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This research paper presents an efficient hybrid framework for the enhancement of X-ray images using the DWT (discrete wavelet transform), Gaussian, and Wiener filters. Digital X-ray images perform an important role in detecting explosives or illegal items. However, lately used X-ray images have certain shortcomings like noise, blurriness, and low contrast. Noise affects the object’s edges and values of intensity of pixels which gives rise to ambiguity for the system to separate objects and also hampers the decision-making process. To overcome these issues, a new hybrid framework is proposed here that consists of two layers. First, it reads high- and low-energy X-ray images and fuses them through various fusion rules in the wavelet realm using DWT and is reconstructed using iDWT (inverse discrete wavelet transform). Subsequently, the Gaussian filter is applied to the fused image to remove the Gaussian noise, and finally, the Wiener filter is used to reduce blurriness and improve image quality. The quality of the image is measured using PSNR (peak signal-to-noise ratio) and MSE (mean squared error) values, which exhibits that an average of the MSE values of fused images is 40% lower, and the PSNR values are 10% higher than the single images. Experimental results show that this hybrid framework successfully reduced noise and blurriness and enhanced contrast and accuracy.

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.

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.

Purpose The purpose of this paper is to provide an overview of smart agriculture systems and monitor and identify the technologies which can be used for deriving traditional agriculture system to modern agriculture system. It also provides the reader a broad area to work for the advancement in the field of agriculture and also explains the use of advanced technologies such as spectral imaging, robotics and artificial intelligence (AI) in the field of agriculture. Design/methodology/approach Smart uses of modern technologies were reviewed in the field of agriculture, which helps to monitor stress levels of plants and perform operations according to requirements. Operations can be irrigation, pests spray, monitoring crops, monitoring yield production, etc. Based on the literature review, a smart agriculture system is suggested. The parameters studied were spectral image processing, AI, unmanned aerial vehicle (UAVs) (fixed and rotatory), water or soil moisture, nutrients and pesticides. Findings The use of autonomous vehicles and AI techniques has been suggested through which the agriculture system becomes much more efficient. The world will switch to the smart agriculture system in the upcoming era completely. The authors conclude that autonomous vehicle in the field of science is time-saving and health efficient for both plants and workers in the fields. The suggested system increases the productivity of crops and saves the assets as well. Originality/value This review paper discusses the various contemporary technologies used in the field of agriculture and it will help future researchers to build on this research. This paper reveals that the UAVs along with multispectral, hyperspectral or red, green and blue camera (depends on the need) and AI are more suitable for the advancement of agriculture and increasing yield rate.

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.

In today’s competitive world software organizations are moving towards global software development environment where experts in teams from different geographical locations and different cultures are working on their roles and responsibilities to deliver a quality and cost-effective product. These teams are distributed in nature and work on same set of goals and objectives. They are undertaking their assignments to get multiple advantages like global exposure, new software world to explore, learning environment, etc. This change is having a significant effect on marketing and distribution as well as on the way software products are perceived, designed, developed, tested, and delivered to customers. Software experts in Global Software Development environment are also facing many challenges like as well. Moreover, risk is a big challenge out of other challenges but not many researchers addressed risk related to Time, Budget and Resources. Developing software projects to address business needs and requirements is so exceedingly complex and troublesome that it is common for software projects to overrun budgets and exceed scheduled completion dates. Business managers need the whole development process streamlined and more manageable, with projected cost and timings and for better decision making. The utilization of artificial intelligence techniques to manage risk is helpful when taking care of and assessing unstructured information because of the self-learning and self-healing nature of artificial intelligence-based algorithms. In this paper an Artificial Intelligence based architecture has been proposed that reduces risk by using Time, Budget and Resource constraints that will help decision maker to take decisions and get optimal solutions.

As Renewable Energy Sources are being used everywhere to minimize the burden of power plants and global emission. Therefore, Integrating Renewable energy with the grid is the trend nowadays in the Power distribution system. Power network get congested due to change in power flow from unidirectional to bidirectional. This paper discusses the importance of self-learning machine, in mitigating the power quality issues while integrating renewable energy with the power grid. Renewables energy are randomness in nature, due to its recurrent characteristic power network get effected. Different approaches like advanced forecasting, load flow analysis etc. are implemented using Artificial Intelligence (AI) techniques to improve the power network. AI machine can perform only those tasks which are pre-defined in its internal memory. Huge amount of data and time are required to train an AI machine. Whereas Active Learning Neural Network (ALNN) uses a self-learning approach to train itself through some model data. This paper mainly focusses on the importance of using ALNN in mitigating the power quality issues much better.

Routing protocols are used to transmit the packets from the source to the destination node in mobile ad hoc networks. The intruders seek chance to pierce into the network and becomes a cause of malfunctioning in the network. These protocols are always prone to attacks. During the phases of routing in different types of protocols, each of the attack finds a way to degrade the performance of the routing protocols. The reactive routing protocols DSR and AODV have lot of similar features and so are considered in this study. In order to transmit the packets safely, a secured approach using triple factor has been proposed. This triple factor computes the trust by using the direct information then verifies the reputation by collecting the information from the neighbouring nodes called distributed factor and uses cryptographic algorithm to ensure security. And to ensure that there are routes available to perform the routing process, the reasons for such attacks are studied so as to re-integrate back the nodes in to the network, once it has repented for being malicious before. The availability of routes increases the throughput.

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.

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.

Renewable energy has a great importance for power generation as it does not use the fossils fuels. Energy generated from alternative energy sources are weather dependent. To generate a continuous power to meet the load requirements, Battery energy storage system are used. Power conversion process must be much efficient as possible to convert the DC stored energy into AC. This conversion process is usually done by the help of inverters. This paper gives the brief overview on three main categories of multilevel inverter like cascaded h-bridge, neutral point clamped and flying capacitor multilevel inverter and highlights their advantages which can also help the scholars to deeply explore the categories of multilevel inverter. Harmonic elimination is usually done by controlling the switching angles of the inverter. Among all the switching angles techniques, selective harmonic elimination pulse width modulation (SHEPWM) technique is widely used, that has also discussed in this paper. Furthermore, to eliminate the harmonics using SHEPWM, it has the set of nonlinear transcendental equations, these set of equations can be effieceintly solved by the optimization methods. The most efficient and reliable optimization method like particle swarm optimization has been discussed with multiple objective functions in this paper. This paper will help the scholars to understand the finest category of multilevel inverter for harmonic elimination in terms of efficiency and output quality.

A circularly polarized rectangular dielectric resonator antenna (DRA) has been designed (3.7–4.2 GHz) for WiMAX, satellite, and sub-6 GHz 5G NR band communications. A new T-shaped monopole has been used to excite the rectangular DRA. Using proposed conformal metal strip, the higher-order degenerate modes TE δ 13 x and TE 1 δ 3 y have been excited to achieve circular polarization (CP) with improved gain. Further, the bandwidths enhancement has been achieved by placing a parasitic patch of optimized height and width at a specific distance from T-shaped strip on DRA surface to cover the desired frequency band. A signaficant enhancement of ~ 73% in the impedance-matching bandwidth and ~ 75.5% in axial ratio (AR) bandwidth has been achieved. The | S 11 | ≤ 10 dB over a wide bandwidth of ~ 17.3% has been offered by the antenna. A broad axial ratio bandwidth of ~ 17.2% along with a useful gain of ~ 6.32 dBic has been provided by the antenna. The antenna designing and optimization has been carried out using CST® Microwave Studio. A reasonable agreement between simulated and measured results has been observed.