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IntroductionOur research addresses the critical need for accurate segmentation in medical healthcare applications, particularly in lung nodule detection using Computed Tomography (CT). Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning.MethodsOur model was trained and evaluated using several deep learning classifiers on the LUNA-16 dataset, achieving superior performance in terms of the Probabilistic Rand Index (PRI), Variation of Information (VOI), Region of Interest (ROI), Dice Coecient, and Global Consistency Error (GCE).ResultsThe evaluation demonstrated a high accuracy of 91.76% for parameter estimation, confirming the effectiveness of the proposed approach.DiscussionOur investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. We proposed a novel segmentation model to identify lung disease from CT scans to achieve this. We proposed a learning architecture that combines U-Net with a Two-parameter logistic distribution for accurate image segmentation; this hybrid model is called U-Net++, leveraging Contrast Limited Adaptive Histogram Equalization (CLAHE) on a 5,000 set of CT scan images.

Smart cities are characterized by the integration of various technologies and the use of data to achieve several objectives. These objectives include the creation of efficiencies, boosting economic development, expanding sustainability, and improving the overall quality of life for individuals residing and working within the urban environment. The aim of this study is to analyze the future of smart cities with respect to developing countries, specifically Jordan as the case. This analysis is based on the opinions and feedback from the field experts. In this study, we are tapping into multiple domains of smart cities such as smart governance, education, healthcare, communication, transportation, security, energy, and sustainability. The field experts’ consensus was developed with the Delphi method. The Delphi survey comprises eight questions to assess the views about smart city adoption and development with respect to Jordan. The results and findings of this study revealed specific challenges and opportunities in smart city adoption with respect to Jordan. The experts’ opinions have validated the study of the 2023 Smart City Index report. They have offered crucial input and future guidance for the adoption of smart cities in Jordan. Additionally, they have indicated which domains of smart cities should be prioritized during the implementation in Jordan.

In hilly areas, landslides can occur due to natural factors such as heavy rainfall, earthquakes, moisture in soil, or man-made factors like unplanned constructions. Landslides can be disastrous leading to a huge loss of property and lives which can be avoided using automatic prediction. Recently, machine learning algorithms have been applied to automatically identify landslides. Numerous feature extraction and classification-based approaches have been implemented on satellite images for semiautomatic detection and prediction of landslides. However, limited research has been done on fully automatic detection with acceptable accuracy. The most challenging task in the classification and prediction of landslides from satellite images is to find an appropriate database for training and yield highly accurate testing results. The primary agenda of a comprehensive study of various techniques used for the detection and classification of landslides using satellite images is to identify the research gap. The secondary objective aims to propose a prototype of novel approach for the same task. Fifty papers based on machine learning and deep learning algorithms from reputed journals are considered for analysis. This article summarizes the performances of different classification techniques from recent literature followed by comparison and discussion with respect to accuracy. Based on the gap identified an effective prototype of the landslide classification approach is proposed. A slightly modified version of the deep learning model ResNet101 is proposed which yields an accuracy of 96.88% when tested on an augmented Beijing dataset of 770 satellite images. The article also offers the researchers the latest status, overview, and potential avenues of machine and deep learning algorithms for landslide detection. The techniques discussed will serve as a valuable resource for identifying research gaps, guiding new researchers, and fostering innovative exploration in the field of landslide classification using satellite images.

Active Noise Control (ANC) is an effective technique for removing undesirable disturbances based on destructive interference between two noises (i.e., the superposition principle). To reduce the Non-Gaussian distribution of impulsive noises, the ANC is implemented using a prominent Filtered Cross Least Mean Square (FxLMS) method that relies on reducing the unwanted noise. The standard FxLMS method fails to adapt to its specifications, resulting in poor convergence and instability in the presence of impulsive noises and a non-linear response from the ANC system’s components. The Least Square family of Recursive Least Square (RLS) increases ANC performance by offering superior convergence performance to traditional stochastic algorithms. This paper proposes a novel technique called the recursive non-linear active threshold-based and modified gain FXRLS (NAMGFxRLS) algorithm to overcome the inadequacies of impulsive noise and non-linearity issues in the ANC. The suggested technique aims to automatically modify weights by the various sample processes, i.e., employing the FxRLS algorithm’s updated gain to adjust the error and reference signals and appropriately deploying the threshold. The potential of the suggested strategy is proved by simulated results of convergence speed, stability, and excellent Mean Noise Reduction of roughly 52.8 % for various noises when compared to previous approaches, notably large impulsive noises.

Background: Plant-derived products or extracts are widely used in folk/traditional medicine to treat several infections, ailments, or disorders. A well-known medicinal herb, Myrtus communis is an evergreen fragrant plant native to the Mediterranean region that has been used for ages in traditional medicine around the world. Materials and methods: The microplate alamarBlue assay and the well diffusion method were used to evaluate the zone of inhibition and MIC, respectively. The double-disc diffusion method was used to investigate the synergy between antibiotics and the extract. The crystal violet method was used to investigate biofilm development. The SulphoRhodamine-B assay and DNA flow cytometry were used to investigate the proliferation and subsequent distribution of cells among different phases of the cell cycle. The apoptotic and necrotic phases of the cancer cells were examined using flow cytometry in conjunction with Annexin V-FITC/PI labeling. Using the IBM SPSS statistical program, a one-way ANOVA with Tukey’s post hoc test was employed for statistical analysis. Results: The ethanolic leaf extract of M. communis showed a strong growth inhibition effect (zone of inhibition: 20.3 ± 1.1–26.3 ± 2.5 mm, MIC: 4.88–312.5 µg/mL, and MBC: 39.07–1250 μg/mL) against several rapidly growing and slow-growing mycobacterial strains in a dose-dependent manner. Damage to the cell wall of bacterial cells was determined to be the cause of the antimycobacterial action. The extract inhibited biofilm formation (MBIC of 9.7 µg/mL) and eradicated already-formed mature and ultra-mature biofilms of M. smegmatis, with MBEC values of 78 µg/mL and 156 µg/mL, respectively. Additionally, the extract exhibited potent anticancer effects against diverse cancer cell lines of the breast (MCF-7), liver (HepG2), cervix (HeLa), and colon (HCT116) (IC50 for HCT116: 83 ± 2.5, HepG2: 53.3 ± 0.6, MCF-7: 41.5 ± 0.6, and HeLa: 33.3 ± 3.6) by apoptosis after arresting the cells in the G1 phase of the cell cycle. Conclusions: These results suggest that M. communis leaf extract is a potential source of secondary metabolites that could be further developed as potential anticancer and antimycobacterial agents to treat diverse types of cancers and mycobacterial infections.

Abstract Background Necrotizing pneumonia is rare in children and is one of the most serious complications of a lung infection caused by antibiotic failure. We present a 12-year-old leukopenic child with a long-lasting lung infection, presenting as having a lung hydatid cyst, but diagnosing with necrotizing pneumonia in the right bilobed lung. Failure to medical treatment and ongoing leukopenia justified surgical intervention with positive results. Case presentation The patient was referred to our teaching hospital’s pediatric surgery department. He had previously been diagnosed with intestinal tuberculosis (TB) and received anti-TB treatment. On referral to our hospital, the patient was suffering from restlessness, frequent coughing, fever, vomiting, and diarrhea. Following the completion of the clinical work-up, a blood test revealed leukopenia (white blood cell count of 2100/microliter), a normal platelet count, and a lesion in the right lung. Computerized tomography scanning (CT-Scan) image reported a lung hydatid cyst. In the pediatrics ward, a broad-spectrum antibiotics regimen with triple-antibiotic therapy (linezolid, vancomycin, and metronidazole) was instituted and continued for a week with no response, but worsening of the condition. In the pediatric surgery ward, our decision for surgical intervention was due to the failure of medical treatment because of a pulmonary lesion. Our team performed right lung upper lobe anterior segment wedge resection due to necrotizing pneumonia and followed the patient 45 days post-operation with a reasonable result. Conclusion Living in remote rural areas with low resources and inaccessibility to proper and specialized diagnostic and treatment centers will all contribute to an improper diagnosis and treatment of lung infection. In total, all of these will increase the morbidity and mortality due to lung necrosis in the pediatric population, regardless of their age. In low-resource facilities, high-risk patients can benefit from surgical intervention to control the ongoing infection process.

This paper discusses the improvement of fifth-grade students’ understanding of Pancasila values by developing the e-NIPA (Edukasi Nilai Pancasila) application, which is an android application that is appropriate for students’ interests and abilities. This application can be operated by students without using an internet network. The research was conducted because the fifth-grade students did not understand Pancasila values in elementary school. The method used in the research is the R&D (Research and Development) method with the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) development model. The research was conducted on fifth-grade students, which consisted of 21 students in one of the elementary schools in Sumedang City. The results of the research indicate that this product is feasible to use after applying the validation by media and material experts in the very good category with a percentage of 95.5%. The results of the research on the research subjects were processed using the Paired Sample T-test by comparing the results of the students’ average pretest and posttest scores, which begins with conducting a normality test on the data. The results imply that the data were normally distributed and there was a difference between the average students’ pretest and posttest scores and it was obtained that the students’ average scores increased at 38% based on N-gain. It implies that the e-NIPA application can improve students’ understanding of the values of Pancasila.

Ceftazidime/avibactam (CAZ-AVI) is FDA-approved for managing infections caused by resistant gram-negative bacilli, particularly infections via carbapenem-resistant Enterobacterales pathogens. The clinical data are still limited, particularly those in Saudi Arabia. The present study is a retrospective cohort study that was carried out at the Armed Forces Hospital in the southern region of Saudi Arabia to compare the clinical and microbiological outcomes for CAZ-AVI-treated patients as monotherapy and as an add-on to standard therapy for carbapenem-resistant Klebsiella pneumonia (CRKP) OXA-48 infections to those treated with standard drugs. The study included CRKP OXA-48-like infected patients who were administered antibiotics for more than seven days from 1 August 2018 to May 2023. Patients’ baseline characteristics and demography were extracted from the clinical records, and their clinical/microbiology efficiencies were assessed as per the corresponding definitions. Univariate and multivariate logistic regressions were conducted to identify the potential independent variable for CAZ-AVI efficiency. A total of 114 patient files were included for the evaluation. Among these patients, 64 used CAZ-AVI combined with standard therapy and were included in the intervention group, and 50 of them used standard therapy and were included in the comparative group. Following analysis, CAZ-AVI’s clinical success was 42.2% (p = 0.028), while the intervention versus comparative groups showed decreased 30-day all-cause mortality (50.0% versus 70.0%; p = 0.036) and infection recurrence (7.8% versus 24.0%; p = 0.019), as well as substantially increased rates of microbial eradication (68.8% versus 42.0%; p = 0.007). CAZ-AVI add-on therapy rather than monotherapy showed statistically significant favored clinical and microbial outcomes over the standard therapy. Furthermore, sex (female %), ICU admission, and fever were negatively associated with patients’ 30-day all-cause mortality, serving as independent negative factors. Only fever, CRP bio levels, inotropes, and ICU admissions were significant predictors influencing the CAZ-AVI’s clinical efficiency. The duration of CAZ-AVI therapy positively influenced CAZ-AVI’s microbial eradication, while both WBC counts and fever experiences were negative predictors. This study shows the effective usage of CAZ-AVI against CRKP OXA-48-like infections. The influencing independent variables depicted here should recommend that clinicians individualize the CAZ-AVI dose based on co-existing risk factors to achieve optimal survival and efficacy. Prospective multicenter and randomized control studies are recommended, with individualized CAZ-AVI precision administration implemented based on patients’ characteristics.

Back pain is an issue affecting millions of people throughout the world. Research on back pain root cause detection is immense. The lumbar spine is a lower back region of the backbone that could also be responsible for back pain. Research on issues related to lumbar spine disease (LSD) is limited. The lumbar spine is critical for the human body as it supports the weight of the body. Many diseases can affect the lumbar spine adversely. This research is directed towards the detection of LSDs using optimized feature extraction and selection phases. Furthermore, the linearity-based model is used for feature selection, selecting only the best possible features to reduce the missed classification degree. The flow of the proposed research work is divided into phases. In the first phase, data collection is performed. Data collection in the proposed work includes both real-time and benchmark magnetic resonance imaging (MRI) datasets. The MRI dataset collected from the hospital is validated by medical experts. After data collection, pre-processing is applied. This step removes the noise from the collected dataset. The pre-processing mechanism is performed using histogram equalization, median filtering, validation, and normalization. After the pre-processing phase, background subtraction and region of interest (ROI) detection are performed using the region-cut mechanism. Optimal feature extraction is achieved using a differential spider monkey optimization (SMO), and feature selection is performed using a linearity-based convolutional neural network (CNN) model. In the end, ensemble-based classification is used for disease prediction. The validation of the result is conducted through classification accuracy, specificity, sensitivity, and F-Score. The high classification accuracy of 96% is achieved with multi support vector machine (MSVM), 94% with random forest (RF), 93.5% with a decision tree (DT), and 91% with the Naïve Bayes (NB) approach, proving the validity of the proposed approach.

Brain tumour (BT) detection involves the process of identifying the presence of a brain tumour in medical imaging, such as MRI scans. BT detection often relies on medical imaging techniques, such as MRI (Magnetic Resonance Imaging), CT (Computed Tomography), or PET (Positron Emission Tomography) scans. Early detection of BT is important and MRI is one of the primary imaging techniques used to diagnose and assess BT. Deep learning (DL) techniques, particularly convolutional neural networks (CNNs) have shown promising results in assisting with BT detection on MRI scans. This study designs an Ensemble Learning Driven Computer-Aided Diagnosis Model for Brain Tumor Classification (ELCAD-BTC) technique on MRIs. The presented system purposes to detect and classify various steps of BTs. The presented system contains a Gabor filtering (GF) approach to remove the noise and increase the quality of MRI images. Moreover, ensemble learning of three DL models namely EfficientNet, DenseNet, and MobileNet is utilized as feature extractors. Furthermore, the denoising autoencoder (DAE) approach can be exploited to detect the presence of BTs. Finally, a social spider optimization algorithm (SSOA) was carried out for the hyperparameter tuning of the DL models. For simulating the improved BT classification outcome, a brief set of simulations occur on BRATS 2015 database.

The augmentation of hyperspectral images requires the design of high-density feature analysis & band-fusion models that can generate multimodal imagery from limited information sets. The feature analysis models use deep learning operations to maximize inter-class variance while minimizing inter-class variance levels for efficient classification operations. When combined with intelligent band-fusion methods, such models allow the augmentation model to enhance its classification efficiency under different use cases. Existing band-fusion-based augmentation models for hyperspectral images do not incorporate continuous efficiency enhancements and showcase higher complexity levels. Furthermore, these models can’t be scaled for more varied use cases because their use is restricted to specific image types. To overcome these issues, we designed a novel multimodal hybrid bioinspired model for the augmentation of hyperspectral imagery via iterative learning for continuous efficiency enhancements. The proposed model initially represents input images into Fourier, Laplacian, Cosine, multimodal Wavelet, Mellin, and Z-Transform domains, which will assist in describing the images in multimodal domains. These transformed image sets are passed through a convolutional filter to extract windowed feature sets. A Grey Wolf Optimizer (GWO) is used to identify high inter-class variance features from the extracted image sets, which assists in selecting transformed images that can help improve hyperspectral classification performance. The selected hyperspectral images are fused via a Bacterial Foraging Optimization (BFO) model, which assists in reducing intra-class variance levels. The final set of selected images is intelligently augmented via Particle Swarm Optimization (PSO), which performs rotation, zooming, shifting, and brightness variation operations selectively. These augmented images are classified via a customized VGGNet-19-based Convolutional Neural Network (CNN) classifier that assists in continuously estimating accuracy levels for different application scenarios. Based on these accuracy levels, the model is reconfigured via hyperparameter tuning of GWO and PSO optimizers. Due to combining these models and incremental accuracy optimizations, the proposed model has improved its hyperspectral classification accuracy by 10.6% and precision by 10.4%, as compared to standard deep learning-based augmentation techniques.

Machine learning has become ubiquitous across all industries, including the relatively new application of predicting antimicrobial resistance. As the first bibliometric review in this field, we expect it to inspire further research in this area. The review employs standard bibliometric indicators such as article count, citation count, and the Hirsch index (H-index) to evaluate the relevance and impact of the leading countries, organizations, journals, and authors in this field. VOSviewer and Biblioshiny programs are utilized to analyze citation and co-citation networks, collaboration networks, keyword co-occurrence, and trend analysis. The United States has the highest contribution with 254 articles, accounting for over 37.57% of the total corpus, followed by China (103) and the United Kingdom (78). Among 58 publishers, the top four publishers account for 45% of the publications, with Elsevier leading with 15% of the publications, followed by Springer Nature (12%), MDPI, and Frontiers Media SA with 9% each. Frontiers in Microbiology is the most frequent publication source (33 articles), followed by Scientific Reports (29 articles), PLoS One (17 articles), and Antibiotics (16 articles). The study reveals a substantial increase in research and publications on the use of machine learning to predict antibiotic resistance. Recent research has focused on developing advanced machine learning algorithms that can accurately forecast antibiotic resistance, and a range of algorithms are now being used to address this issue.

To identify the associations between different genotypes of TLR9 -1486T/C (rs187084) with gastric cancer patients and reveal their relation to Helicobacter pylori virulence genes (cagA, sodB, hsp60 and vacA). Patients with gastric cancer were recruited to our study, diagnosed both endoscopically and histopathologically. H. pylori were isolated from gastric samples by culture and PCR amplification of the glmM gene. Virulence genes cagA, sodB, hsp60, and vacA were detected by multiplex PCR. Blood samples were used for genotyping of TLR9 -1486T/C (rs187084) by PCR-RFLP. Out of 132 patients with gastric cancer, 106 (80.3%) were positive for H. pylori. A similar number of healthy participants was recruited as controls. The prevalence of cagA, sodB, hsp60, and vacA genes among H. pylori was 90.6%, 70.8%, 83.0%, and 95.3%, respectively. The vacA gene alleles had a prevalence of 95.3% for vacAs1/s2, 52.8% for vacAm1, and 42.5% for vacAm2. The CC genotype of TLR9 -1486T/C had a significantly higher frequency in gastric cancer patients when compared to healthy participants (p = 0.045). Furthermore, the CC genotype demonstrated a significant association with H. pylori strains carrying sodB, hsp60, and vacAm1 virulence genes (p = 0.021, p = 0.049, and p = 0.048 respectively). Patients with CC genotype of TLR9 -1486T/C (rs187084) might be at higher risk for the development of gastric cancer, and its co-existence with H. pylori strains carrying sodB, hsp60, or vacAm1 virulence genes might have a synergistic effect in the development of gastric cancer. Further studies on a wider scale are recommended.

Deep learning has paved the way for critical and revolutionary applications in almost every field of life in general. Ranging from engineering to healthcare, machine learning, and deep learning has left their mark as the state-of-the-art technology application which holds the epitome of a reasonable high benchmarked solution. Incorporating neural network architectures into applications has become a common part of any software development process. In this paper, we perform a comparative analysis on the different transfer learning approaches in the domain of hand-written digit recognition. We use two performance measures, loss, and accuracy. We later visualize the different results for the training and validation datasets and reach a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with a simultaneous focus on the suitable model selection procedure for the digit recognition problem.

The growth in sustainable generation technology such as fuel cell, wind energy conversion system, photovoltaic system, increase in fuel cost, energy necessity and the reduction in the fossil fuel reserve, for better power quality and reliability, is obliging the power sector to use the renewable based energy sources. In India, wind energy is gradually becoming an important and significant energy resource. Keeping in opinion the aforementioned wind energy prediction is becoming an essential study for harnessing the wind energy prospective. This paper proposes an effective technique based on intelligent approach for predicting wind power in different areas. This technique is based on using an intelligent model concerning the predicted gap to its similar one and two year old data. There are many intelligent and conventional models existed in literature for the wind power prediction like support vector machines (SVM), back propagation (BP) prediction etc. In this paper an effective fuzzy logic and model predictive control based models have been developed and offered for the wind power prediction for microgrid application by using air density and wind speed as the input parameters for fuzzy system. The outcomes are compared with the computed data and existing models and it can be observed that the different errors are found within the permissible limits. The outcomes obtained from fuzzy based technique are very close to calculated values if compared with model predictive based technique. Hence, the proposed models can be employed for the prediction of wind speed and wind power generation in the selected stations. The existing models results are compared with Kolkata city outcomes. The Error RMSE with Support vector machine, Back propagation, Model of forecast error correction +SVM and Model of forecast errors correction +BP, Neural Network method, model predictive based system, and proposed fuzzy logic based system are 30.48%, 32.83%, 26.81%, 28.58%, 1.1431%, 1.38% and 1.12% respectively. Therefore, the proposed techniques provide the best results and even these are observed within the suitable limits. Additionally, the achieved outcomes can be used for Microgrid/SmartGrid applications.

Breast cancer is one the most critical disease and suffered many people around the world. The efficient and correct detection of breast cancer is still needed to ensure this medical issue although the researchers around the world are proposed different diagnostic methods for detection of this disease, however these existing methods still needed further improvement to correct and efficient detection of this disease. In this study, we proposed a new breast cancer identification method by using machine learning algorithms and clinical data. In the proposed method supervised (Relief algorithm) and unsupervised (Autoencoder, PCA algorithms) techniques have been used for related features selection from data set and then these selected features have been used for training and testing of classifier support vector machine for accurate and on time detection of breast cancer. Additionally, in the proposed approach k fold cross validation method has been used for model validation and best hyperparameters selection. The model performance evaluation metrics have been used for model performance evaluation. The BC data sets have been used for testing of the proposed method. The analysis of experimental results has been demonstrated that the features selected by Relief algorithm are more related for accurate detection of Breast cancer instead of features selected by Auotencoder and PCA algorithms. The proposed method has been attained high results in terms of accuracy on selected feature selected by Relief algorithm and achieved 99.91% accuracy. We have been employed McNemar's statistical test for performance comparison of our different models. Further, the proposed method performance has been compared with baseline methods in the literature and the proposed method performance is high as compared to base line methods. Due to the high performance of the proposed method (Relief-Support vector machine) we highly recommended it for the diagnosis of breast cancer. In addition, the proposed method can be easily incorporated into the healthcare system for reliable diagnosis of Breast cancer.

This research was conducted in Kajongan Village, Purbalingga Regency. This study aims to analyze the value added and contribution of the broom craft business to the household income of entrepreneurs in a period of one year. The types of brooms that were studied are gelagah brooms, ijuk brooms, and hamada (brooms made from wheat plants). Respondents in this study amounted to 34 broom entrepreneurs. Data collection techniques were carried out through questionnaires and interviews. The analytical method used is the hayami method value added analysis and income contribution analysis. This study is a development from previous research that uses the Hayami method to analyze the value added from craft products. Based on the results of the study, it can be concluded that the highest value added ratio is produced by the production of hamada brooms. Then the second highest is produced from the production of ijuk brooms. The production of gelagah brooms contributes the lowest value added and value added ratio. Meanwhile, the largest to the smallest income contribution was generated from the production of hamada brooms, gelagah brooms, and ijuk brooms, respectively. The implication is that entrepreneurs need to choose the best raw materials and provide innovations to improve product quality so that the value added can be increased. In addition, entrepreneurs need to expand the market so that the number of sales can be increased. In the end, this will have an impact on increasing the household income of entrepreneurs. Keywords: Value Added Craft, Broom Craft, Income Contribution

Currently, the whole world is struggling with the biggest health problem COVID-19 name coined by the World Health Organization (WHO). This was raised from China in December 2019. This pandemic is going to change the world. Due to its communicable nature, it is contagious to both medically and economically. Though different contributing factors are not known yet. Herein, an effort has been made to find the correlation between temperature and different cases situation (suspected, confirmed, and death cases). For a said purpose, k-means clustering-based machine learning method has been employed on the data set from different regions of China, which has been obtained from the WHO. The novelty of this work is that we have included the temperature field in the original WHO data set and further explore the trends. The trends show the effect of temperature on each region in three different perspectives of COVID-19 – suspected, confirmed and death.

E-Learning Education systems are gaining attention day-to-day because of their inclusive pertinence in the distance education system. Due to COVID-19, the online learning education system has become very popular. Most probably, all education systems have been using the IoT-based E-Learning system to continue the students' education without hindrance during the COVID lockdown. Several E-Learning IoT schemes are explored that reflect privacy and security, but still, there is no detailed scheme; hence, it needs a sustainable, secure E-Learning IoT system. The characteristics and prospects of the Internet of Things are discussed in this article. By analyzing the various functions and capabilities of the Internet of Things, this article aims to provide an overview of the various advantages and challenges of using the platform for e-learning. This paper proposed the E-Learning IoT architecture with Blockchain technology, with layers of different IoT and Blockchain concepts to secure the online education system. Also, the block diagram of the proposed architecture demonstrates how students can securely access or interact with the online learning system through Blockchain technology. By implementing the proposed e-learning IoT architecture, universities and colleges can improve their distance learning programs and increase efficiency without affecting their academic activities. Finally, the study found that e-learning positively impacts students' learning experience and overall quality of education. It also exhibited a significant positive impact on their flexibility and academic productivity.

Pothohar region of the Punjab, Pakistan is rain-fed with undulating topography. Soils of the area are predominantly loose with low water retention capacity and are vulnerable to erosion. Erratic and high intensity rainfall causes land erosion in the area producing gullies and gorges. Therefore, agriculture in the area faces twin menace of soil erosion and moisture stress. On-farm water control structures are among the important measures to control soil erosion and conserve moisture to enhance agriculture productivity. Present study was designed to evaluate the impact of these structures on soil moisture conservation and physio-chemical characteristics of soil in wheat growing fields. Wheat was sown in the fields, with and without structures during 2009 to 2013. It has been found that construction cost of such structures is about one-fourth that of conventionally used structures in the area whereas reduction in soil erosion has been observed up to 10% with 21% soil moisture conservation as compared to control. Soil fertility level improved significantly as a result of reduced run off and nutrient loss. A substantial improvement in wheat yield up to 15% due to increased fertility and moisture content was also recorded. These structures facilitated a safe disposal of surplus rain water which minimized the gully development, improved the soil structure, checked fertility loss and improved soil moisture retention.

The influential stage of Internet of Things (IoT) has reformed all fields of life in general but specifically with the emergence of artificial intelligence (AI) has drawn the attention of researchers into a new paradigm of life standard. This revolution has been accepted around the globe for making life easier with the use of intelligent devices such as smart sensors, actuators, and many other devices. AI-enabled devices are more intelligent and capable of doing a specific task which saves a lot of resources and time. Different approaches are available in the existing literature to tackle diverse issues of real life based on AI and IoT systems. The role of decision-making has its own importance in the AI-enabled and IoT systems. In-depth knowledge of the existing literature is dire need of the research community to summarize the literature in effective way by which practitioners and researchers can benefit from the prevailing proofs and suggest new solutions for solving a particular problem of AI-enabled sensing and decision-making for the IoT system. To facilitate research community, the proposed study presents a systematic literature review of the existing literature, organizes the evidences in a systematic way, and then analyzes it for future research. The study reported the literature of the last 5 years based on the research questions, inclusion and exclusion criteria, and quality assessment of the selected study. Finally, derivations are drawn from the included paper for future research.

In this work, the Haar collocation scheme is used for the solution of the class of system of delay integral equations for heterogeneous data communication. The Haar functions are considered for the approximation of unknown function. By substituting collocation points and applying the Haar collocation technique to system of delay integral equations, we have obtained a linear system of equations. For the solution of this system, an algorithm is developed in MATLAB software. The method of Gauss elimination is utilized for the solution of this system. Finally, by using these coefficients, the solution at collocation points is obtained. The convergence of Haar technique is checked on some test problems.

Autonomous vehicles offer various advantages to both vehicle owners and automobile companies. However, despite the advantages, there are various risks associated with these vehicles. These vehicles interact with each other by forming a vehicular network, also known as VANET, in a centralized manner. This centralized network is vulnerable to cyber-attacks which can cause data loss, resulting in road accidents. Thus, to prevent the vehicular network from being attacked and to prevent the privacy of the data, key management is used. However, key management alone over a centralized network is not effective in ensuring data integrity in a vehicular network. To resolve this issue, various studies have introduced a blockchain-based approach and enabled key management over a decentralized network. This technique is also found effective in ensuring the privacy of all the stakeholders involved in a vehicular network. Furthermore, a blockchain-based key management system can also help in storing a large amount of data over a distributed network, which can encourage a faster exchange of information between vehicles in a network. However, there are certain limitations of blockchain technology that may affect the efficient working of autonomous vehicles. Most of the existing blockchain-based systems are implemented over Ethereum or Bitcoin. The transaction-processing capability of these blockchains is in the range of 5 to 20 transactions per second, whereas hashgraphs are capable of processing thousands of transactions per second as the data are processed exponentially. Furthermore, a hashgraph prevents the user from altering the order of the transactions being processed, and they do not need high computational powers to operate, which may help in reducing the overall cost of the system. Due to the advantages offered by a hashgraph, an advanced key management framework based on a hashgraph for secure communication between the vehicles is suggested in this paper. The framework is developed using the concept of Leaving of Vehicles based on a Logical Key Hierarchy (LKH) and Batch Rekeying. The system is tested and compared with other closely related systems on the basis of the transaction compilation time and change in traffic rates.

AquaCrop is a water-driven model that simulates the effect of environment and management on crop production under deficit irrigation. The model was calibrated and validated using three databases and four irrigation treatments (i.e., 100%ET, 80%ET, 70%ET, and 50%ET). Model performance was evaluated by simulating canopy cover (CC), biomass accumulation, and water productivity (WP). Statistics of root mean square error (RMSE) and Willmott’s index of agreement (d) showed that model predictions are suitable for non-stressed and moderate stressed conditions. The results showed that the simulated biomass and yield were consistent with the measured values with a coefficient of determination (R2) of 0.976 and 0.950, respectively. RMSE and d-index values for canopy cover (CC) were 2.67% to 4.47% and 0.991% to 0.998% and for biomass were 0.088 to 0.666 ton/ha and 0.991 to 0.999 ton/ha, respectively. Prediction of simulated and measured biomass and final yield was acceptable with deviation ˂10%. The overall value of R2 for WP in terms of yield was 0.943. Treatment with 80% ET consumed 20% less water than the treatment with 100%ET and resulted in high WP in terms of yield (0.6 kg/m3) and biomass (1.74 kg/m3), respectively. The deviations were in the range of −2% to 11% in yield and −2% to 4% in biomass. It was concluded that AquaCrop is a useful tool in predicting the productivity of cotton under different irrigation scenarios.

Abstract Obesity is the most common chronic disease, due to its ignorance in society. It gives birth to other diseases such as endocrine. The objective of this research is to analyze the different trends of each BMI category and predict its related serious consequences. Data mining based Support Vector Machine (SVM) technique has been applied for this and the accuracy of each BMI category has been calculated using Receiver Operating Characteristics (ROC), which is an effective method and potentially applied to medical data sets. The Area Under Curve (AUC) of ROC and predictive accuracy have been calculated for each classified BMI category. Our analysis shows interesting results and it is found that BMI ≥ 25 has the highest AUC and Predictive accuracy compares to other BMI, which claims a good rank of performance. From our trends, it has been explored that at each BMI precaution is mandatory even if the BMI < 18.5 and at ideal BMI too. Development of effective awareness, early monitoring and interventions can prevent its harmful effects on health.

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.

Data mining is an information exploration methodology with fascinating and understandable patterns and informative models for vast volumes of data. Agricultural productivity growth is the key to poverty alleviation. However, due to a lack of proper technical guidance in the agriculture field, crop yield differs over different years. Mining techniques were implemented in different applications, such as soil classification, rainfall prediction, and weather forecast, separately. It is proposed that an Artificial Intelligence system can combine the mined extracts of various factors such as soil, rainfall, and crop production to predict the market value to be developed. Smart analysis and a comprehensive prediction model in agriculture helps the farmer to yield the right crops at the right time. The main benefits of the proposed system are as follows: Yielding the right crop at the right time, balancing crop production, economy growth, and planning to reduce crop scarcity. Initially, the database is collected, and the input dataset is preprocessed. Feature selection is carried out followed by feature extraction techniques. The best features were then optimized using the recurrent cuckoo search optimization algorithm, then the optimized output can be given as an input for the process of classification. The classification process is conducted using the Discrete DBN-VGGNet classifier. The performance estimation is made to prove the effectiveness of the proposed scheme.

The phenomenon of Global Software Development (GSD) has attracted the interest of businesses all over the world. It brings together partners from various national and corporate cultures to develop applications with numerous advantages, including access to a vast labor pool, cost savings, and round the clock growth. GSD, on the other hand, is technologically and organizationally diverse and poses a number of obstacles for the development team, such as geographical distance, cultural differences, communication and language barriers. Global services are provided by selecting one of the suitable global delivery options, i.e., the onshore model, nearshore model or offshore model. Experts typically choose one of the models based on the nature of the project and the needs of the customer. However, the vendors and clients lack an adequate decision support system that can assist them in making suitable sourcing decisions. Therefore, the current study presents a Multi-Criteria Decision Making (MCDM) model for offshore outsourcing decisions of application maintenance. To achieve our target, two systematic literature reviews were conducted that explored a list of 15 influencing factors. The identified factors were further evaluated in the outsourcing industry by performing an empirical study that resulted in a list of 10 critical success factors. We propose a sourcing framework based on the critical success factors that can assist decision makers in adopting a suitable sourcing strategy for the offshore outsourcing of application maintenance. In order to further enhance the decision-making process, the MCDM model is developed based on the Analytic Hierarchy Process (AHP). The MCDM model is evaluated with three case studies in highly reputable international companies, including IBM Stockholm, Sweden, Vattenfall AB, Stockholm, Sweden and a London based company in the United Kingdom. The outcomes of these case studies are further reviewed and validated by the outsourcing specialists in other firms. The proposed model is used as a decision support system that determines the ranking of sourcing alternatives and suggests the most suitable option for application maintenance offshoring.

Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.

This study was aimed to determine the acylglycerols composition, quality characteristics, and protective role of dietary pumpkin seed oil (PSO) in rabbits. PSO was thermally oxidized and analyzed for quality characteristics and acylglycerols composition using reversed phase high performance liquid chromatography with diode array detection (HPLC-DAD). Oxidized and un-oxidized oil samples were fed to the rabbits in different doses for 2 weeks. The changes in the serum biochemistry, hematology, and liver histology were studied. The levels of quality parameters such peroxide value (PV), anisidine value (AV), total phenolic contents (TPC), thiobarbituric acid reactive substances (TBARS), conjugated dienes (CD) and conjugated trienes (CT) significantly increased with thermal treatment. HPLC analyses revealed 10 individual triacylglycerols (TAGs), total di-acylglycerols (DAGs), mono-acylglycerols (MAGs), and total oxidized TAGs. Trilinolein (LLL), 1-oleoyl-2,3-dilinolinoyl glycerol (OLL), triolein (OOO) and 1,2-distearoyl-3-palmitoyl glycerol (SSP) were present in higher amounts and decreased with thermal treatment. Animal's studies showed that oxidized oils decreased the whole body weight, which was ameliorated by the co-administration of un-oxidized oils. The levels of serum biochemical parameters were improved by co-administration of pumpkin seed oils. There were no significant effects of both oxidized and un-oxidized PSO on the hematological and histological parameters of rabbits. In conclusion, nutritionally important triacylglycerols were present in PSO with protective role against the toxicity of its corresponding oxidized oils.

Skin auto fluorescence (SAF) is used as a proxy for the accumulation of advanced glycation end products (AGEs) and has been proposed to stratify patients into cardiovascular disease (CVD) and diabetes mellitus (DM) risk groups. This study evaluates the effects of seven different ethnicities (Arab, Central-East African, Eastern Mediterranean, European, North African, South Asian and Southeast Asian) and gender on SAF as well as validating SAF assessment as a risk estimation tool for CVD and DM in an Arabian cohort. SAF data from self-reported healthy 2,780 individuals, collated from three independent studies, has been linear modelled using age and gender as a covariate. A cross-study harmonized effect size (Cohens'd) is provided for each ethnicity. Furthermore, new data has been collected from a clinically well-defined patient group of 235 individuals, to evaluate SAF as a clinical tool for DM and CVD-risk estimation in an Arab cohort. In an Arab population, SAF-based CVD and/or DM risk-estimation can be improved by referencing to ethnicity and gender-specific SAF values. Highest SAF values were observed for the North African population, followed by East Mediterranean, Arab, South Asian and European populations. The South Asian population had a slightly steeper slope in SAF values with age compared to other ethnic groups. All ethnic groups except Europeans showed a significant gender effect. When compared with a European group, effect size was highest for Eastern Mediterranean group and lowest for South Asian group. The Central-East African and Southeast Asian ethnicity matched closest to the Arab and Eastern Mediterranean ethnicities, respectively. Ethnic and gender-specific data improves performance in SAF-based CVD and DM risk estimation. The provided harmonized effect size allows a direct comparison of SAF in different ethnicities. For the first time, gender differences in SAF are described for North African and East Mediterranean populations.