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Abstract This research addresses the critical need to optimize the Energy Efficiency (EE) for Ultra-Dense HetNets amid the ever-increasing demands for high-speed data networks. The rapid increase in high-speed devices highlights the urgent necessity for a transformative change in upcoming 5G cellular networks. According to Ericsson's 2022 report, mobile data traffic volume is expected to double by 2027, with mobile video traffic anticipated to rise by nearly 30% each year. In response to these challenges, researchers have identified essential technologies that facilitate 5G networks, including massive Multiple-input-Multiple-output (m-MIMO) systems and HetNets. Although both promise enhanced coverage and throughput, HetNets emerges as a cost-effective solution, surpassing m-MIMO in implementation cost and coverage. However, achieving maximum EE in HetNets necessitates careful consideration of various constraints, including delay, coverage probability, and Signal-to-Interference-plus-Noise Ratio (SINR) thresholds. This research marks a significant milestone in adopting the Distributed Dynamic Opportunistic Sleep Strategy (D-DOSS) approach. The D-DOSS method organizes small clusters throughout the network and evaluates key Quality of Service (QoS) parameters including Energy Utilization Efficiency (EUE), Coverage Probability, Data Throughput, and Success Probability using Monte Carlo simulations. This research analyzes Distributed Sleep (DS) and Centralized Schemes (CS) concerning given QoS parameters. While DS methodologies often exhibit performance trade-offs compared to CS, they provide significant advantages in terms of ease of implementation and management. CS, though representing the most commonly used method in ultra-dense HetNets involves high computational costs that complicate its management. By integrating the D-DOSS and addressing various constraints, this research not only advances HetNet technologies but also makes a significant contribution to optimizing EE while preserving network performance and QoS. The innovative D-DOSS approach offers a promising solution to the challenges of energy efficiency in wireless communication networks and paves the way for future advancements in HetNet deployments. The results and analysis show that D-DOSS effectively addresses the limitations of DS and outperforms existing CS techniques.

With the exponential expansion of the wireless industry, the demand for improved data network throughput, capacity, and coverage has become critical. Heterogeneous ultra-dense networks (UDNs) have emerged as a promising solution to meet these escalating requirements for high data rates and capacity. However, effectively deploying and managing small cells within UDNs presents significant challenges, particularly amidst varying traffic loads and the necessity for efficient resource utilization to minimize energy consumption, especially in environments with high interference levels. Inadequate deployment of small cells can lead to excessive interference, resulting in suboptimal profitability and inefficient energy resource utilization. Addressing these challenges demands innovative approaches such as data-driven deployment strategies and efficient energy efficient resource (EER) management for small cells. Leveraging data-driven methodologies, operators can optimize small cell deployment locations and configurations based on real-time traffic patterns and environmental conditions, thereby maximizing network performance while minimizing energy consumption. This research investigates the effectiveness of a data-driven mechanism in enhancing the average achievable data rate of small cells within Heterogeneous UDNs. Our proposed approach Data Driven Opportunistic Sleep Strategy (D-DOSS) employs stochastic geometry based mathematical model for the heterogeneous networks (HetNets) wireless network will assess the impact of strategic small cell deployment on network performance in respect of energy savings. The results from Monte Carlo simulations reveal that D-DOSS outperforms traditional strategies by improving energy efficiency (EE) by 20% and achieving a 15% higher average data rate. Additionally, D-DOSS achieves a coverage probability of 50% at a signal-to-interference-plus-noise ratio (SINR) threshold of 5 dB, significantly better than random sleep mode (RSM) and load aware sleep (LAS) strategies. Overall, our findings underscore the significance of data-driven deployment and management strategies in optimizing the performance of HetNets UDNs. By embracing such approaches, wireless operators can meet the escalating demands for high-speed data transmission while achieving greater EE and sustainability in wireless network operations.

With the rapid growth in the usage of the Internet, criminals have found new ways to engage in cyber-attacks. The most common and widespread attack is URL phishing. The proposed system focuses on improving phishing website detection using feature selection and ensemble learning. This model uses two datasets, DS-30 and DS-50, each with 30 and 50 features. Ensemble learning using a voting classifier was then applied to train the model, achieving more accuracy. The combination of HEFS with random forest distribution achieved 94.6% accuracy while minimizing the number of features used (20.8% of the base feature set). The classifier works in the proposed model, and the accuracy is 96% and 98% on the DS-30 and DS-50 datasets, respectively. The hybrid model uses a combination of different factors to distinguish phishing websites from legitimate websites.

In November 2022, ChatGPT3, an advanced AI-powered chatbot, emerged suddenly, generating significant interest in higher education. Concerns arose regarding its potential to complicate the authentication of genuine student work. While some foresaw negative impacts, we advocate a positive outlook, suggesting educators can utilize ChatGPT to cultivate supportive learning environments that enhance students' character development. Our study thoroughly explores ChatGPT's implications for education in underdeveloped countries, examining both opportunities and challenges within AI research in education. We investigate practical applications, and potential benefits, and propose responsible integration strategies for students, teachers, and schools in utilizing ChatGPT for learning and assessment. Emphasizing ethical use, we stress leadership, character development, and authentic assessment as crucial factors. Despite concerns about academic integrity, we highlight ChatGPT's dual nature, it can facilitate cheating but also has the potential to deepen learning experiences. Our research focuses on understanding ChatGPT's impact on education from both student and teacher perspectives, discussing future trends in learning and teaching. The strategic integration of ChatGPT and AI in universities demands ethical foresight, personalized learning strategies, and ongoing research to optimize educational benefits while preserving core values and fostering student development.

This article proposes a knowledge-driven four-wave mixing (FWM) mitigation using supervised learning approaches and a multilevel regression-based dense wavelength division multiplexing (SMR-DWDM) system design. The evolution of 5G and the Internet of Things (IoT) results in an immense data rate consumption and introduces unprecedented dynamic network traffic. DWDM networks effectively accommodate these challenges by being highly responsive and adaptable to changes in traffic impact and network conditions. High-capacity DWDM transmission causes fiber nonlinearities, reducing system performance and effective bandwidth utilization and affecting Quality of Transmission (QoT) by inducing crosstalk, dispersion, and Inter-Symbol Interference (ISI). This work discusses knowledge-driven DWDM design, utilizing machine learning to improve flexibility, identify FWM parameters, and predict transmission quality. Firstly, machine learning optimizes parameters at the transmitter end to identify FWM monitoring factors, predict QoT based on subscriber requirements, and create a comprehensive database for training Machine Learning (ML) models. Then, supervised multilevel regression builds the knowledge-driven QoT Estimator, accurately selecting input parameter combinations for the automatic monitoring controller of the DWDM system. The accuracy of the proposed SMR-DWDM system is confirmed by validating it with various FWM mitigating factors monitored by Optical Spectrum Analyzer (OSA) and Bit Error Rate (BER) analyzers. Through parametric analysis and supervised multilevel regression, the system achieves high precision and accurately predicts QoT by over 80%, and improves 25% of the QoT enhancement compared with traditional methods, proving its effectiveness in managing fiber nonlinearities.

Effective transfer learning, within medical image classification, is probably one of the most critical areas of research due to the associated complexities with the nature of medical images. These involve variations in the acquisition techniques of images, resolutions, modalities, and patient demographics. This study aims to develop a robust transfer learning model by combining Mobile-Net with Particle Swarm Optimization, called MOB-CFPSO that classifies efficiently variety of datasets across heterogenic dataset modalities, including colored, black, and white datasets. A high-performing machine learning model needs enough data to enable robust feature extraction that can identify patterns in each class, allowing the model to differentiate between different classes accurately. The current statistical algorithm is less effective in the case of colored and black image classification and they also produce errors in the classification of findings of obvious disease in heterogenic image datasets e.g. color, Black, and white. There is a need to design and implement a robust image classification model based on heterogenic datasets that will reduce the error rate and optimize classification accuracy by using the Transfer Learning technique. Model robustness in deep learning refers to the capacity to sustain performance and deliver accurate predictions, even in the face of uncertainties, disturbances, or adversarial conditions. Two separate experiments are conducted on a pre-trained Mobile-Net model along with Modified PSO with constriction factor for model optimization, accuracy enhancement, and reduction of error rate for disease classification on two distinct datasets. DR Dataset comprised 10 distinct, whereas the MRI brain tumor included 8 various classes. The results showed that the proposed Model MOB-CFPSO exceeds the state-of-the-art algorithm in datasets, achieving high accuracy, robustness, precision, recall, and F1-score, with acceptable and favorable validation loss. A comprehensive MRI study showed that the MOB-CFPSO achieved 100% and 99.86% of training and validation accuracy respectively, with 0.0071% and 0.0071% of validation and training loss. However, Precision, Recall, and F1 scores were 100%, 100%, and 100%, respectively. DR Dataset achieved 95.09% and 97.66% of training and validation accuracy respectively whereas validation loss was 0.1902%, and training loss was 0.2469%. Precision, Recall, and F1 score were 98.3%, 92.4%, and 95.3% respectively.

The MQTTset dataset has been extensively investigated for enhancing anomaly detection in IoT-based systems, with a focus on identifying Denial of Service (DoS) attacks. The research addresses a critical gap in MQTT traffic anomaly detection by proposing the incorporation of the ‘source’ attribute from PCAP files and utilizing hand-crafted feature engineering techniques. Various filtering methods, including data conversion, attribute filtering, handling missing values, and scaling, are employed. Anomalies are categorized and prioritized based on frequency of occurrence, with a specific emphasis on DoS attacks. The study compares the performance of the decision tree and its eight variant models (ID3, C4.5, Random Forest, CatBoost, LightGBM, XGBoost, CART, and Gradient Boosting) for anomaly detection in IoT-based systems. Evaluation metrics such as prediction accuracy, F1 score, and computational times (training and testing) are utilized. Hyperparameter fine-tuning techniques like grid search and random search are applied to enhance model performance, accuracy, and reduce computational costs. Results indicate that the benchmark Decision Tree model achieved 92.57% accuracy and a 92.38% F1 score with training and testing times of 2.95 seconds and 0.86 seconds, respectively. The Feature Engineering (Modified) dataset demonstrated a substantial improvement, reaching 98.56% accuracy and a 98.50% F1 score, with comparable training and testing times of 0.70 seconds and 0.02 seconds. Furthermore, the Modified Decision Tree Algorithm significantly improved accuracy to 99.27%, F1 score to 99.26%, and reduced training time to 0.73 seconds and testing time to 0.14 seconds. The research contributes valuable insights into feature engineering and guides the selection of effective approaches for anomaly detection in IoT-based systems, providing early threat warnings and enhancing overall system security and reliability.

The significance of digital data, specifically of digital visual information has grown immensely and driving advancements in fields like medical, entertainment, and scientific research. The domain focused on retrieving such visual content is known as Content-Based Information Retrieval (CBIR). Content-Based Image Retrieval (CBIR) encompasses two primary sub-domains: image searching and reverse image searching. The study presents a significant contribution to the field of reverse image searching. Most current research focuses on reverse searching of individual images, primarily exploring techniques for searching single images in databases. However, composite images or collages are now widely used to represent summarized visual data in fields like medicine, entertainment, and satellite imagery. Even though our prior work for RISiC (baseline method) demonstrated promising results in accuracy, it did not explicitly address the challenge of time constraints. The study addresses the gap by proposing a method that achieves a balance between accuracy and time efficiency. The approach leverages Colored Binary SIFT (CBSIFT) combined with a novel region-balanced feature extraction technique. Additionally, an Inverted Index Structure is used in the online phase, along with a feature pruning method to enhance time efficiency while maintaining high retrieval precision. The work-balanced method for Reverse Image Searching in Collages (B-RISiC) is evaluated on Caltech-101, Corel5k, and Oxford5k datasets. The performance is benchmarked against two approaches: the simple BSIFT with Indexing and the baseline method. The results indicate that B-RISiC outperformed all others, achieving search times of 1.796 s, 7.763 s, and 0.718 s for the Caltech-101, Corel5k, and Oxford5k datasets respectively, with precision values of 0.8217, 0.8575, and 0.8810.

This study introduces a novel and refined approach for generating exceptionally efficient S-boxes. The proposed methodology employs a hybrid approach that combines linear fractional transformation (LFT) with a multilayer perceptron (MLP) architecture. This method makes use of a perceptron with three layers: input, hidden, and output. Each layer's neuron count is fine-tuned to conform to the S-box layout. In addition, a threshold nonlinear transformation is utilized to increase nonlinearity, and a novel algorithm for boosting nonlinearity is introduced. The utilization of both LFT and MLP approaches has led to the development of S-boxes that possess nearly ideal average nonlinearity values, surpassing those that have been presented in literature. Notably, one S-box achieved an exceptional nonlinearity score of 114.50. Furthermore, to demonstrate how well the S-box works, this study also employs it in an image encryption application.

The popularity of cloud computing (CC) has increased significantly in recent years due to its cost-effectiveness and simplified resource allocation. Owing to the exponential rise of cloud computing in the past decade, many corporations and businesses have moved to the cloud to ensure accessibility, scalability, and transparency. The proposed research involves comparing the accuracy and fault prediction of five machine learning algorithms: AdaBoostM1, Bagging, Decision Tree (J48), Deep Learning (Dl4jMLP), and Naive Bayes Tree (NB Tree). The results from secondary data analysis indicate that the Central Processing Unit CPU-Mem Multi classifier has the highest accuracy percentage and the least amount of fault prediction. This holds for the Decision Tree (J48) classifier with an accuracy rate of 89.71% for 80/20, 90.28% for 70/30, and 92.82% for 10-fold cross-validation. Additionally, the Hard Disk Drive HDD-Mono classifier has an accuracy rate of 90.35% for 80/20, 92.35% for 70/30, and 90.49% for 10-fold cross-validation. The AdaBoostM1 classifier was found to have the highest accuracy percentage and the least amount of fault prediction for the HDD Multi classifier with an accuracy rate of 93.63% for 80/20, 90.09% for 70/30, and 88.92% for 10-fold cross-validation. Finally, the CPU-Mem Mono classifier has an accuracy rate of 77.87% for 80/20, 77.01% for 70/30, and 77.06% for 10-fold cross-validation. Based on the primary data results, the Naive Bayes Tree (NB Tree) classifier is found to have the highest accuracy rate with less fault prediction of 97.05% for 80/20, 96.09% for 70/30, and 96.78% for 10 folds cross-validation. However, the algorithm complexity is not good, taking 1.01 seconds. On the other hand, the Decision Tree (J48) has the second-highest accuracy rate of 96.78%, 95.95%, and 96.78% for 80/20, 70/30, and 10-fold cross-validation, respectively. J48 also has less fault prediction but with a good algorithm complexity of 0.11 seconds. The difference in accuracy and less fault prediction between NB Tree and J48 is only 0.9%, but the difference in time complexity is 9 seconds. Based on the results, we have decided to make modifications to the Decision Tree (J48) algorithm. This method has been proposed as it offers the highest accuracy and less fault prediction errors, with 97.05% accuracy for the 80/20 split, 96.42% for the 70/30 split, and 97.07% for the 10-fold cross-validation.

Collage, a popular form of visual-content summarization technique is commonly used by internet users and digital artists. Social media usage is a rising trend that significantly affects the increasing demand for collages. The primary source of collage generation is social media, but other sources also generate it. Searching for a required query image in this corpus is a crucial demand and also valuable. The query image can be retrieved using Reverse Image Search (RIS), either in its exact form or with a small variation. Well-known search engines like Google and Yandex have this functionality, but their method has not been made public. In this research, we propose a consolidated framework for reverse image searching for the problem of collage. Essentially, the local features of collage images are extracted by using SIFT, SURF, and ORB algorithms. These features undergo the localization of the region of interest (ROI) process which handles by binning technique. We propose to use the Manhattan distance to calculate the similarity. The proposed model is extensively evaluated on standard databases and is shown to always have good results using SIFT algorithm. The proposed model is entirely generic and attains 90.96%, accuracy using the SIFT algorithm. The proposed approach is also evaluated on flip and scale variant college and achieves a result of 83% and 78% respectively, using SIFT algorithm.

The ever-increasing heterogeneous connections and the demands of the users pose many new challenges to the network service providers to sustain by providing improved quality of service (QoS). Software-defined networking (SDN) is a game changer in networking by allowing user customization to enhance performance. With the advent of 5G and the increasing user requests, a massive volume of heterogeneous traffic is generated in the network, increasing load. Currently, the existing load balancing techniques lack efficiency in handling the load under unicontroller deployment. In addition, the network paths selected must also be reliable and optimal. We proposed the self-socio adaptive, reliable particle swarm optimization (SSAR-PSO) load balancing technique to address the issue of load balancing in the unicontroller deployment of SDN. In the proposed technique, the performance of the node itself, known as direct information, and the performance of the neighbouring nodes, known as indirect information, were considered to identify the reliable node to form an optimal path. Simulation results showed that the proposed technique outperforms the existing state-of-the-art techniques under TCP and UDP load in the following network performance metrics: latency, packet loss ratio, throughput, average round trip time, and bandwidth utilization ratio.

The benefits and opportunities offered by cloud computing are among the fastest-growing technologies in the computer industry. Additionally, it addresses the difficulties and issues that make more users more likely to accept and use the technology. The proposed research comprised of machine learning (ML) algorithms is Naïve Bayes (NB), Library Support Vector Machine (LibSVM), Multinomial Logistic Regression (MLR), Sequential Minimal Optimization (SMO), K Nearest Neighbor (KNN), and Random Forest (RF) to compare the classifier gives better results in accuracy and less fault prediction. In this research, the secondary data results (CPU-Mem Mono) give the highest percentage of accuracy and less fault prediction on the NB classifier in terms of 80/20 (77.01%), 70/30 (76.05%), and 5 folds cross-validation (74.88%), and (CPU-Mem Multi) in terms of 80/20 (89.72%), 70/30 (90.28%), and 5 folds cross-validation (92.83%). Furthermore, on (HDD Mono) the SMO classifier gives the highest percentage of accuracy and less fault prediction fault in terms of 80/20 (87.72%), 70/30 (89.41%), and 5 folds cross-validation (88.38%), and (HDD-Multi) in terms of 80/20 (93.64%), 70/30 (90.91%), and 5 folds cross-validation (88.20%). Whereas, primary data results found RF classifier gives the highest percentage of accuracy and less fault prediction in terms of 80/20 (97.14%), 70/30 (96.19%), and 5 folds cross-validation (95.85%) in the primary data results, but the algorithm complexity (0.17 seconds) is not good. In terms of 80/20 (95.71%), 70/30 (95.71%), and 5 folds cross-validation (95.71%), SMO has the second highest accuracy and less fault prediction, but the algorithm complexity is good (0.3 seconds). The difference in accuracy and less fault prediction between RF and SMO is only (.13%), and the difference in time complexity is (14 seconds). We have decided that we will modify SMO. Finally, the Modified Sequential Minimal Optimization (MSMO) Algorithm method has been proposed to get the highest accuracy & less fault prediction errors in terms of 80/20 (96.42%), 70/30 (96.42%), & 5 fold cross validation (96.50%).

To provide various applications in various domains, a large-scale cloud data center is required. Cloud computing enables access to nearly infinite computing resources on demand. As cloud computing grows in popularity, researchers in this field must conduct real-world experiments. Configuring and running these tests in an actual cloud environment is costly. Modeling and simulation methods, on the other hand, are acceptable solutions for emulating environments in cloud computing. This research paper reviewed several simulation tools specifically for cloud computing in the literature and presented the most effective simulation methods in this research domain, as well as an analysis of a variety of cloud simulation tools. Cloud computing tools such as CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports were evaluated. Furthermore, a parametric evaluation of cloud simulation tools is presented based on the identified parameters. Several 5-parameter tests were performed to demonstrate the capabilities of the cloud simulator. These results show the value of our proposed simulation system. CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports are used to evaluate host processing elements, virtual machine processing elements, cloudlet processing elements, userbase average, minimum, and maximum, and cloudlet ID Start Time, Finish Time, Average Start, and Average Finish for each simulator. The outcomes compare these five simulator metrics. After reading this paper, the reader will be able to compare popular simulators in terms of supported models, architecture, and high-level features. We performed a comparative analysis of several cloud simulators based on various parameters. The goal is to provide insights for each analysis given their features, functionalities, and guidelines on the way to researchers’ preferred tools.

Artificial intelligence (AI) is one of the emerging technologies. In recent decades, artificial intelligence (AI) has gained widespread acceptance in a variety of fields, including virtual support, healthcare, and security. Human-Computer Interaction (HCI) is a field that has been combining AI and human-computer engagement over the past several years in order to create an interactive intelligent system for user interaction. AI, in conjunction with HCI, is being used in a variety of fields by employing various algorithms and employing HCI to provide transparency to the user, allowing them to trust the machine. The comprehensive examination of both the areas of AI and HCI, as well as their subfields, has been explored in this work. The main goal of this article was to discover a point of intersection between the two fields. The understanding of Explainable Artificial Intelligence (XAI), which is a linking point of HCI and XAI, was gained through a literature review conducted in this research. The literature survey encompassed themes identified in the literature (such as XAI and its areas, major XAI aims, and XAI problems and challenges). The study’s other major focus was on the use of AI, HCI, and XAI in healthcare. The poll also addressed the shortcomings in XAI in healthcare, as well as the field’s future potential. As a result, the literature indicates that XAI in healthcare is still a novel subject that has to be explored more in the future.

Brain Stimulation is now becoming the preferred approach where pharmacological treatment is ineffective in neurological conditions such as Parkinson’s Disease (PD), Tourette Syndrome, Addiction disorder, Depression, and Anxiety. The brain can be stimulated by electrical current, light, and sound energy, invasively or non-invasively. The article comprises of pathophysiology in Parkinson’s, current pharmacological modalities used for the treatment (along with limitations), and deep brain stimulation as the last resort. The article also provides a comprehensive analysis of intelligent Deep Brain Stimulation methods (procedure, architecture, and type) from the studies and research conducted in the last five years. In the end, research gaps and associated challenges are discussed.

Automation in agriculture nowadays is the main focus and area of development for various countries. The population rate of the world is increasing rapidly and will be double in upcoming decades and the need of food is also increasing accordingly. To meet this rapid growth in demand, agriculture automation is the best solution. Traditional strategies employed by farmers are not efficient enough to fulfill the rising demand. Improper use of nutrients, water, fertilizers and pesticides disturbs the agricultural growth and the land remains barren with no fertility. This research paper presents different control strategies used to automate agriculture such as: IoT, aerial imagery, multispectral, hyperspectral, NIR, thermal camera, RGB camera, machine learning, and artificial intelligence techniques. Problems in agriculture like plant diseases, pesticide control, weed management, irrigation and water management can easily be solved by different automated and control techniques mentioned above. Automation by advance control strategies of agricultural methods have verified to increase the crops yield and also the soil fertility become strong. This research paper reviews and observe the work of different researchers to present a brief summary about the trends in smart agriculture and also provides the work flow and revenue of smart agriculture system in figure 15 using technologies verified by researchers in their research papers.

Background and ObjectiveViral hepatitis is a major public health concern on a global scale. It predominantly affects the world's least developed countries. The most endemic regions are resource constrained, with a low human development index. Chronic hepatitis can lead to cirrhosis, liver failure, cancer and eventually death. Early diagnosis and treatment of hepatitis infection can help to reduce disease burden and transmission to those at risk of infection or reinfection. Screening is critical for meeting the WHO's 2030 targets. Consequently, automated systems for the reliable prediction of hepatitis illness. When applied to the prediction of hepatitis using imbalanced datasets from testing, machine learning (ML) classifiers and known methodologies for encoding categorical data have demonstrated a wide range of unexpected results. Early research also made use of an artificial neural network to identify features without first gaining a thorough understanding of the sequence data.MethodsTo help in accurate binary classification of diagnosis (survivability or mortality) in patients with severe hepatitis, this paper suggests a deep learning-based decision support system (DSS) that makes use of bidirectional long/short-term memory (BiLSTM). Balanced data was utilized to predict hepatitis using the BiLSTM model.ResultsIn contrast to previous investigations, the trial results of this suggested model were encouraging: 95.08% accuracy, 94% precision, 93% recall, and a 93% F1-score.ConclusionsIn the field of hepatitis detection, the use of a BiLSTM model for classification is better than current methods by a significant margin in terms of improved accuracy.

Anomaly detection (AD) has captured a significant amount of focus from the research field in recent years, with the rise of the Internet of Things (IoT) application. Anomalies, often known as outliers, are defined as the discovery of anomalous occurrences or observations that differ considerably from the mainstream of the data. The IoT which is described as a network of Internet-based digital sensors that continuously generate massive volumes of data and use to communicate with one another theMessage Queuing Telemetry Transport (MQTT) protocol. Brute-force, Denial-of-Service (DoS), Malformed, Flood, and Slowite attacks are the most common in theMQTT network. One of the significant factors in IoT AD is the time consumed to predict an attack and take preemptive measures. For instance, if an attack is detected late, the loss of attack is irreversible. This paper investigates the time to detect an attack using machine learning approaches and proposes a novel approach that applies correlation analysis to reduce the training and testing time of these algorithms. The new approach has been evaluated on Random Forest, Decision Tree, Naïve Bayes, Multi-Layer Perceptron, Artificial Neural Network, Logistic Regression, and Gradient Boost. The findings indicate that the correlation analysis is significantly beneficial in the process of feature engineering, primarily to determine the most relevant features in the MQTT dataset. This is, to the best of our knowledge, the first study on MQTTset that reduces the prediction time for DoS 0.92 (95% CI −0.378, 2.22) reduced to 0.77 (95% CI −0.414, 1.97) and for Malformed 2.92 (95% CI −2.6, 8.44) reduced to 0.49 (95% CI −0.273, 1.25).

Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing’s challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system’s ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.

Cloud computing has seen a major boom during the past few years. Many people have switched to cloud computing because traditional systems require complex resource distribution and cloud solutions are less expensive. Load balancing (LB) is one of the essential challenges in cloud computing used to balance the workload of cloud services. This research paper presents a performance evaluation of the existing load-balancing algorithms which are particle swarm optimization (PSO), round robin (RR), equally spread current execution (ESCE), and throttled load balancing. This study offers a detailed performance evaluation of various load-balancing algorithms by employing a cloud analyst platform. Efficiency concerning various service broker policy configurations for load-balancing algorithms’ virtual machine load balance was also calculated using metrics such as optimized response time (ORT), data center processing time (DCPT), virtual machine costs, data transfer costs, and total cost for different workloads and user bases. Many of the past papers that were mentioned in the literature worked on round robin and equally spread current execution, and throttled load-balancing algorithms were based on efficiency and response time in virtual machines without recognizing the relation between the task and the virtual machines, and the practical significance of the application. A comparison of specific load-balancing algorithms has been investigated. Different service broker policy (SBP) tests have been conducted to illustrate the load-balancing algorithm capabilities.

Ultimate extreme flash floods can be acknowledged as a main reason of high casualties and infrastructure loss in many countries like Pakistan, Malaysia, Philippines, Southern France, India, Bangladesh, China, Nepal, Canada, United States of America and others. Run offs can devastate huge buildings and personal belongings within fraction of seconds. Flash floods usually occurs due to many reasons like higher precipitation velocity, melting of ice debris in ocean, high wave current at sea shore, broken reservoir (dam), Cloud to ground flashes, thunderstorm and hurricane inside the ocean. More than one hundred and twenty thousand casualties resulted due to the flash floods during the 1992 and 2005. According to the literature review deadliest flash floods have been observed in past history. Many approaches have been completed to investigate the flash floods accurately and precisely with less false alarm rate. Disaster management authorities are unable to forecast the natural disasters accurately and precisely like tsunami, flash floods, hurricanes and seismic events due to the poor efficiency of the sensors and transmission of missed information. It has also been observed that during the wireless data transmission of sensors to the controller unit some bits of the data are missed, due to these phenomena data is not transmitted properly or indicate the wrong observations. Several diversified approaches have been made to identify the run offs more accurately and precisely. Generally, the approaches can be classified into two categories a) Engineering Based b) Non-Engineering Based. Engineering techniques based on the construction of the dams and reservoirs to store the excess water which causes severe run offs. Designing of various Artificial Intelligence Based competent algorithms to predict the flash floods vigorously can be considered as non-engineering based approaches. Authors have tried their best to summarize and portray all the successful techniques that can be used for the early prediction of flash floods. Scientists can be benefited by this research paper as this research paper is the detailed capsulization of all the approaches that has been carried out for the robust investigation of flash floods. Extensive literature review has been done to observe the comparative analysis for the investigation of flash floods identification accurately. Literature review has been categorized into following types; 1. Sensory Fusion based 2. Artificial Intelligence Based methods 3. Radar and Satellite based approaches 4. Modeling and Nowcasting. According to the exhaustive literature review it can be concluded that swarm intelligence weights optimization for multi-layer perceptron neural network configuration performed better among all the forecasting approaches and recommended as the future enhancement.

Flash floods can demolish infrastructure and property within seconds as they are very sudden. Flash floods are the main cause of the casualties and loss of properties. Existing natural disaster prediction algorithms contains false alarms. Indefinite techniques have been applied to overcome this leading issue in many countries. A competent flood management system must have the potential and tendency to identify the flash floods and atmospheric and climatic changes on early basis with less false alarm rate. Techniques which have been designed for the flash flood investigation may be categorized into following types a. Sensors based direct measurement b. Radar images c. Satellite based X-band images. The proposed research consisted of Artificial intelligence-based decision making for multi-modal sensing (direct measurement from multi-resolution sensors). A combination of sensors like Passive infrared (PIR), water level sensor, ultrasonic sensor, temperature sensor, pressure and altimeter sensors have been integrated on a single device to investigate the flash floods. The use of most suitable pair of measurement sensors can substantially enhance the advantage of more accuracy and reliability compared to a single sensor. In recent trends Particle swarm optimization is very popular for solving stochastic global optimization problems. The data was trained and processed by modified multi-layer feed forward neural network optimized by particle swarm optimization algorithm. Hybrid Modified Particle swarm optimization has been combined with feed forward neural network for the vigorous investigation of flash floods with less false alarm rate. Simulated results showed that the proposed research algorithm Modified multi-layer feed forward neural network optimized by Particle swarm optimization for multi-modal sensing performed very well in terms of evaluation parameters compared to other existing strategies with minimum false alarm ratio. Moreover, modified multi-layer feed forward neural network optimized by article swarm optimization algorithm results have been compared with the cuckoo search, modified cuckoo search, particle swarm optimization and Multi-layer perceptron neural network configurations for the validation purpose.

The traditional networks are facing difficulties in managing the services offered by cloud computing, big data, and the Internet of Things as the users have become more dependent on their services. Software-Defined Networking (SDN) has pulled enthusiasm in the integration process of technologies and function as per the user's requirements for both academia and industry, and it has begun to be embraced in actual framework usage. The emergence of SDN has given another idea to empower the focal programmability of the system. Because of the increasing demand and the scarcity of resources, the load balancing issue needs to be addressed efficiently to manage the incoming traffic and resources and to improve network performance. One of the most critical issues is the role of the controller in SDN to balance the load for having a better Quality of Service (QoS). Though there are few survey articles written on load balancing, there is no detail and systematic review conducted in load balancing in SDN. Hence, this paper extends and reviews the discussion with a taxonomy of current emerging load balancing techniques in SDN systematically by categorizing the techniques as conventional and artificial intelligence-based techniques to improve the service quality. The review also includes the study of metrics and parameters which have been used to measure the performance. This review would allow gaining more information on load balancing approaches in SDN and enables the researchers to fill the current research gaps.

The past few years have witnessed the emergence of a novel paradigm called cloud computing. CC aims to provide computation and resources over the internet via dynamic provisioning of services. There are several challenges and issues associated with implementation of CC. This research paper deliberates on one of CC main problems i.e. load balancing (LB). The goal of LB is equilibrating the computation on the cloud servers such that no host is under/ overloaded. Several LB algorithms have been implemented in literature to provide effective administration and satisfying customer requests for appropriate cloud nodes, to improve the overall efficiency of cloud services, and to provide the end user with more satisfaction. An efficient LB algorithm improves efficiency and asset's usage through effectively spreading the workload across the system's different nodes. This review research paper objective is to present critical study of existing techniques of LB, to discuss various LB parameters i.e. throughput, performance, migration time, response time, overhead, resource usage, scalability, fault tolerance, power savings, etc. The research paper also discusses the problems of LB in the CC environment and identifies the need for a novel LB algorithm that employs FT metrics. It has been found that traditional LB algorithms are not good enough and they do not consider FT efficiency metrics for their operation. Hence, the research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments. A novel algorithm that employs FT in LB is therefore proposed.

Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, and proficiently as the demand increases for food supplies. Traditional counting methods have numerous disadvantages, such as long delay times and high sensitivity, and they are easily disturbed by noise. In this research, the detection and counting of rice plants using an unmanned aerial vehicle (UAV) and aerial images with a geographic information system (GIS) are used. The technique is implemented in the area of forty acres of rice crop in Tando Adam, Sindh, Pakistan. To validate the performance of the proposed system, the obtained results are compared with the standard plant count techniques as well as approved by the agronomist after testing soil and monitoring the rice crop count in each acre of land of rice crops. From the results, it is found that the proposed system is precise and detects rice crops accurately, differentiates from other objects, and estimates the soil health based on plant counting data; however, in the case of clusters, the counting is performed in semi-automated mode.

It is brief according to the topic that it will focus on IoT based security issues. This study will focus on rigorous literature review which will provides us trustworthy path to satisfy the industry need. In curtail IoT is not just about interconnecting embedded devices or gadgets to the Internet, however, it is also fast and continuously growing to improve the ease or satisfaction of life. The motive of IoT services is to connect the entire globe through sensors. This study reviews the IoT methodologies in the light of qualitative research. The data analysis and synthesis focus over the last three years (2018 to 2020) which are based on the PRISMA block diagram for understanding. The review identifies the IoT privacy and security issues from a different perspective and also finds out which security issue is mostly discussed in the last few years which elaborated as a basis for further research. After a review of this paper, we can easily understand the different problem faces of IoT devices with the help of comparative analysis using summarize tables and graphical representation of IoT in context of the privacy and security challenges and issues face of IoT devices. After vigorous survey, it is clear that in future most of the paper will discuss data security and privacy, confidentiality, and authenticity.

A rectangular dielectric resonator antenna (DRA) has been excited using a unique conformal H-shaped metal strip. Using this excitation, the degenerate mode pair of first higher orderTEδ13x and TE1δ3y has been excited for bandwidth improvement and high gain. A broadband circular polarization (CP) over a bandwidth of ~20% (3.67-4.4 GHz) in conjunction with a wide impedance-matching bandwidth of ~27.7% (3.67-4.73 GHz) has been achieved. A CP beamwidth of 89° has been offered by the antenna in Φ = 0° plane and ~32° in Φ = 90° plane. A high gain of ~6.8 dBic has been provided by the antenna, which is a significant improvement to those circularly polarized rectangular DRAs reported in the literature for similar applications. This broad CP bandwidth and beamwidth can be considerably beneficial for the worldwide interoperability for microwave access (WiMAX) and satellite applications. Furthermore, the proposed antenna has been fabricated to validate the simulated results. The measured results have been observed to agree well with the simulated results.

A unique circularly polarized (CP) compact wearable dielectric resonator antenna (DRA) has been proposed for off-body communication in wireless body area network (WBAN) applications. The design is singly fed and a new H-shaped conformal metal strip has been used to excite the DRA. A circular polarization over a bandwidth of ~9.6% (7.47-8.25 GHz) in conjunction with an impedance matching bandwidth of ~20.7% (6.95-8.68 GHz) has been offered by the antenna. A prototype has been fabricated and measured. The antenna has been tested both in free space and on-body environment. A robust performance has been offered by the antenna against human body lossy effects. The measured results agree very well with the simulated results.

A wideband circularly polarized rectangular dielectric resonator antenna (DRA) fed by single feeding mechanism has been studied theoretically and experimentally. The study has been carried out to find out the effect on different antenna parameters when a parasitic strip is attached to the wall beside the conformal metallic feed. The DRA is excited by an E-shaped metal strip. The novel feed produces a narrow impedance matching |S11|

Research efforts in the field of sentiment analysis have exponentially increased in the last few years due to its applicability in areas such as online product purchasing, marketing, and reputation management. Social media and online shopping sites have become a rich source of user-generated data. Manufacturing, sales, and marketing organizations are progressively turning their eyes to this source to get worldwide feedback on their activities and products. Millions of sentences in Urdu and Roman Urdu are posted daily on social sites, such as Facebook, Instagram, Snapchat, and Twitter. Disregarding people’s opinions in Urdu and Roman Urdu and considering only resource-rich English language leads to the vital loss of this vast amount of data. Our research focused on collecting research papers related to Urdu and Roman Urdu language and analyzing them in terms of preprocessing, feature extraction, and classification techniques. This paper contains a comprehensive study of research conducted on Roman Urdu and Urdu text for a product review. This study is divided into categories, such as collection of relevant corpora, data preprocessing, feature extraction, classification platforms and approaches, limitations, and future work. The comparison was made based on evaluating different research factors, such as corpus, lexicon, and opinions. Each reviewed paper was evaluated according to some provided benchmarks and categorized accordingly. Based on results obtained and the comparisons made, we suggested some helpful steps in a future study.

Fifth-generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, lower power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising device-to-device networks, macro-cells, and different categories of small cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need for cellular service and the limited resources to provide it, capably handling network traffic and operation has become a problem of resource distribution. One of the utmost serious problems is to alleviate the jamming in the network in support of having a better QoS. However, although a limited number of review papers have been written on resource distribution, no review papers have been written specifically on 5G resource allocation. Hence, this article analyzes the issue of resource allocation by classifying the various resource allocation schemes in 5G that have been reported in the literature and assessing their ability to enhance service quality. This survey bases its discussion on the metrics that are used to evaluate network performance. After consideration of the current evidence on resource allocation methods in 5G, the review hopes to empower scholars by suggesting future research areas on which to focus.