rules, Gaussian-mutation operations, and the Adam optimizer for enhanced feature extraction and faster, more accurate predictions. The model was tested using precipitation data from Doppler Weather Radar (DWR) Chennai Radar and weather parameters from European Centre for Medium-Range Weather Forecasts (ECMWF). A dedicated GeoServer facilitates realtime data processing. Experimental results show the proposed algorithm achieves 97.5% accuracy, outperforming CNN and long short-term memory (LSTM) models by 5.84% and 2.41%, respectively.