Okra (Abelmoschus esculentus) is a vital crop in the Indian agriculture sector, producing one-third of its production. Identifying fresh and ripe okra plants for maximum yield and profit is significantly challenging. Ripeness can be determined by shape, length, color variation, and moisture content. However, to reduce this time-consuming effort, this work emphasizes the classification of fresh and diseased okra leaves as the initial step and assesses the maturity stages, including ripe, unripe, and overripe. The OkraFarm dataset was collected from the real-time farm to determine the maturity stage. Building on state-of-the-art convolutional neural networks, three experiments are performed to lay identification of fresh and ripe okra-Experiment 1: leaf disease classification using the pre-trained VGG19 model achieving a maximum accuracy of 98.89%; Experiment 2: detection of okra fruit using the YOLOv5 model, achieving a maximum accuracy of 84.5%; Experiment 3: handling data imbalance using the MLSMOTE algorithm and classifying the maturity stages of the okra plant into ripe, overripe, and unripe, achieving a maximum accuracy of 96.10% on the test data.

BackgroundThe Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans.MethodsRViT incorporates rotated patch embeddings to enhance the accuracy of brain tumor identification.ResultsEvaluation on the Brain Tumor MRI Dataset from Kaggle demonstrates RViT's superior performance with sensitivity (1.0), specificity (0.975), F1-score (0.984), Matthew's Correlation Coefficient (MCC) (0.972), and an overall accuracy of 0.986.ConclusionRViT outperforms the standard Vision Transformer model and several existing techniques, highlighting its efficacy in medical imaging. The study confirms that integrating rotational patch embeddings improves the model's capability to handle diverse orientations, a common challenge in tumor imaging. The specialized architecture and rotational invariance approach of RViT have the potential to enhance current methodologies for brain tumor detection and extend to other complex imaging tasks.

Cloud computing is a dynamic technology that requires efficient resource allocation strategies, and the task scheduling is optimized in heterogeneous cloud environments. The virtualization technology is largely responsible for the popularity of the cloud. During peak load periods, due to resource scarcity, some tasks are migrated to other data centers to achieve balance. To tackle this challenge, a novel method is proposed such as the genetic water evaporation optimization (GWEO) algorithm to perform an effective resource allocation in diverse cloud infrastructures. In order to reduce task execution time, the GWEO is associated with genetic algorithms and water evaporation optimization algorithm that maximize resource allocation. The scalability and adaptability of novel GWEO were explored through experiments involving larger-scale cloud deployments, showcasing its efficacy in handling complex scheduling tasks in real-world cloud environments. The promising results indicate its potential to significantly enhance performance and scalability in diverse cloud computing environments, addressing critical challenges associated with resource optimization and task scheduling.

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.

The rapid proliferation of the Internet of Things (IoT) has led to a significant surge in interconnected devices across diverse domains, ranging from smart homes and healthcare systems to industrial automation and smart cities. However, this exponential growth has exposed IoT devices to a plethora of cyber threats, including illegal access, data breaches, and malicious attacks, primarily due to their inherent limitations in terms of network capabilities, computational power, and memory. To combat these security challenges and ensure the safety of IoT ecosystems, the development of effective intrusion detection systems has become imperative. Such systems play a crucial role in detecting and preventing unauthorized activities within IoT networks. In this context, this article presents a pioneering approach called DeepLG SecNet, which leverages a combination of deep learning techniques, including Long Short-Term Memory (LSTM), gated Secure Network (SecNet), and Crossover Chaos Game Optimization (CCGO), to fortify IoT devices against unauthorized access and potential threats. To validate the efficacy of the proposed DeepLG SecNet method, various samples were collected from the BoT-IoT dataset and the NSL-KDD dataset. Performance evaluation was conducted using essential metrics to assess the model's detection capabilities in an IoT intrusion context. The experimental analysis yielded promising results, highlighting the effectiveness of the DeepLG SecNet method in intrusion detection for IoT environments. Specifically, DeepLG SecNet outperformed existing methods, demonstrating higher accuracy, precision, recall, and F1 score in safeguarding IoT systems from potential security breaches.

As digitization continues to expand and cybercrimes become more prevalent, making it is crucial to prioritize the implementation of robust security measures. Malicious short URLs are frequently utilized as a vector for cyber-attacks on online forums and social media platforms. To address this issue, a plugin-based solution that uses ensemble learning to combine random forest, k-neighbors classifier, and logistic regression into a stacked model, was developed. The model was trained over the combination of three most popular kaggle datasets, with over 1081195 URLs. Additionally, gradient boosting was applied to further enhance the model's performance, resulting in a 92% accuracy in the detection. We developed the browser extension with Flask and JavaScript that identifies URLs as malicious or safe, for facilitation of the proposed solution. The work emphasizes the need for effective measures to mitigate cyber-attack risks, particularly those involving malicious short URLs.

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 Chimp-Whale 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).

The cloud computing has developed a vital service in information technology (IT). It assures resource pooling as well as offers services on-demand around the network. The efficient task scheduling and the balanced task distribution become the major challenging problem in the cloud computing system because of the active heterogeneous nature of resources as well as the tasks. The resources are unstable in nature whenever a large number of resources are requested for completing the tasks. The main part of this issue is to design the effective intelligent searching arrangement for scheduling the tasks in suitable virtual machines and how VM schedules the task in the efficient way. In this article, an effective method using MAP reduces structure and HBSFD for the effective task scheduling in the provided cloud. First, from the client's task, task features are extracted. Later, these extracted features are chosen by the feature choice using the adjusted rand index and the standard deviation ratio (FSASR) method. Then, the larger tasks are divided to smaller subtasks using the map-reduce structure. Finally, the tasks are effectively scheduled with the help of the hybrid bird swarm-based flow directional algorithm (HBSFD). The experimental evaluations are conducted on the platform of cloudsim, and the experimental outcomes demonstrates that the proposed HBSFD technique performs better than other state-of-art approaches with respect to measures such as average turnaround time and processing time.

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.

On-demand, automatic resource delivery in a transparent manner to users is a remarkable feature offered by the cloud computing environment. User demands are met by dynamically provisioning the cloud resources. Incidental failures during task execution in cloud could be attributed to a variety of reasons. Such failures bring down the cloud performance. Recently, a variety of intelligent task scheduling algorithms have been demonstrated to address several issues in cloud scheduling. Most of these algorithms neglect the fault tolerance criterion, which if addressed appropriately could contribute to better cloud performance. In this research, we had proposed a Dynamic Clustering Cuckoo Whale Optimization Algorithm (DCCWOA) for carrying out efficient scheduling in the cloud by paying equal attention to the fault tolerance parameter. The proposed fault tolerance aware algorithm addresses the scheduling of tasks by maintaining a tab on the currently available resources such that unfortunate failures of autonomous tasks get effectively addressed leading to reduced failures. The performance of the proposed fault tolerance aware DCCWOA has been compared with Ant Colony Optimization algorithm (ACO), Genetic Algorithm (GA) and League Championship Algorithm (LCA)with respect to makespan, failure ratio and failure slowdown parameters under three different scenarios, where in each scenario the number of tasks were appropriately varied. It has been found that the proposed DCCWOA had produced an improvement of 58.19%, 19.88% and 29.32% under scenario 1 for makespan, failure ratio and failure slowdown parameters respectively when compared to ACO, GA and LCA algorithms respectively. Detailed experimental results for scenarios 1, 2 and 3 had been presented in the results section of this article. Results obtained prove the efficacy of the proposed algorithm in overcoming the faults and increasing the scheduling performance of the cloud with respect to the failure rate.

Well organized datacentres with interconnected servers constitute the cloud computing infrastructure.User requests are submitted through an interface to these servers that provide service to them in an on-demand basis.The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category.Task scheduling in cloud poses numerous challenges impacting the cloud performance.If not handled properly, user satisfaction becomes questionable.More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment.The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution.An improvised seagull optimization algorithm which combines the features of the Cuckoo search (CS) and seagull optimization algorithm (SOA) had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment.The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment.Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization (MO-ACO), ACO and Min-Min algorithms.The proposed SOA-CS technique had produced an improvement of 1.06%, 4.2%, and 2.4% for makespan and had reduced the overall cost to the extent of 1.74%, 3.93% and 2.77% when compared with PSO, ACO, IDEA algorithms respectively when 300 vms are considered.The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.

Internet of Health Things plays a vital role in day-to-day life by providing electronic healthcare services and has the capacity to increase the quality of patient care. Internet of Health Things (IoHT) devices and applications have been growing rapidly in recent years, becoming extensively vulnerable to cyber-attacks since the devices are small and heterogeneous. In addition, it is doubly significant when IoHT involves devices used in healthcare domain. Consequently, it is essential to develop a resilient cyber-attack detection system in the Internet of Health Things environment for mitigating the security risks and preventing Internet of Health Things devices from becoming exposed to cyber-attacks. Artificial intelligence plays a primary role in anomaly detection. In this paper, a deep neural network-based cyber-attack detection system is built by employing artificial intelligence on latest ECU-IoHT dataset to uncover cyber-attacks in Internet of Health Things environment. The proposed deep neural network system achieves average higher performance accuracy of 99.85%, an average area under receiver operator characteristic curve 0.99 and the false positive rate is 0.01. It is evident from the experimental result that the proposed system attains higher detection rate than the existing methods.

The security of civilians and high-profile officials is of the utmost importance and is often challenging during continuous surveillance carried out by security professionals. Humans have limitations like attention span, distraction, and memory of events which are vulnerabilities of any security system. An automated model that can perform intelligent real-time weapon detection is essential to ensure that such vulnerabilities are prevented from creeping into the system. This will continuously monitor the specified area and alert the security personnel in case of security breaches like the presence of unauthorized armed people. The objective of the proposed system is to detect the presence of a weapon, identify the type of weapon, and capture the image of the attackers which will be useful for further investigation. A custom weapons dataset has been constructed, consisting of five different weapons, such as an axe, knife, pistol, rifle, and sword. Using this dataset, the proposed system is employed and compared with the faster Region Based Convolution Neural Network (R-CNN) and YOLOv4. The YOLOv4 model provided a 96.04% mAP score and frames per second (FPS) of 19 on GPU (GEFORCE MX250) with an average accuracy of 73%. The R-CNN model provided an average accuracy of 71%. The result of the proposed system shows that the YOLOv4 model achieves a higher mAP score on GPU (GEFORCE MX250) for weapon detection in surveillance video cameras.

Automated object detection has received the most attention over the years.Use cases ranging from autonomous driving applications to military surveillance systems, require robust detection of objects in different illumination conditions.State-of-the-art object detectors tend to fare well in object detection during daytime conditions.However, their performance is severely hampered in night light conditions due to poor illumination.To address this challenge, the manuscript proposes an improved YOLOv5-based object detection framework for effective detection in unevenly illuminated nighttime conditions.Firstly, the preprocessing strategies involve using the Zero-DCE++ approach to enhance lowlight images.It is followed by optimizing the existing YOLOv5 architecture by integrating the Convolutional Block Attention Module (CBAM) in the backbone network to boost model learning capability and Depthwise Convolutional module (DWConv) in the neck network for efficient compression of network parameters.The Night Object Detection (NOD) and Exclusively Dark (ExDARK) dataset has been used for this work.The proposed framework detects classes like humans, bicycles, and cars.Experiments demonstrate that the proposed architecture achieved a higher Mean Average Precision (mAP) along with a reduction in model size and total parameters, respectively.The proposed model is lighter by 11.24% in terms of model size and 12.38% in terms of parameters when compared to baseline YOLOv5.

The Cloud environment had been the go-to for many users recently. Once request from users get submitted, cloud resources are put into action to fulfill the request. Scheduling is the primary task in cloud that needs to be up-to the mark for completing the requests swiftly. Multiple dynamic requests are submitted simultaneously by cloud users that necessitates precise and prompt scheduling in cloud. Scheduling in cloud may be hampered by various constraints, take for example the various QoS parameters that needs to be upheld. Though many researchers had proposed solutions for scheduling in cloud, improvisations can still be made by combining several QoS parameters that help attain optimized scheduling in cloud to boost the overall cloud performance. In this paper, we had proposed a Binary Grey Wolf Optimization (BGWO) algorithm to optimize the scheduling activity in cloud computing environment. The BGWO is a multi-heuristic algorithm where tasks are scheduled based on a fitness function, explicitly designed for achieving optimization goal. The fitness function that had been designed comprises of three prime parameters namely, the degree of imbalance (DoI), energy consumption and makespan. The performance efficiency of the proposed BGWO had been ascertained by comparing it with Oppositional based Grey Wolf Optimization algorithm (OGWO) and Mean Grey Wolf Optimization algorithm (Mean GWO) with respect to imbalance, energy and makespan parameters. The proposed algorithm had produced a cumulative improvement of 10.13% and 17.4% for makespan, 30.18% and 41.96% for DoI, 8.94% and 14.95% for energy consumption parameters. Detailed comparative results obtained had been described in the Results part of this research article.

In IoT applications where connectivity to cloud-based systems is irregular yet low latency is a crucial requirement, fog computing is necessary for success. Fog Computing's dispersed functionality enables storage and execution to take place in totally separate places. Resource management and multiple programming paradigms are required due to the distributed capabilities and numerous user applications taken together. It is important to manage and evaluate failures in network-based systems and services caused by a variety of limitations. The study's main goal is to shed light on various QoS-based scheduling strategies that aim to improve execution in fog computing to evaluate several fault-tolerant methods used in the fog computing environment.

Abstract The growth of cloud computing is astounding in the business and research domains in the last decade. The popularity of cloud could be attributed to the virtualization technology. To achieve balance and due to resource scarcity during peak load period, a few of those tasks are migrated to other datacenters. The potential of cloud computing can be harvested to its fullest by employing efficient task scheduling techniques. In cloud, focus is on makespan and maximum system utilization phenomenon owing to which tasks may be scheduled on different virtual machines. Meta-heuristic scheduling algorithms could be employed to provide solution to such issues since task scheduling is a NP-hard problem. In this proposed research work, a integration of Genetic Water Evaporation Optimization Algorithm (GWEO) had been presented for efficiently scheduling of tasks on a heterogeneous cloud environment. The proposed research work objective is to minimize the time of task execution, cost and energy consumed by the resources during task execution. The proposed hybrid GWEO algorithm is hybridization of Genetic Algorithm (GA) and Water Evaporation Optimization Algorithm (WEO). Different datasets had been employed for evaluating the proposed GWEO algorithm. The proposed hybrid technique performance was evolved for the metrics cost, energy and execution time using CloudSim toolkit. The experimental results obtained through simulation had been compared with other algorithms, which showed that the proposed GWEO technique achieve a significant improvement in the QoS metrics (cost, energy consumption and execution time).

Computer!! The one everyone uses almost every day in this busy world, a device capable of handling tasks from simple addition to complex tasks like launching rockets, but to sustain these tasks, and be a part of the huge networks like the internet and serve as an integrated part of IoT (internet of things) many different devices like cloud computing, servers are developed which can handle the request and return the appropriate response to the device. However, IoT device face certain issues among which the obtainability of on-going energy heads in one of the biggest issue and reason of this is because of the enormous amount of information that is created by using IoT devices and handling this information is one of the biggest constraints. To reduce these issues, many different schedulers were introduced like the optimized schedule task requests which enhances system productivity and performance, and the task scheduler whose objective is to divide tasks based on the available resources so the it reduces make span and time consumption. Hence to improve cloud computing many different meta-heuristics systems for task scheduling were introduced, but their throughput was far from the optimal state and needed upgrading. Keeping that in mind, this paper proposes an Opposition based sunflower optimization (OSFO) algorithm for enhancing the throughput of already available task schedulers in terms of cost, energy and makespan.

Task scheduling is an important issue in cloud computing when it comes to achieving multiple goals and satisfying different user needs. The increasing demand and users urge the necessity to minimize the task completion time and enhance the load balancing capacity. To achieve this goal, this article proposes a Hybrid firebug and Tunicate Optimization (HFTO) algorithm. Based on the previous scheduling information, the HFTO classifier classifies the task and creates different variants of Virtual Machine (VM). This step helps to minimize the time taken for VM creation. The proposed HFTO task scheduling framework aims at optimizing different Quality of Service (QoS) parameters such as fault tolerance, response time, efficiency, and makespan. The optimization algorithm helps to expand the search space of the solutions and frames an optimal task scheduling strategy for the virtual machines. The HFTO optimization method has several advantages, including enhanced search capability and faster convergence. The HFTO algorithm improves the fault tolerance capability by allocating the tasks to appropriate resources based on the resource load peak. The lightweight tasks can be allocated to the resources with high CPU utilization and the computation-intensive tasks can be allocated to the resources with low CPU utilization. The response time and execution time are improved by task pre-emption. Hence the time complexity and computational complexity can be improved by the HFTO algorithm even with limited resource capability. The experiments are conducted using the CloudSim experimental platform and the results are compared to the state-of-art techniques. The performance of the proposed methodology is evaluated in terms of different performance metrics namely makespan, load balancing, and average execution time. The results show that, when compared to existing techniques, the proposed methodology provides higher load balancing efficiency and improved cloud task scheduling performance.