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The importance of image encryption in cryptography has surged due to advancement in image processing technology. Our research presents a novel image encryption technique using advanced image processing and permutation-based S-box generation. Utilizing a sine series expansion, we achieve a pseudorandom distribution of values applicable across various fields. Our systematic method for computing the Laplace transform of symbolic metrices ensures enhanced security. This study integrates core cryptographic principles: permutation, substitution, randomness, symbolic operations, and cyclic encryption. The proposed cryptosystem’s modular nature allows adaptability and customization for specific cryptographic needs, enhancing overall security. Our efforts to improve key generation, introduce randomness, and increase operational complexity drive advancements in secure cryptographic systems. Comprehensive analysis, including key sensitivity, key space, Information Entropy(IE), histogram correlation, Number of Pixel Change Rate (NPCR), Peak Signal Noise Ratio(PSNR), Unified Average Changing Intensity (UACI), and similarity assessments like Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM),Validate the efficiency of our encryption scheme. Our results surpass previous efforts, setting a new standard in cryptographic research excellence.

Alzheimer’s disease is a neurodegenerative disease causing memory loss and brain protein accumulation. Early diagnosis is crucial for clinical trials and patient care. Magnetic resonance imaging (MRI) methods have improved diagnosis and prognosis, but doctors need to interpret images proficiently. Deep learning technology has shown potential in detecting Alzheimer’s disease, but the disease progresses slower in early phases. A new dual-attention convolutional autoencoder model is presented, offering improved detection abilities and potential for real-time use in Alzheimer’s disease diagnosis. The study utilized two datasets: the first ADNI dataset, which includes three classes (MCI, CN, and AD), and the second Alzheimer’s Disease Neuroimaging Dataset, which includes two distinct classes (AD and MCI). We analyze the effectiveness of our proposed model by evaluating key performance metrics such as accuracy, precision, sensitivity, specificity, F1 score, and AUC score. In addition, we utilize cross-validation and mean absolute error to validate our model while also fine-tuning the parameters. Based on experimental data, the proposed model accurately detected Alzheimer’s disease with an accuracy of 0.9902 ± 0.0139. Based on the results, the proposed model demonstrates excellent performance compared to the existing methods described in the literature. The proposed mode achieves precision, sensitivity, and specificity of 0.9882 ± 0.0587, 0.9898 ± 0.0865, 0.9912 ± 0.0872 respectively. The model achieved an AUC score of 0.9992 for MCI and 0.9919 for AD class. Furthermore, the proposed method can enhance the affordability of Alzheimer’s disease diagnostics and increase the rate of early AD detection by facilitating remote healthcare.

Software defect prediction plays a crucial role in enhancing software quality while achieving cost savings in testing. Its primary objective is to identify and send only defective modules to the testing stage. This research introduces an intelligent ensemble-based software defect prediction model that combines diverse classifiers. The proposed model employs a two-stage prediction process to detect defective modules. In the first stage, four supervised machine learning algorithms are employed: Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Network. These algorithms are optimized through iterative parameter optimization to achieve the highest accuracy possible. In the second stage, the predictive accuracy of the individual classifiers is integrated into a voting ensemble to make the final predictions. This ensemble approach further improves the accuracy and reliability of the defect predictions. Seven historical defect datasets from the NASA MDP repository, namely CM1, JM1, MC2, MW1, PC1, PC3, and PC4, were utilized to implement and evaluate the proposed defect prediction system. The results demonstrate that each dataset’s proposed intelligent system achieved remarkable accuracy, outperforming twenty state-of-the-art defect prediction techniques, including base classifiers and ensemble methods.

Abstract Generation of fluid flow due to simultaneous occurrence of heat and mass diffusions caused by buoyancy differences is termed as double diffusion. Pervasive applications of such diffusion arise in numerous natural and scientific systems. This article investigates double diffusion in naturally convective flow of water-based fluid saturated in corrugated enclosure and containing hybrid nano particles composed of Copper (Cu) and Alumina (Al2O3). Impact of uniformly applied magnetic field is also accounted. To produce thermosolutal convective potential circular cylinder of constant radius is also adjusted by providing uniform temperature and concentration distributions. Finite element approach is capitalized to provide solution of utilized governing equations by utilizing Multiphysics COMSOL software. Wide-range of physical parameters are incorporated to depict their influence on associated distributions (velocity, temperature and concentration). Interesting physical quantities like Nusselt number, Sherwood numbers are also calculated against involved sundry parameters. It is note worthily observed that maximum strength of stream lines $${(\psi }_{max})$$ ( ψ max ) is 3.3 at $$\phi =0$$ ϕ = 0 and drops to 1.2 when $$\phi $$ ϕ is increased to 0.04. Furthermore, in the hydrodynamic case (Ha = 0), it is observed that the velocity field exhibits an increasing trend compared to the hydromagnetic case $$\left(Ha\ne 0\right),$$ H a ≠ 0 , which is proved from the attained values of stream-function i.e., $${|\psi |}_{max}=11$$ | ψ | max = 11 (in the absence of a magnetic field) and $${|\psi |}_{max}=3.5$$ | ψ | max = 3.5 (in the presence of a magnetic field). It is revealed from the statistics of Nusselt number that increase in volume fraction of nano particles from 0 to 0.4, heat flux coefficient upsurges up to 7% approximately. Since, present work includes novel physical aspects of thermosolutal diffusion generated due to induction of hybrid nanoparticles in water contained in corrugated enclosure, so this study will provide innovative thought to the researchers to conduct research in this direction.

Brain tumors present a significant medical challenge, demanding accurate and timely diagnosis for effective treatment planning. These tumors disrupt normal brain functions in various ways, giving rise to a broad spectrum of physical, cognitive, and emotional challenges. The daily increase in mortality rates attributed to brain tumors underscores the urgency of this issue. In recent years, advanced medical imaging techniques, particularly magnetic resonance imaging (MRI), have emerged as indispensable tools for diagnosing brain tumors. Brain MRI scans provide high-resolution, non-invasive visualization of brain structures, facilitating the precise detection of abnormalities such as tumors. This study aims to propose an effective neural network approach for the timely diagnosis of brain tumors. Our experiments utilized a multi-class MRI image dataset comprising 21,672 images related to glioma tumors, meningioma tumors, and pituitary tumors. We introduced a novel neural network-based feature engineering approach, combining 2D convolutional neural network (2DCNN) and VGG16. The resulting 2DCNN-VGG16 network (CVG-Net) extracted spatial features from MRI images using 2DCNN and VGG16 without human intervention. The newly created hybrid feature set is then input into machine learning models to diagnose brain tumors. We have balanced the multi-class MRI image features data using the Synthetic Minority Over-sampling Technique (SMOTE) approach. Extensive research experiments demonstrate that utilizing the proposed CVG-Net, the k-neighbors classifier outperformed state-of-the-art studies with a k-fold accuracy performance score of 0.96. We also applied hyperparameter tuning to enhance performance for multi-class brain tumor diagnosis. Our novel proposed approach has the potential to revolutionize early brain tumor diagnosis, providing medical professionals with a cost-effective and timely diagnostic mechanism.

Gastrointestinal diseases cause around two million deaths globally. Wireless capsule endoscopy is a recent advancement in medical imaging, but manual diagnosis is challenging due to the large number of images generated. This has led to research into computer-assisted methodologies for diagnosing these images. Endoscopy produces thousands of frames for each patient, making manual examination difficult, laborious, and error-prone. An automated approach is essential to speed up the diagnosis process, reduce costs, and potentially save lives. This study proposes transfer learning-based efficient deep learning methods for detecting gastrointestinal disorders from multiple modalities, aiming to detect gastrointestinal diseases with superior accuracy and reduce the efforts and costs of medical experts. The Kvasir eight-class dataset was used for the experiment, where endoscopic images were preprocessed and enriched with augmentation techniques. An EfficientNet model was optimized via transfer learning and fine tuning, and the model was compared to the most widely used pre-trained deep learning models. The model’s efficacy was tested on another independent endoscopic dataset to prove its robustness and reliability.

Energy transfer and boundary layer flow on a stretching cylinder are used in extrusion procedures, fiber technology, the manufacture of polymer sheets, and the creation of plastic films. In view of these applications, this article examines a transport model for a boundary layer flow with magnetohydrodynamics derived from a Maxwell fluid flow under the influence of radiation, Brownian motion, thermo phoresies and Stefan blowing on a stretching cylinder. Using MATLAB bvp4c function, the governing equations are numerically solved after being changed into ordinary differential equations via similarity renovations. Calculations and analyses determined how various emergent parameters affected the flow field, heat transfer and mass transfer. Some characteristics were discovered to affect the width of the boundary layer. The influence on MHD (M), radiation (Rd), the Schmidt number (Stc), Brownian motion (Nb), thermo phoresies (Nt) and the Stefan blowing parameter (Sb) on the velocity, energy and concentration profile have been explored. Excellent assessment is recognized over a tabular explanation to validate the adopted numerical technique. Significantly the Stefan blowing parameter rises the profiles on velocity and temperature. For an increasing value of the Maxwell fluid parameter, the skin friction coefficient decreases compared to the Stefan parameter.

In the internet of things (IoT) networks, machine learning (ML) is significantly used for malware and adversary detection. Recently, research has shown that adversarial attacks have put ML-based models at risk. This problem is exacerbated in an IoT environment because of the absence of adequate security measures. Consequently, it is crucial to evaluate the strength of such malware detectors using powerful adversarial samples. The existing adversarial sample generation strategies either rely on high-level image features or an unfiltered feature set, making it challenging to determine which feature modifications are crucial in evading malware detection systems, without compromising the malware functionality. This encourages us to propose an evasion framework named IF-MalEvade, based on Generative Adversarial Network (GAN) and Deep Reinforcement Learning (DRL) that effectively generates fully-working, malware samples with several effective perturbations such as header Section manipulation and benign bytes insertion. The DRL framework selects a few suitable action sequences to change malicious samples, thus allowing our malware samples to bypass various black-box ML based malware detectors and the detection search engines of VirusTotal, while maintaining the executability and malicious behavior of the original malware samples. The neural networks of GAN take in the unfiltered feature set of malware dataset and using minimax objective function yields a set of useful features that are subsequently used by the DRL agent to make effective changes. Experimental results illustrated that by utilizing the influential features in sequence of transformations, the adversarial samples generated by our model outperformed the state-of-the-art evasion models with an impressive evasion rate. Additionally, the detection rate of well-known machine learning models was also brought down to up to 97%. Furthermore, when the machine learning models were retrained using adversarial samples, a 35% increase in detection accuracy was observed.

Identifying opinion leaders in social networks, particularly in social media, is a crucial marketing strategy. These individuals have a considerable influence on the purchasing decisions of their communities. Companies can benefit from collaborating with relevant opinion leaders in their market as this can increase their visibility, establish their credibility, and gain consumer trust, leading to increased sales, improved brand perception, and an expanded market share. Additionally, by gaining a comprehensive understanding of opinion leaders, companies can better comprehend the trends and preferences of their target audience. This allows them to tailor their marketing and product strategies more effectively. Identifying suitable influencers to endorse their products or services is a significant challenge for companies. The identification of opinion leaders is complicated by their informal and unstructured nature, as well as the varying selection criteria depending on the marketing campaign’s goals. While numerous research studies have focused on detecting opinion leaders in social networks based on content, interactions, or a combination of both, few have explored sentiment analysis of post content, received interactions, and user comments in relation to published posts. The purpose of this paper is to present an hybrid approach to detect opinion leaders in Facebook. This approach involves analyzing the trajectory of post content by examining interactions on the post, as well as mining the text content of the post itself and analyzing the users’comments sentiments.

Diabetic Retinopathy (DR) is one of the leading causes of blindness and vision loss worldwide. According to the International Diabetes Federation (IDF), approximately one-third of individuals with diabetes, equivalent to 32.2%, are affected by some form of DR. Due to the uneven data distribution, intra-class variance, and a dearth of ophthalmologists, DR diagnosis is considered challenging. In recent years, Convolutional Neural Networks (CNN) and supervised learning techniques have been potentially useful in computer vision applications. However, unsupervised CNN has received less attention. Moreover, it is more manageable to use synthetic images for model training with the recent advancements in graphics. Therefore, the proposed method combines the actual and augmented views using the Deep Convolutional Generative Adversarial Network (DCGAN) algorithm. The generated views are implemented to balance the minority class in the imbalanced dataset. Furthermore, a novel ensemble convolutional neural network algorithm named Different View Ensemble (DVE) that merges the weighted average prediction of CNN, CNN-i, and CNN+i algorithms has been proposed. The proposed algorithm is evaluated on the DDR and EyePACS datasets, and its performance is compared with K-Means, Fuzzy C-Means (FCM), and Autoencoder-based Deep Embedded Clustering Techniques (DEC). The results demonstrate the superiority of the proposed algorithm, achieving an accuracy rate of 97.4%, specificity of 99.6%, and sensitivity of 92.3%. The promising results underscore the potential impact of this methodology in enhancing the accuracy and reliability of automated diagnostic systems in the field of ophthalmology. Notably, the evaluation considers imbalanced data and a DCGAN-balanced dataset, where the proposed approach exhibits even better performance with balanced classes.

Presents corrections to the paper, (Corrections to “Software Defect Prediction Using an Intelligent Ensemble-Based Model”).

Andrés Coca-Pelaz,1 Justin A Bishop,2 Nina Zidar,3 Abbas Agaimy,4 Eloisa Maria Mello Santiago Gebrim,5,6 Vanni Mondin,7 Oded Cohen,8 Primož Strojan,9 Alessandra Rinaldo,10 Ashok R Shaha,11 Remco de Bree,12 Marc Hamoir,13 Antti A Mäkitie,14 Luiz P Kowalski,15 Nabil F Saba,16 Alfio Ferlito17 1Department of Otolaryngology, Hospital Universitario Central de Asturias-University of Oviedo, Instituto de Investigación Sanitaria del Principado de Asturias, IUOPA, CIBERONC, Oviedo (Asturias), Spain; 2Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA; 3Institute of Pathology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia; 4Institute of Pathology, Friedrich Alexander University Erlangen-Nürnberg, University Hospital, Erlangen, Germany; 5Otorhinolaryngology Department, National Institute of Rehabilitation, Mexico City, Mexico; 6Radiology Department, Hospital das Clínicas, University of São Paulo, São Paulo, Brazil; 7ENT Clinic, Policlinico Città di Udine, Udine, Italy; 8ARM - Center for Otolaryngology - Head and Neck Surgery and Oncology, Assuta Medical Center, Tel Aviv, Affiliated with Ben Gurion University, Beer Sheva, Israel; 9Department of Radiation Oncology, Institute of Oncology, Ljubljana, Slovenia; 10University of Udine School of Medicine, Udine, Italy; 11Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA; 12Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, Utrecht, The NetherlAnds; 13Department of Head and Neck Surgery, UC Louvain, St Luc University Hospital and King Albert II Cancer Institute, Brussels, Belgium; 14Department of Otorhinolaryngology, Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; 15Head and Neck Surgery and Otorhinolaryngology Department, A C Camargo Cancer Center and Department of Head and Neck Surgery, University of São Paulo Medical School, São Paulo, Brazil; 16Department of Hematology and Medical Oncology, The Winship Cancer Institute of Emory University, Atlanta, GA, USA; 17International Head and Neck Scientific Group, Padua, ItalyCorrespondence: Andrés Coca-Pelaz, Department of Otolaryngology, Hospital Universitario Central de Asturias-University of Oviedo, Instituto de Investigación Sanitaria del Principado de Asturias, IUOPA, CIBERONC, Avenida de Roma s/n, Oviedo (Asturias), 33011, Spain, Email acocapelaz@yahoo.esIntroduction: Lymph node metastasis (LNM) from primary tumors of the central nervous system (CNS) is an infrequent condition, and classically it was thought that CNS tumors could not spread via the lymphatic route. Recent discoveries about this route of dissemination make its knowledge necessary for surgeons and pathologists to avoid delays in diagnosis and unnecessary treatments. The aim of this paper is to review the literature and to discuss the relevant pathogenetic mechanism and the cytologic features along with recommendations for surgical treatment of these cervical LNM.Materials and Methods: Using PRISMA guidelines, we conducted a systematic review of the literature published from 1944 to 2021, updating the comprehensive review published in 2010 by our group.Results: Our review includes data of 143 articles obtaining 174 patients with LNM from a primary CNS tumor. The mean age of the patients was 31.9 years (range, 0.1– 87) and there were 61 females (35.1%) and 103 males (59.2%), and in 10 cases (5.7%) the gender was not specified. The more frequent sites of distant metastasis were bones (23%), lungs (11.5%) and non-cervical lymph nodes (11%).Conclusion: Cervical LNM from CNS tumors is infrequent. Pathologic diagnosis can be obtained by fine-needle aspiration cytology in most cases, giving surgeons the option to plan the appropriate surgical treatment. Given the poor prognosis of these cases, the most conservative possible cervical dissection is usually the treatment of choice.Keywords: brain tumors, central nervous system, extraneural metastasis, extracranial metastasis, cervical lymph node metastasis

Mobile learning has become increasingly important for higher education due to its numerous advantages and transformative potential. The aim of this study is to investigate how students perceive and utilize mobile learning (m-learning) services in universities. To achieve this objective, a conceptual model was developed, combining the TAM with additional new determinants, including perceived security, perceived trust, perceived risk, and service quality. The primary goal of this model is to assess the adoption of m-learning apps among users in university settings. To evaluate the proposed model, SEM was utilized to test the research model. The findings of the study highlight the critical roles of perceived security, perceived trust, and service quality in promoting the adoption of m-learning apps. Moreover, the results indicate that perceived risk negatively impacts both students’ trust and their attitudes towards using mobile learning services. The study reveals that the perceived trust, and service quality factors positively influence students’ attitudes towards adopting m-learning apps. These research findings hold significant implications for universities and academia, offering valuable insights to devise effective strategies for increasing the utilization of m- learning services among students. By gaining a deeper understanding of students’ perceptions and acceptance, universities can optimize their m-learning offerings to cater to students’ needs and preferences more effectively.

Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods.

Innovative technological solutions are required to improve patients’ quality of life and deliver suitable treatment. Healthcare workers may be able to watch patients from a distance using the Internet of Things (IoT) by using big data algorithms to analyze instrument outputs. Therefore, it is essential to gather information on use and health problems in order to improve the remedies. To ensure seamless incorporation for use in healthcare institutions, senior communities, or private homes, these technological tools must first and foremost be easy to use and implement. We provide a network cluster-based system known as smart patient room usage in order to achieve this. As a result, nursing staff or caretakers can use it efficiently and swiftly. This work focuses on the exterior unit that makes up a network cluster, a cloud storage mechanism for data processing and storage, as well as a wireless or unique radio frequency send module for data transfer. In this article, a spatio-temporal cluster mapping system is presented and described. This system creates time series data using sense data collected from various clusters. The suggested method is the ideal tool to use in a variety of circumstances to improve medical and healthcare services. The suggested model’s ability to anticipate moving behavior with high precision is its most important feature. The time series graphic displays a regular light movement that continued almost the entire night. The last 12 h’ lowest and highest moving duration numbers were roughly 40% and 50%, respectively. When there is little movement, the model assumes a normal posture. Particularly, the moving duration ranges from 7% to 14%, with an average of 7.0%.

Energy efficiency is considered the major design issue in wireless sensor networks (WSN), which can be addressed using clustering and routing techniques. They are treated as Non-deterministic Polynomial (NP)-hard optimization problems and are solved using metaheuristic algorithms to identify the optimal or near-optimal solutions. With this motivation, this paper develops a hybridization of the metaheuristic cluster-based routing (HMBCR) technique for WSN. The HMBCR technique initially involves a brainstorm optimization with levy distribution (BSO-LD) based clustering process using a fitness function incorporating four parameters such as energy, distance to neighbors, distance to the base station, and network load. Besides, a water wave optimization with a hill-climbing (WWO-HC) based routing process is carried out for optimal route selection. Extensive experimentation analysis is performed to ensure the energy efficiency and network lifetime performance of the HMBCR technique. The experimental outcome ensured the superior results of the HMBCR technique over the compared methods under different aspects.

Antibiotics become an emerging contaminant and receive more interests due to its ecotoxicological and strong stability in water ecosystems. Antibiotic adsorption onto carbon materials are biochars among the wastewater mechanisms. This research used machine learning (ML) techniques to generate general adsorption forecasting model for sulfamethoxazole (SMX) and tetracycline (TC) on CBM. Dirichlet design parameters and a combined combination of Neumann and Dirichlet boundary situation are applied to the system of differential equations. In addition, the proposed method use the learning under supervision technique of a nonlinear autoregressive for estimating the CO2 concentration and flows in units of rate of a reaction characteristics, an exogenous (NARX) neural network model with two activation functions was used (Log-sigmoid and hyperbolic tangent) and for both the findings of a TC and SMX absorption simulations showed the random forest performed support vector tree and nonlinear autoregressive exogenous neural networks and machine learning methods. Their relevance and complete dependency graph evaluation lead reasonable CBM uses for antimicrobial wastewater treatment. Also, machine learning forecasting model with good generalization capability is useful for building effective CBMs with few empirical screens. It evaluates the accuracy, precision, recall, false positive rate (FPR), and false negative rate (FNR) and also reduces the experimental screening.

Object-oriented programming (OOP) is prone to defects that negatively impact software quality. Detecting defects early in the development process is crucial for ensuring high-quality software, reducing maintenance costs, and increasing customer satisfaction. Several studies use the object-oriented metrics to identify design flaws both at the model level and at the code level. Metrics provide a quantitative measure of code quality by analyzing specific aspects of the software, such as complexity, cohesion, coupling, and inheritance. By examining these metrics, developers can identify potential defects in OOP, such as design defects and code smells. Unfortunately, we cannot assess the quality of an object-oriented program by using a single metric. Identifying design-defect-metric-based rules in an object-oriented program can be challenging due to the number of metrics. In fact, it is difficult to determine which metrics are the most relevant for identifying design defects. Additionally, multiple thresholds for each metric indicates different levels of quality and increases the difficulty to set clear and consistent rules. Hence, the problem of object-oriented metrics selection can be ascribed to a multi-criteria decision-making (MCDM) problem. Based on the experts’ judgement, we can identify the most appropriate metric for the detection of a specific defect. This paper presents our approach to reduce the number of metrics using one of the MCDM methods. Therefore, to identify the most important detection rules, we apply the fuzzy decision-making trial and evaluation laboratory (Fuzzy DEMATEL) method. We also classify the metrics into cause-and-effect groups. The results of our proposed approach, applied on four open-source projects, compared to our previous published results, confirm the efficiency of the MCDM and especially the Fuzzy DEMATEL method in selecting the best rules to identify design flaws. We increased the defect detection accuracy by the selection of rules containing important and interrelated metrics.

From the past half of a century, identification of the relevant documents is deemed an active area of research due to the rapid increase of data on the web. The traditional models to retrieve relevant documents are based on bibliographic information such as Bibliographic coupling, Co-citations, and Direct citations. However, in the recent past, the scientific community has started to employ textual features to improve existing models’ accuracy. In our previous study, we found that analysis of citations at a deep level (i.e., content level) can play a paramount role in finding more relevant documents than surface level (i.e., just bibliography details). We found that cited and citing papers have a high degree of relevancy when in-text citations frequency of the cited paper is more than five times in the citing paper’s text. This paper is an extension of our previous study in terms of its evaluation of a comprehensive dataset. Moreover, the study results are also compared with other state-of-the-art approaches i.e., content, metadata, and bibliography. For evaluation, a user study is conducted on selected papers from 1,200 documents (comprise about 16,000 references) of an online journal, Journal of Computer Science (J.UCS). The evaluation results indicate that in-text citation frequency has attained higher precision in finding relevant papers than other state-of-the-art techniques such as content, bibliographic coupling, and metadata-based techniques. The use of in-text citation may help in enhancing the quality of existing information systems and digital libraries. Further, more sophisticated measure may be redefined be considering the use of in-text citations.

Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset’s characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.

The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting.

This study examines the influence of the 2008 global financial crisis on trade flows between Kuwait and its main trading partners. Data sample covers the period from 1990 to 2012. In this study, estimates are tested using, fixed and random effects specifications, as well as Hausman test. Findings reveal that not all trading flows with Kuwait’s trading partners got affected by the financial crises. Findings show that is the exports from Kuwait to China are affected Due to the financial crises. The expected negative influence of the global financial crisis is statistically significant in the case of Kuwait trade flows with China.Keywords: Financial Crisis, Gravity Model, Panel Approach, Kuwait.JEL Classifications: F21, O53, C1

Water pollution by antibiotic residues poses a potential threat to environmental and human health. Graphene-based materials are highly stable, recyclable and effective adsorbents for efficiently removing antibiotics from polluted water. In this study, the adsorption behavior of levofloxacin onto sulfonated graphene oxide (SGO) was investigated by varying the contact period, solution pH, adsorbent quantity, levofloxacin concentration, inorganic ions, and solution temperature. Spectroscopic and microscopic techniques were employed to confirm the adsorptive interaction between levofloxacin and SGO. The adsorption process was most accurately characterized by the pseudo-second-order kinetic model and the Langmuir isotherm model, as indicated by their high correlation coefficients (R2) and low root-mean-square error (RMSE) values. The maximal quantity of levofloxacin that can be adsorbed onto SGO was determined to be 1250 μmol/g at pH 4 and 25 °C using the Langmuir model. Thermodynamic studies reveal that the process of levofloxacin adsorption onto SGO is endothermic and spontaneous in nature. Taking into consideration the results of adsorption, desorption and regeneration studies, it is proposed that SGO can be applied as an economic viable agent for the adsorptive removal of levofloxacin from the aqueous environment.