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Synthesis and characterization of biowaste-derived porous carbon supported palladium: a systematic study as a heterogeneous catalyst for the reduction of nitroarenes
In this study, we present a green synthesis approach for the fabrication of porous carbon supported palladium catalysts derived from Caesalpinia pods. The synthesis involves self-activation of Caesalpinia pods in a nitrogen atmosphere at various temperatures (600C, 800C, and 1000C) to produce porous carbon nanoparticles. Among the synthesized carbon materials, the sample CP-CNS/10 synthesized at 1000C exhibited the highest surface area of 793 m2/g with an average pore size diameter of 1.8nm. The resulting porous carbon material served as an efficient support for palladium nanoparticles, with a low metal loading of about 0.2mol% Pd for the reaction. This catalyst demonstrated excellent performance in the reduction of nitroarenes to their corresponding aromatic amines. The successful incorporation of approximately 4.5% Pd during the deposition process highlights the potential of the porous carbon supported palladium catalyst synthesized at 1000C for a sustainable and efficient heterogeneous catalyst for the reduction of nitroarenes. Graphical Abstract: (Figure presented.) Akadiai Kiad Budapest, Hungary 2024. -
Development of personalized diet and exercise recommender system based on clinical data
The present health scenario indicates that thyroid diseases are a common challenge experienced by most individuals. According to the statistics in India, one out of eight women suffer from thyroid-related conditions. Hyperthyroid, hypothyroid, or thyroid cancer are categories of thyroid disorder. It is imperative to maintain optimum levels of secretion of the thyroid hormones as the imbalance could lead to thyroid diseases. Therefore, thyroid patients must be vigilant regarding their iodine intake and follow a customized daily diet and exercise plan. The diet plan, along with balanced iodine levels, must also be able to meet the patient's nutritional needs. A personalized diet plan could help thyroid patients to be more aware and focused on their body metabolism. Existing recommender systems usually provide generic diet recommendations, and unfortunately, it may not be beneficial to patients suffering from a specific disease. Content-based Neighborhood-Conditional RBM (CB-NCRBM) model has posited to recommend Top-3 diet and exercise plans for thyroid patients. The proposed model considers the joint probability distribution of different scores using the user profile. Similarly, preference and health scores are estimated based on content features. The model feeds these scores as visible units to conditional RBM. The proposed model also integrates several content-based features such as users' physiological profiles, thyroid disease information, food, and exercise preferences. The proposed recommender model validates the experimental results using recommendation error and classification accuracy metrics. The proposed hybrid model outperforms several popularly used recommendation models, such as collaborative filtering, content-based, and pure RBM models. The system also provides a feedback loop to enhance the quality of the recommended diet and exercise plans based on user experience. -
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A study on strategic use of language in newspapers headlines /
The language we use to communicate with one another is like a knife. In the hands of a careful and skilled surgeon, a knife can work to do great good. But in the hands of a careless or ignorant person, a knife can cause great harm. Exactly as it is with our words- Anonymous. For the news media, particularly the newspaper, the greatest weapon is the power of words. The success or failure of a newspaper depends largely on the way its headlines appear to the readers. A well thought out headline does half the job of conveying the message. And consequently, a poorly written one can lead to misinterpretation. This research is an effort to delve into a complete understanding of the structuring of headlines in English newspapers and analyzing it against the use of phrases and words that are misleading and ambiguous. -
Mathematical model for effective CO2 emission control with forest biomass using fractional operator
The emission of CO2 is the foremost culprit for global warming and is also considered a significant greenhouse gas. Due to the human populations tremendous growth and activities, the rate of CO2 in the atmosphere has increased. To mitigate the emission of CO2 there are artificial ways. But, naturally have a natural resource called "Forest Biomass," one of the significant sinks to absorb CO2 during photosynthesis. Considering all these factors, the main objective of the current investigation is to understand and illustrate the importance of forest biomass in the emission of CO2. The proposed nonlinear model consists of four variables: atmospheric CO2, human population, energy sectors, and forest biomass. We have studied the model both qualitatively and quantitatively, which will help us make future predictions. To study the model in depth, we have formed a fractional-order model to study the systems behavior at different ranges of fractional orders. The model is termed with the Caputo fractional operator. Boundness and Lyapunov stability for non-linear and fractional order models are studied, and equilibrium points, existence and uniqueness, and numerical simulation are examined. The Adams-Bashforth-Moulton method illustrates the essence of the systems numerical method. The numerical approach reveals that the altered models stability is unchanged. Also, we have examined the model by changing the parameter values to different fractional orders to understand the systems behavior, and the changes are captured as figures. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Development of an Efficient and Secured E-Voting Mobile Application Using Android
Smart technologies, particularly the development of the Internet, are employed to enhance the quality of human existence. Thanks to the Internet's explosive expansion, more and more tasks can now be completed quickly and easily compared to the earlier times. E-voting is a relatively recent field that has been identified. Voting can be conducted in a variety of methods, including in person at a polling place, online, and via a mobile application. The security of applications cannot be disregarded given the internet's explosive growth. In order to prevent phishing attacks, we created an Android application and included a 3-step security process before voting. Students can now vote online from any location at any time using a mobile device. Android Studio is used to create and deploy the application. While creating the voting application, this research adheres to the software development life cycle. The result of this research is the creation of a mobile application that is user-friendly for students and serves as a practical tool for letting them vote with three levels of security. 2022 Anli Sherine et al. -
Role of Filters in Speckle Reduction in Medical Ultrasound Images- A Comparative Study
To diagnose and predict complex disorders in human body, various Medical Imaging Techniques are used. Widely accepted technique among them is the Ultrasound imaging modality, because of its low cost and noninvasive nature. But the images produced by ultrasound scanning are of low quality and amenable to faster degradation due to the presence of speckle noise. This led to various studies for effectively removing speckle noise from ultrasound images. In this paper, an endeavor is made for a comparative analysis of chosen set of post filtering methods for Speckle reduction, VIZ Anisotropic Diffusion, Wavelet, Adaptive Median Filter, Hybrid Algorithm, Modified Fourier Transform and Sparse Code Shrinkage using ICA. The different methods are tested on a collection of ultrasound images and their performance evaluated with the Normalized Cross Correlation metric (NCC), Peak Signal to Noise Ratio (PSNR), Structural Content (SC), Universal Quality Index (UQI), Edge Preservation Index (EPI) and Structural Similarity Index (SSI). Further relative execution time of different approaches are also analyzed. On analysis of the values of different metrics and execution time, Wavelet Based Hybrid Thresholding is found to outperform the other filters considered. 2019 IEEE. -
Medical Ultrasound Image Segmentation Using U-Net Architecture
This research article discusses the implementation aspects of a Deep Learning architecture based on U-Net for medical image segmentation. A base model of the U-Net architecture is extended and experimented. Unlike the existing model, the input images are enhanced by applying a Non-Local Means filter optimized using a metaheuristic Grey wolf optimization method. Further, the model parameters are modified to achieve better performance. Tests were performed using two benchmark B-mode Ultrasound image datasets of 200 Breast lesion images and 504 Skeletal images. Experimental results demonstrate that the modifications resulted in more accurate segmentation. The performance of the modified implementation is compared with the base model and a Bidirectional Convolutional LSTM architecture. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Modified Non-local Means Model for Speckle Noise Reduction in Ultrasound Images
In the modern health care field, various medical imaging modalities play a vital role in diagnosis. Among the modalities, Medical Ultrasound Imaging is the most popular and economic modality. But its vulnerability to multiplicative speckle noise is challenging, which obscure accurate diagnosis. To reduce the influence of the speckle noise, various noise filtering models have been proposed. But while filtering the noise, these filters exhibit limitations like high computational complexity and loss of detailed structures and edges of organs. In this article, a novel Non-local means (NLM)-based model is proposed for the speckle reduction of Ultrasound images. The design parameters of the NLM filter are obtained by applying the Grey Wolf Optimization (GWO) to the input image. The optimized parameters and the noisy image are passed to the NLM filter to get the denoised image. The efficiency of this proposed method is evaluated with standard performance metrics. A comparative analysis with existing methods highlights the merit of the proposal. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A novel optimised method for speckle reduction in medical ultrasound images
The advancement of medical imaging techniques evolving from X-ray to PET images and the medical image analysis helped medical experts to detect, diagnose and offer treatments for complex disorders and deadly diseases in the human body. Among the various modalities used, Ultrasound imaging is the most widely accepted modality because of its affordability, non-invasive nature and various other features. But the presence of speckle noise in ultrasound image lowers the image quality and reduces diagnostic value. This article states an improved hybrid speckle noise reduction method, a combined application of Kuan and non-local means filters. In this method, Kuan filter is used to sharpen the edges and thereafter the speckle noise elimination is done by using the non-local means. In addition, the performance of the proposed hybrid filter and its design parameters are optimised by using a meta-heuristic called grey wolf optimiser. The performance of hybrid method is evaluated by analysing a chosen set of well-known post filtering methods used for speckle reduction with given ultrasound B-mode images. The comparison of test results using remarkable performance metrics and computation time demonstrate that the hybrid method can be used as the efficient speckle reduction method for image analysis. Copyright 2022 Inderscience Enterprises Ltd. -
EEG Neurofeedback Training in Children With Attention Deficit/Hyperactivity Disorder: A Cognitive and Behavioral Outcome Study
Background. Attention deficit/hyperactivity disorder (ADHD) is a highly prevalent childhood disorder with symptoms of inattention, impulsivity, and hyperactivity. EEG neurofeedback training (NFT) is a new intervention modality based on operant conditioning of brain activity, which helps reduce symptoms of ADHD in children. Methods and Procedures. To examine the efficacy of NFT in children with ADHD, an experimental longitudinal design with pre-post comparison was adopted. A total of 30 children in the age range of 6 to 12 years diagnosed as ADHD with or without comorbid conditions were assigned to treatment group (TG; n = 15) and treatment as usual group (TAU; n = 15). TG received EEG-NFT along with routine clinical management and TAU received routine clinical management alone. Forty sessions of theta/beta NFT at the C3 scalp location, 3 to 4 sessions in a week for a period of 3.5 to 5 months were given to children in TG. Children were screened using sociodemographic data and Binet-Kamat test of intelligence. Pre-and postassessment tools were neuropsychological tests and behavioral scales. Follow-up was carried out on 8 children in TG using parent-rated behavioral measures. Results. Improvement was reported in TG on cognitive functions (sustained attention, verbal working memory, and response inhibition), parent- and teacher-rated behavior problems and on academic performance rated by teachers. Follow-up of children who received NFT showed sustained improvement in ADHD symptoms when assessed 6 months after receiving NFT. Conclusion. The present study suggests that NFT is an effective method to enhance cognitive deficits and helps reduce ADHD symptoms and behavior problems. Consequently, academic performance was found to be improved in children with ADHD. Improvement in ADHD symptoms induced by NFT were maintained at 6-month follow-up in children with ADHD. EEG and Clinical Neuroscience Society (ECNS) 2018. -
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Effectiveness of Systematic Group Counselling in Enhancing Academic Performance, Emotional Intelligence and Moral Values of College Students with Unsatisfactory Academic Performance
International Journal of Multidisciplinary Sciences and Research, Vol-1 (1), pp. 86-91. ISSN-2321-4872 -
BSSA: Binary Salp Swarm Algorithm with Hybrid Data Transformation for Feature Selection
Feature selection is a technique commonly used in Data Mining and Machine Learning. Traditional feature selection methods, when applied to large datasets, generate a large number of feature subsets. Selecting optimal features within this high dimensional data space is time-consuming and negatively affects the system's performance. This paper proposes a new binary Salp Swarm Algorithm (bSSA) for selecting the best feature set from transformed datasets. The proposed feature selection method first transforms the original data-set using Principal Component Analysis (PCA) and fast Independent Component Analysis (fastICA) based hybrid data transformation methods; next, a binary Salp Swarm optimizer is used for finding the best features. The proposed feature selection approach improves accuracy and eliminates the selection of irrelevant features. We validate our technique on fifteen different benchmark data sets. We conduct an extensive study to measure the performance and feature selection accuracy of the proposed technique. The proposed bSSA is compared to Binary Genetic Algorithm (bGA), Binary Binomial Cuckoo Search (bBCS), Binary Grey Wolf Optimizer (bGWO), Binary Competitive Swarm Optimizer (bCSO), and Binary Crow Search Algorithm (bCSA). The proposed method attains a mean accuracy of 95.26% with 7.78% features on PCA-fastICA transformed datasets. The results show that bSSA outperforms the existing methods for the majority of the performance measures. 2013 IEEE. -
Memetic Spider Monkey Optimization for Spam Review Detection Problem
Spider monkey optimization (SMO) algorithm imitates the spider monkey's fission-fusion social behavior. It is evident through literature that the SMO is a competitive swarm-based algorithm that is used to solve difficult real-life problems. The SMO's search process is a little bit biased by the random component that drives it with high explorative searching steps. A hybridized SMO with a memetic search to improve the local search ability of SMO is proposed here. The newly developed strategy is titled Memetic SMO (MeSMO). Further, the proposed MeSMO-based clustering approach is applied to solve a big data problem, namely, the spam review detection problem. A customer usually makes decisions to purchase something or make an image of someone based on online reviews. Therefore, there is a good chance that the individuals or companies may write spam reviews to upgrade or degrade the stature or value of a trader/product/company. Therefore, an efficient spam detection algorithm, MeSMO, is proposed and tested over four complex spam datasets. The reported results of MeSMO are compared with the outcomes obtained from the six state-of-art strategies. A comparative analysis of the results proved that MeSMO is a good technique to solve the spam review detection problem and improved precision by 3.68%. 2023 Mary Ann Liebert, Inc., publishers. -
Synthesis, Computational, and Photophysical Probing Studies on Mono-Azo Sulfonamides, and Their Antibacterial Activity
Abstract: Objective: Novel azo-linked substituted sulfonamides were synthesized via diazo coupling with the molecular formula (C9H10N4O2S2, C11H11N3O2S) and characterized by FT-IR, UV-vis, HR-MS, and 1H NMR spectroscopy techniques. The photophysical studies were carried out using experimental techniques. Absorption and fluorescence maxima of all the synthesized molecules were determined by using different solvents. Our synthesized mono-azo derivatives are interested in identifying the cellular target site for sulfonamides (F1-F2) and (P1-P2). The newly synthesized compounds were examined for their in vitro antibacterial activity against Staphylococcus aureus and Escherichia coli strains. Methods: In this study, we focused on the sulfonamide architecture. Antibacterial activity of compound (F1), (F2), (P1), and (P2) derivatives was studied by measuring the diameter of the inhibition zone, using the Disc-agar diffusion method. Results and Discussion: Density functional theory was used to demonstrate the electronic and optical properties of the synthesized molecules. In the correlation between the HOMOLUMO energy gap, the derivative (F1) shows a higher (3.9866 eV) and (F2) shows a lower (3.2063 eV) excitation energy. The synthesized compound (F1) looks into antibacterial activity, exhibited more zone inhibition 25 mm in the concentration 75 L/mL in gram-negative bacteria when compared with the common antibiotic Ciprofloxacin. Additionally, the results emerged from the in silico molecular docking studies the compound (F2) showed highest binding energy against cyclin-dependent kinase (?Gb = 9.8 kcal/mol). Conclusions: The synthesized four mono-azo sulfonamide derivatives (F1), (F2), (P1), and (P2) are reported in photophysical, CDFT, antibacterial, and molecular docking studies with relevant results. Pleiades Publishing, Ltd. 2024. -
Photophysical and antitubercular studies on newly synthesised structurally architectured sulphonamide
This study presents the synthesis and characterisation of four mono-azo sulphonamide derivatives through diazo-coupling electrophilic substitution reactions. The structural analysis of the synthesised molecules was conducted utilising FT-IR, 1H-NMR and HR-MS techniques. Absorption and fluorescence maxima of the synthesised molecules were determined across solvents of varying polarity to explore Solvatochromic behaviour. Density functional theory was employed to elucidate electronic and optical properties, including the computation of HOMOLUMO energies using Gaussian 09W software, with comparisons to experimental data. Molecular electrostatic potential 3D plots identified electrophilic and nucleophilic sites. Solvent interactions were evaluated using KamletAbboud Taft and Catalan parameters. Further, global chemical reactivity descriptors were estimated to ascertain chemical reactivity of the molecules. Additionally, the effectiveness of the colourant anti-tubercular activity was evaluated using in vitro and molecular docking techniques. The biological activity results reveal that methyl-pyridone and barbituric acid coupled with sulphamethizole (SMP and SMB) displayed excellent anti-tubercular activity compared with the standard Gentamycin. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Spectroscopic Studies on Structurally Modified Anthraquinone Azo Hydrazone Tautomer: Theoretical and Experimental Approach
A series of unique four mono-azo substituted anthraquinone analogue were synthesized by using the anthraquinone components in the diazo-coupling technique. The FT-IR, 1H NMR, and HRMS, data were used to confirm the structure of the molecules, and spectroscopic techniques like UV-Vis, and photoluminescence spectroscopy were employed to estimate the photophysical properties of the molecules. The molecular optimized geometry and frontier molecular orbitals were estimated using density functional theory. Further, global chemical reactivity descriptors parameter was theoretically estimated using the value of the highest occupied molecular orbit and lowest unoccupied molecular orbits. The anti-tubercular action of the synthesised dyes were also examined. The results of this biological activity showed that N-isopropyl aniline combined with anthraquinone N-isopropyl aniline had superior anti-tubercular activity when compared to Rifampicin as the standard. As per molecular docking studies, the synthesized compound Q1 showed excellent binding energy (-10.0kcal/mol) among all compounds against the 3ZXR Protein. These results agreed with our in-vitro anti-TB activity results. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Empirical Assessment of Artificial Intelligence Enablers Strengthening Business Intelligence in the Indian Banking Industry: ISM and MICMAC Modelling Approach
Considering the context of the issue based on literature survey and expert opinion, this study investigates the drivers of Artificial Intelligence (AI) implementation, which further strengthens the Business Intelligence (BI) in taking better decision-making industries in India. For the purpose of serving the objective of examining the enablers towards having a smarter AI ecosystem in banking, the relevance of identified enablers from exhaustive literature survey were discussed with the experts from banking sector and AI professionals. Based on their opinion, 15 final enablers were defined based on the data collected have been put through Interpretive Structural Modelling (ISM) that reveals the binary relationship between the enablers to draw a hierarchical conclusion, and then assess the enablers about their independence, linkage, autonomous character, and dependence based on their calculated driving and dependence power through MICMAC analysis. The ISM and MICMAC integrated approaches have been used to establish interdependence among the enablers of AI in banking in India context. The study reveals that strong algorithms result in building quality AI information, and also the efforts from management related to commitment, financial readiness towards technological advancement, training, and skill development are quite essential in making the baking system smarter and would enable the industry to take better management decision. 2023 selection and editorial matter, Deepmala Singh, Anurag Singh, Amizan Omar & S.B Goyal. -
TumorInsight: GAN-Augmented Deep Learning for Precise Brain Tumor Detection
In addition to the shortage in data as well as the low quality of MRI images, one of the most difficult tasks in contemporary medical imaging is the diagnosis of tumors in brain. This work presents a new approach to enhance diagnostic accuracy using sophisticated preprocessing techniques. Combining BRATS 2023 and Cheng et al. datasets to apply cutting-edge deep learning preprocessing methods with Generative Adversarial Networks (GANs), specifically DCGAN, Contrast Limited Adaptive Histogram Equalization (CLAHE), and gamma correction, it aims to significantly improve the quality of MRI images. As a result, updated data should be generated with greater precision and detail, making it possible to identify tumor-affected areas with greater accuracy. Thorough assessment, demonstrated by metrics such as Accuracy (0.98), Specificity (0.99), Sensitivity (0.99), AUC (0.65), Dice Coefficient (0.67), and Precision (0.71), highlights possible advancements in brain tumor identification and treatment, thereby highlighting the effectiveness of the suggested approach. 2024 IEEE.