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Abstract Background A thorough psychosocial assessment is time-consuming, often requiring multiple sessions to uncover the psychological factors contributing to mental illness, such as depression. The duration varies depending on the severity of the patient’s condition and how effectively the psychotherapist can establish rapport. However, prolonged assessment periods pose a significant risk of patient deterioration. Methods The comprehensive psychosocial intervention, led by the Multi-Criteria Decision-Making (MCDM) approach utilizing the Multi-Objective Optimization by Ratio Analysis (MOORA) method, played a pivotal role in identifying the key psychological factors contributing to the depression of the client among the 21 factors specified by BDI-II analysis. Results The integration of the MOORA strategy compared to traditional psychotherapy on 254 samples demonstrates a Jaccard similarity coefficient of 0.8, with a minimum error margin of 7% (vulnerability index = 0.57), indicating a significant agreement between the two approaches, both converging towards a similar solution. For patients with extreme depression, the number of sessions reduced from 18 ± 2 to 11 ± 2, showing a 33–35% reduction (χ 2 = 6.94, p = 0.008). Severe depression patients experienced a reduction from 14 ± 2 to 8 ± 1 sessions i.e., 34–39% reduction (χ 2 = 8.32, p = 0.004). Moderate depression patients saw sessions drop from 9 ± 1 to 5 ± 1, i.e., 37–43% reduction (χ 2 = 0.29, p = 0.001). The accuracy for detecting dominant psychological factors improved to 82.88% for extreme, 86.74% for severe, and 90.34% for moderate depression, respectively. Conclusion The implementation of MOORA facilitated the identification and prioritization of key psychosocial intervention strategies, making the process significantly faster compared to traditional methods. This acceleration greatly enhanced the precision and efficacy of the work. Additionally, critical vulnerable factors were identified through ordered statistics and correlation analysis [Pearson (r) = 0.8929 and Spearman’s rank (ρ) = 0.7551] on the Beck Depression Inventory-II model. These findings were supported by other MCDM schemes such as EDAS and TOPSIS, demonstrating high stability and robustness in dynamic decision-making environments, maintaining consistency across scenarios adapted by different psychotherapists. Overall, the combined application of MCDM (MOORA) and targeted psychological interventions yielded substantial positive outcomes in enhancing the well-being of individuals with psychological illnesses, such as depression, cognitive, affective, and somatic syndromes.

BackgroundThe necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error.ObjectiveThis research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans.MethodsThe dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification.ResultsThe proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications.ConclusionThis study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

The preservation and digitization of historical data are crucial for ensuring the continuity and accessibility of information over successive generations. The present study investigates the utilization of machine learning methodologies in the identification of Assamese words, focusing specifically on their distinctive visual characteristics. The main aim of this project is to improve word recognition technologies in Indic languages, specifically focusing on Assamese, in order to preserve and provide access to Assamese literature for future generations. The classification procedure entails the examination of 19 shape-related attributes through a range of machine learning algorithms, such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM) with various kernels, K Nearest Neighbors, and Gradient Boosting. The assessment of the model involves the utilization of various metrics such as Accuracy, Precision, Kappa, F1-score, Model Build Time, and Model Run Time to evaluate the computational efficiency. Additionally, the metrics of Area under the Curve (AUC) and Receiver Operating Characteristic (ROC) are also considered in the evaluation process. Out of the four datasets analyzed, Dataset 3 exhibits the highest level of performance. It is worth noting that Gradient Boosting demonstrates the highest level of accuracy, reaching 96.03% for conventional machine learning appraoches. Logistic Regression and SVM with RBF kernel closely trail behind, achieving accuracies of 95.64% and 95.60% respectively. Furthermore, the research conducted in this study also employs multiple layers of Convolutional Neural Networks (CNN), resulting in a remarkable recognition accuracy of 97.3%. This finding demonstrates that the CNN model and the proposed feature-set are in close proximity to one another in terms of the evaluation metrics.

Through this investigation, we establish the mechanism and physical characterization of zinc (II) sequestration by Morganella morganii ACZ05 strain, which was isolated and characterized from soil polluted by effluents from electroplating industries. As far as we know, there is very little literature concerning zinc biosorption using an environmental strain of M. morganii. The SEM analysis shows the dark porous gaps in the aggregated cell-matrix of test bacterial biomass which is inferred as water channels usually seen in biofilms, as compared to metal-unexposed control. M. morganii is not known to produce biofilms unless in the rare nosocomial conditions. Here, SEM analysis shows the production of biofilms after exposure to zinc (II) at 500 ppm, which has not been previously reported. EDX analysis of bacterial biomass also specified the sorption of zinc (II) by the bacterial cells and the presence of new peaks for zinc in contrast to control. Both XRD and FTIR analysis observations strongly implicate the potential of physical adsorption as a mechanism for heavy metal resistance. Analysis of the cell surface by Atomic force microscopy and examination of the topography revealed cell aggregation occurs during biofilm production after zinc biosorption. Unlike other reports, regular models such as Langmuir isotherm and Freundlich isotherm were found insufficient to explain the physisorption of zinc (II) metal ions on complex multicomponent adsorbents such as the exopolymeric surface of the bacterial cells. However, adsorption kinetics of zinc (II) to the bacterial biomass was most effectively elucidated by a pseudo-second-order kinetic model, suggesting a certain kind of chemisorption that requires further study. HIGHLIGHTS XRD and FTIR – potential of physical adsorption as a mechanism for heavy metal resistance.; SEM-EDX and AFM – morphological changes were observed after metal addition.; The isotherm studies – fit of pseudo-second order for biosorption experimental data suggests the involvement of cellular mechanisms for the activity of binding sites of metal ions and other chemical sorption processes.; Biosorption, chemisorption,;

One of the most cutting-edge technologies over the years is the Internet of Things (IoT), which is a major force behind the paradigm shift away from conventional medical practises. The goal of IoT-based eHealth is to provide healthcare services that are more effective and individualised through continuous data exchange between linked devices and enhanced data analytics. The IoT and decision-making systems are the main areas of focus of this programme, which seeks to deliver intelligent and proactive healthcare. By considering the huge array of physiological characteristics and applying potent analytical tools like cluster analysis, it is possible to obtain more insight into health-data. In this study, e-health technologies and remote patient monitoring were developed to assist patients in avoiding hospital visits, especially during viral epidemics. This project will use IoT and artificial intelligence (AI) technology to address these problems. The study's objective is to select the most appropriate and effective number of hidden layers and activation function types for the deep net (NN). Describe the patient data sent using IoT protocols next. NN analyses the information from the patient's medical sensors to choose the optimal option. The diagnosis is then communicated to the physician. The proposed technology enables patients to autonomously recognise and forecast the sickness while also supporting clinicians in remote disease discovery and analysis without requiring patients to attend the hospital.

Information Communication and Technology (ICT) integration has influenced various fields and it has brought various changes. This article examines; How ICT has influenced the Teaching-Learning Process (TLP) in engineering education? In the midst of the pandemic situation caused by the Covid–19 virus, the usage of ICT tools helps in continuing the engineering education from home online. However, there exist a few difficulties like teacher’s adaptability, availability of tools, and content delivery. This paper identifies the integration of ICT in delivering the content of programming branches, circuital, and non-circuital engineering subjects. This survey article emphasizes the usage of various ICT tools used in teaching descriptive, analytical, and laboratory-based subjects through online mode in an efficient way. Also, a comprehensive survey is conducted among teachers and students as well; the responses are analyzed in attaining the Program Outcomes (PO). The results of attaining the PO before and after the online mode of education using ICT tools are compared and the significant features are elaborately discussed. This helps the faculty members of engineering education to get an overview of the pros and cons of using ICT tools.

The contemporary concern in worldwide power scenario and consumption rate is frightening significantly at a marvelous amount of population, and it originates a very high augment in the electrical energy area. The all-embracing attraction and manufacture of fossil energy is the chief reason in the ecological issues. Due to these relic fuels, the ecological resources get exhausted, and it results in climatic change. Due to these negative aspects, every country puts effort to bring the energy efficiency high with the help of renewable energy. Presently, the solar energy donation worldwide decreases. Contrarily, solar energy contribution to the global energy is highly contrary to nonconventional energy resources. But there is a progress in power generation, and it plays a vital role in solar photovoltaic generation. Gallium nitride and silicon carbide power semiconductors will emerge to bring the efficiency high in the photovoltaic technology. In this work, we will converse about how to increase the efficiency by using gallium nitride.

Abstract In Wireless Sensor Networks (WSN), as sensor nodes have limited energy, the design of an energy‐efficient clustering technique is a critical challenge to be addressed. We propose an Energy‐Efficient Zone‐based Clustering algorithm for WSN that works efficiently in large area WSN as well. In the proposed method, the sensing field is divided into equal size zones, and one Zone Monitor is selected in each zone. The distance of a Zone Monitor from the Base Station is considered in the calculation of the number of desired Cluster Heads (CHs) in its zone for implementing Unequal clustering. The CHs are selected such that they are uniformly distributed inside each zone to conserve the energy of sensor nodes needed for intra‐cluster transmission. Also, for better load balancing among the selected CHs, we propose that cluster size should be uniform at zone level. Hence, after the CHs are selected, the remaining sensor nodes in a zone decide to become a cluster member of one of the selected CHs using a fuzzy‐based cluster formation technique. We compare the proposed algorithm with other approaches for three different network scenarios using parameters such as the number of alive nodes, the first node dies, half of the nodes die, the last node dies, the amount of data received at Base Station, and total remaining energy. Simulation results show that the proposed clustering algorithm provides better performance than other methods.

Let G=(V,E) be a simple, finite and undirected (p,q)-graph with p vertices and q edges. A graph G is Skolem odd difference mean if there exists an injection f:V(G)→{0,1,2,…,p+3q-3} and an induced bijection f∗:E(G)→{1,3,5,…,2q-1} such that each edge uv (with f(u)>f(v)) is labeled with f∗(uv)=f(u)-f(v)2. We say G is Skolem even difference mean if there exists an injection f:V(G)→{0,1,2,…,p+3q-1} and an induced bijection f∗:E(G)→{2,4,6,…,2q} such that each edge uv (with f(u)>f(v)) is labeled with f∗(uv)=f(u)-f(v)2. A graph that admits a Skolem odd (or even) difference mean labeling is called a Skolem odd (or even) difference mean graph. In this paper, first, we construct some new Skolem odd difference mean graphs and then investigate the Skolem even difference meanness of some standard graphs. Keywords: Mean labeling, Odd mean labeling, Skolem difference mean labeling, Skolem odd difference mean labeling, Skolem even difference mean labeling, AMS Classification (2010): 05C78

Background: The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. Objective: This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. Methods: The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. Results: The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. Conclusion: This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods. [ABSTRACT FROM AUTHOR]