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Deep Learning in Waste Management and Recycling in Digital Smart City
For waste management and recycling in smart cities, the fast growth of urban populations and the subsequent rise in garbage creation have posed considerable issues. For cities to be sustainable and ecologically friendly, good waste management and the promotion of recycling practises are crucial. Deep learning techniques have become a potent tool for solving complicated issues and streamlining numerous procedures in a variety of fields in recent years. In the framework of smart cities, this chapter proposes improved Deep learning model with IOT Architecture for recycling and garbage management. 2025 Scrivener Publishing LLC. -
Ensemble Deep Learning for COVID-19 Detection Using Multi-Modal Medical Imaging
The COVID-19 pandemic has had a profound impact worldwide This work proposes a deep ensemble learning model incorporating multi-modal inputs, i.e., CT scans and Xrays, to classify the cases into COVID-19, Viral Pneumonia, or Normal. Employing an ensemble average voting approach from three different CNN models InceptionV3, DenseNet-169, and Xception the suggested methodology is highly accurate and reliable. Preprocessing methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE) improve data quality, and Local Interpretable Model-Agnostic Explanations (LIME) allow interpretable prediction through identification of major image features driving classifications. The ensemble model suggested attains an accuracy of 99.64%, outperforming single models, with precision at 99.50%, recall at 99.73%, and an F1-score of 99.61%, which makes it very reliable for detecting COVID-19. Comparative analysis shows that our ensemble method performs better than individual CNN architectures, such as Xception (99.18%), ResNet101 (98.95%), and DenseNet201 (98.83%), which showcases its better diagnostic performance. 2025 IEEE. -
Lung cancer detection and classification with optimal feature selection and two-fold-deep-learning-classifiers
The respiratory system is undoubtedly hampered by lung disorders. Also, one of the important reasons for death among people all around the world has been lung cancer. Early discovery can advance human survival probabilities. As a result, a unique ensemble-deep-learning paradigm for lung cancer detection and classification is established in the present research effort. The projected model includes five major phases: (a) image augmentation, (b) pre-processing, (c) segmentation, (d) feature extraction, (e) feature selection, and (f) lung cancer detection and classification, respectively. The collected raw CT images are augmentation with SMOTE. The augmented images are pre-processed via Median Filtering (for noise removal) and Contrast-limited adaptive histogram equalization (CLAHE) (for image contrast enhancement). Subsequently, from the pre-processed data, the ROI is identified via optimized U-NETS. The activation function (hyper-parameter) of U-NETS is optimized via a new hybrid optimization model-Digging Tunaswarm Optimizer (DTO). This DTO is the conceptual amalgamation of two standard meta-heuristic optimization models, namely Honey Badger Algorithm (HBA) and Tuna Swarm Optimization (TSO) models, respectively. Then, from the selected ROI area, the features like texture features (Manhattan Distance-based-GLCM, GLRM), Color features (Color Histogram), and Shape features (Moments, Area, Perimeter) are extracted. Among the extracted features, the optimal features are selected using DTO. This optimal feature selection reduces the computational complexity of the projected model. Finally, using these extracted optimal features, the two-fold-deep-learning-classifier framework is trained. This two-fold-deep-learning-classifiers framework encapsulates the Bidirectional long-short term memory (Bidirectional LSTM) and the Recurrent Neural Network (RNN) and the Modified Convolutional Neural Network (M-CNN). In the first phase, the Bi-LSTM and RNN are clamped, and they are trained with the selected optimal factors. The outcome from Bi-LSTM and also RNN was fed as input to M-CNN. Final detected findings based on the existence or absence of lung cancer are acquired from the M-CNN, whose loss function has been modified with RMSE. Finally, a comparative evaluation is undergone to validate the efficiency of the projected model. The proposed model has a higher overall accuracy (92.4%) detecting modelling accuracy (96.3%) and classification accuracy (92.4%) compared to other models such as HBA, TSO, CNN, 3D CenterNet, and TSCNN. The use of a two-fold deep learning framework is responsible for these improvements, and the model also has lower failure rates (FPR and FNR) in detecting lung cancer. It is suggested that the proposed approach is effective in early-stage lung cancer detection. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
The mathematical and machine learning models to forecast the COVID-19 outbreaks in Bangladesh
The COVID-19 virus mutates in many different variants after its outbreak. Although several vaccines have been developed by many countries and implemented worldwide, it is difficult to prevent the outbreaks due to the pops out of different variants from its regular mutations. This study is an attempt to develop models which could precisely forecast the COVID-19 outbreaks in Bangladesh. In this study, we have developed a SEIRD based machine learning model to forecast the next possible one year outbreaks scenario in this country. We have tested the accuracy of this model by fitting the results with the considered historical data from March 08, 2020 to October 14, 2021. Also, we have validated this model by predicting the future inside the existing dataset, which is almost similar to the real dataset. It is observed that the final future forecasting results are very realistic compared to the current outbreak situation. Additionally, we have shown that the classical SEIRD model cannot predict the COVID-19 future outbreaks even it does not fit with the real datasets of outbreaks. Moreover, another machine learning time series forecasting model, FBProphet, has been implemented to forecast the future outbreaks of Bangladesh. Finally, we have analyzed and compared the forecasting results and hence identify the limitations of the proposed models which can improve future research in this field. 2022 Taru Publications. -
Hepatoprotective activity of Peltophorum pterocarpum leaf and bark in the isoniazid and rifampicin-induced hepatotoxic rats
Background: In recent years, various phytomedicines have been used in the treatment of hepatic disorders. The aim of this study is to find out whether Peltophorum pterocarpum bark and leaves can help rats that have been exposed to isoniazid and rifampicin-induced hepatotoxicity. Materials and Methods: The rats were divided into 10 groups each with 6 rats. The liver damage is induced by isoniazid and rifampicin. The leaf and bark of P. pterocarpum extracted with ethanol are freshly mixed in sterile water (100, 200 and 400 mg/kg body weight), and given to rats orally in the early morning as a single dosage per day until the study period. The animals were sacrificed and the tissue and serum samples were collected for further investigations. Results: The liver damage induced by isoniazid and rifampicin altered the various biochemical parameters levels, but after the treatment of P. pterocarpum barks and leaves the levels were significantly altered when compared to the negative control rats. The drug silymarin was used as a standard. Conclusion: The extracts have the protective effect of liver markers and membrane-bound enzymes against the toxin-treated rats. This result highlights the hepatoprotective properties of the leaves and barks of the plant in a similar manner and the formulation (Group 10) have high beneficial effects than other groups. 2024 -
Photosensitizer Anchored Nanoparticles: A Potential Material for Photodynamic Therapy
Detection and treatment of cancer have been demanding areas with the increase in cancer and malignant diseases across the globe. Photodynamic therapy is a multi-step treatment procedure using photosensitizers as a drug in the presence of light. Photosensitizers anchored on different nanomaterials through covalent and non-covalent interactions contribute significantly to photodynamic therapy. Nanoparticles have been employed as promising carriers to transport photosensitizers to the target cells. Photosensitizer functionalized nanoparticles are more effective in terms of stability and water solubility than bare ones. This review is a brief account of different types of nanoparticles functionalized on photosensitizers currently used for photodynamic therapy. We have focused on photosensitizer anchored organic, inorganic, and carbon-based nanomaterials, which can be effectively used in photodynamic therapy. 2022 Wiley-VCH GmbH. -
Drill hole surface characterisation of hybrid FRP laminates through statistical analysis
As it is known that the hybrid Fibre Reinforced Polymer (FRP) composite laminate is a recently evolved class of structural material. Hence, the present work deals with secondary processing ability like hole drilling on hard to machine FRP laminate. The influence of drilling attributes on the delamination factor and surface roughness contours are studied for a high thickness hybrid (carbon/glass FRP) laminates. Here, the experimentation was performed utilising Taguchis L27 design of experiments array. Later, on post-drilling, the predominant and optimum variables were studied through taguchi and variance analysis to highlight their contribution on the response functions. Taguchi results indicate that the combination of the 90?tungsten carbide tool, speed of 800 rpm, and rate of feed 50 mm/min gives the best performance concerning the delamination. Also, it was observed that the combination of the 118?tungsten carbide tool, cutting speed of 900 rpm and the rate of feed 60 mm/min give the best performance concerning surface roughness. Whereas, as per ANOVA, the highest percentage contribution factor was concerned to a tool material followed by other factors and analysed data lie with the confidence level of 95%. The work also indicates that tungsten carbide tool yield better results compared to high-speed steel tool. Further, fibre morphology has been studied, which indicates optimal structure with minimal damage. 2020 Engineers Australia. -
A Systematic Review of Various Advancements Implementation in the Field of Crop (Plant) Production
An essential component of agricultural output is pest management, especially in fertigation-based farming. Although fertigation systems in Malaysia are beneficial for irrigation and fertilization, they frequently don't have effective pest control techniques. Because pests usually live beneath crop leaves, hand spraying is difficult and labor-intensive. Insect pests have the power to seriously harm, weaken, or even kill agricultural plants, which can lead to lower yields, worse-quality goods, and unsalable outcomes. Furthermore, insects may still cause harm to processed or stored items after harvest. Therefore, creating an autonomous pesticide sprayer specifically designed for chilli fertigation systems is the main goal of this research. The main goal is to create a sprayer arm that is flexible enough to reach under crop leaves. The goal of this project is to build an autonomous, unmanned pesticide sprayer. The goal of autonomous operation is to reduce the amount of dangerous pesticides that people are exposed to, especially in enclosed spaces like greenhouses. In addition, the sprayer arm's adaptability to different agricultural circumstances makes it a valuable tool in both greenhouse and outdoor settings. It is expected that the successful adoption of the autonomous pesticide sprayer would completely transform fertigation-based farming's approach to pest management. 2024 IEEE. -
CONVECTIVE INSTABILITY IN POROUS MEDIA: IMPACT OF CHEMICAL REACTION ON MAXWELL-CATTANEO COUPLE-STRESS FERROMAGNETIC FLUIDS; [??????????? ??????????? ? ???????? ???????????: ????? ???????? ??????? ?? ?????????? ??? ?????????-???????? ? ????????????? ???????]
The current study analyzes the initiation of convection in a Maxwell-Cattaneo couple-stress ferrofluid within a porous layer, considering the effects of a chemical reaction. Small perturbations are applied to the fluid under the assumption of a zero-order energy release chemical reaction. The system is cooled from the upper layer while maintaining a steady temperature at the lower boundary. We employed linear stability analysis and determined Rayleigh number using the Galerkin Method (GM). This study emphasizes the influence of magnetic, chemical, Maxwell-Cattaneo, and couple-stress parameters on the initiation of ferro-convection. The findings indicate that both magnetic and chemical reaction parameters hasten the initiation of ferro-convection, while the porous medium and couple-stress parameters have a stabilizing effect. Notably, it is demonstrated that the destabilizing effects of chemical reactions and magnetic stresses can be effectively regulated in the presence of couple-stresses. The solutions provide insights into the potential application of ferromagnetic fluids for controlling efficient heat transfer mechanisms. 2024 Oles Honchar Dnipro National University. -
COUPLE STRESS EFFECT ON FERRO-CONVECTION TRIGGERED BY CHEMICAL REACTION IN A POROUS LAYER WITH SPARSE DISTRIBUTION; [????? ??????? ?????????? ?? ?????????????, ??????????? ???????? ???????? ? ????????? ???? ? ??????????? ??????????]
The study delves into the impact of couple stress on the commencement of convection in a porous material oriented horizontally. This layer contains a chemically reactive ferromagnetic fluid and experiences bottom heating. The investigation utilizes small perturbation methodology to explore and understand the impact of couple stress on the initiation of convection in this specific system. With the assumption of a non-autocatalytic exothermic reaction, eigenvalues are determined utilizing the Galerkin method. The analysis explores the effects of magnetic and couple stress parameters, as well as the Frank-Kamenetskii number. The observation indicates that the acceleration of the onset of ferroconvection is influenced by both magnetic forces and chemical reactions. Simultaneously, the presence of the couple stress component serves to stabilize the system. Moreover, when the nonlinearity of magnetization is sufficiently pronounced, the destabilization of the fluid layer is observed to be marginal. 2024 Oles Honchar Dnipro National University; -
Post listing IPO returns and performance in India: An empirical investigation
Objectives: (a) To analyse the performance of Indian IPOs in the short term. (b) To determine the significance of abnormal return of the IPOs. (c) To study the impact of over-subscription, profit after tax, promoters' holdings, issue price and market returns on IPO performance. Design/ Methodology/Approach: This research paper is based on empirical analysis. All the 52 IPO's listed in the NSE (National Stock Exchange, India) during the year 2018 to 2020 were considered for the study. The study is based on secondary data. The daily share price and Nifty-50 index value were taken from NSE website (www.nseindia.com) and other relevant data from red-herring prospectus of the respective company. The research / statistical tools used are: Market adjusted short run performance model, Wealth relative model, 't' test and regression analysis. Scope of the study: The scope of the study is limited to the IPO's listed only in the National Stock Exchange (NSE), India. Period of study: The study covers a period from January 2018 to December, 2020. Limitation of the study: The study considers only the influence of the external factors on the performance of IPOs. Findings: The average IPO return on the first trading day is 13.52%, ranging from -23.15% to 82.16% with standard deviation of 26.72%. The average IPO return on the third trading day was the highest and is found to be14.52%, ranging from -19.22% to 117.55% with standard deviation of 18.57%. The analysis reveals that the over subscription impacts the IPO performance and the other factors namely, issue price, Profit after Tax, market returns and promoters holdings do not influence IPO returns. Originality / Value: This is an original work that analyses the listing gain or loss and the post listing performance of IPO's in India and other factors that might influence the listing gain or loss. Copyright 2021. T. Ramesh Chandra Babu and Aaron Ethan Charles Dsouza. Distributed under Creative Commons Attribution 4.0 International CC-BY 4.0 -
Boosting Surface Coverage of CO Intermediates through Multimetallic Interface Interactions for Efficient CO2 Electrochemical Reduction
Given the inherent challenges of the CO2 electroreduction (CO2ER) reaction, solely from CO2 and H2O, it is desirable to develop selective product formation pathways. This can be achieved by designing multimetallic nanocomposites that provide optimal CO coverage, allowing for tunability in the product formation. In this work, Ag and Zn codoped-SrTiO3 (ZAST) composite immobilized carbon black (CB)-modified GCE working electrode (ZAST@CB/GCE) was developed for the electrochemical conversion of CO2 to multicarbon products. The complete reaction was carried out in a CO2-saturated aqueous system of 0.5 M KHCO3 electrolyte. A potential-dependent product selectivity was suggested based on the NMR results, wherein raising the potential value enhanced the formation of liquid products such as acetone and alcohols while suppressing competitive HER. The total Faradaic efficiency for liquid products reached an impressive 97% at a potential of ?0.6 V vs. RHE. This represents a significant advancement in acetone production pathways and valorization of CO2ER technology. 2025 American Chemical Society. -
Silver decorated copper coordination polymer for the electroreduction of CO2 to hydrocarbon liquid fuels
Recently, the development of bimetallic interfaces has emerged as a promising strategy for enhancing electrocatalytic CO2 electroreduction (CO2ER) efficiently, with selectivity towards energy-dense hydrocarbons. In this study, we report an electrochemically designed silver-decorated copper-dimercapto thiadiazole modified carbon electrode (Ag@Cu-DM), which facilitates a controlled transition from C1to C2liquid products. The optimized electrochemical synthesis method enabled precise control over the polymer-metal interface, promoting strong electronic interactions and charge transfer. The multiple characterizations conducted for the material revealed distinct lattice fringes corresponding to well-defined Cu and Ag phases. The interplay between Cu and Ag, along with the heteroatoms present within the polymer enhances CO2 activation and intermediate stabilization. The developed Ag@Cu-DM catalyst achieved 89 % F.E. for liquid products alone at a low potential of -0.73 V vs RHE and also demonstrated stable electrolysis for upto 8 Hr. 2025 Elsevier Ltd. All rights reserved. -
Copper-Embedded Aminothiazole-Engineered Nanocatalyst for Electrochemical Reduction of CO2to Alcohols
The electrochemical reduction of CO2(CO2ER) to value-added products such as methanol and ethanol is gaining significant attention as a sustainable solution to excess carbon footprints and increased energy demand. To this end, we present the electrochemical preparation of a copper-coordinated aminothiazole metallopolymer (CAM), which fosters efficient charge transfer through multiple redox couples. The prepared CAM electrode displayed excellent efficiency toward the selective production of methanol and ethanol at a low potential of ?0.73 V vs RHE, marking a significant achievement. Notably, the incorporation of Cu species along with the nitrogen- and sulfur-containing heterocyclic group of polyaminothiazole (AMp) allowed easy stabilization of the intermediates over the electrode surface, with a marked shift from C1to C2product formation. The study explores the dynamic aspects of the electrocatalyst leading to such pronounced selectivity. These findings are pivotal in encouraging more research toward the profitable production of electrofuels, particularly for decarbonizing the transportation and industrial sectors. 2025 American Chemical Society -
Electroreduction of CO2 to Methanol Using a Coordination-Moiety-Anchored Carbon-Based Electrode
Electrochemical reduction of carbon dioxide (CO2ER) has gained wide attention lately because of its potential to create a closed carbon loop, offering a sustainable solution toward environmental as well as energy crisis. However, the key challenge lies in the selective conversion of CO2 into electrofuels, such as methanol, which necessitates six proton-coupled electron transfers. In this work, we report the first instance of an electrochemically prepared Cu-coordinated 2,5-dimercapto-1,3,4-thiadiazole-modified carbon fiber paper electrode (CDM@CFP). The hence-engineered novel electrode was applied for the CO2ER reaction to produce methanol exclusively with an F.E. of 59.6% at a low potential of ?0.73 V versus RHE. Unlike most of the copper-based electrocatalysts, which result in multiple hydrocarbons, here, we have optimized a potential-dependent selectivity for maximum efficiency, which is a significant milestone in the field. 2025 American Chemical Society. -
Fluorescent detection of Pb2+ pollutant in water samples with the help of Delonix regia leaf-derived CQDs /
Synthetic Metals, Vol.291, ISSN No: 0379-6779.
Heavy metals released from different sources into water bodies are a major concern in the view of environmental protection. Their non-biodegradability and the numerous health hazards add to the issue. Scientists worldwide have emphasized the issue and are trying to resolve it by different means. Among all the methods, the fluorescent method stands out for its simplicity and rapid results. Here, the study focuses on the development of an efficient and sustainable method for the detection of lead in waste-water effluents. Carbon quantum dots (GCDs), a highly non-toxic substance developed from <em>Delonix regia</em> leaves for the purpose via a simple hydrothermal method. -
Surface modulation and structural engineering of graphitic carbon nitride for electrochemical sensing applications /
Journal of Nanostructure in Chemistry, Vol.12, Issue 5, ISSN No: 2193-8865.
The rediscovery of the old-age material graphitic carbon nitride (g-C3N4), a 2D conducting polymer, has given rise to a tide of articles exploring its diverse applications. Recently, owing to its excellent physicochemical stability and tunable electronic structure, the material has proven to be an eminent candidate for improving the sensing quality of electrodes. Excellent properties of g-C3N4 such as exposed surface area, metal-free characteristics, and low-cost synthesis have attracted facile and economical designing of sensors for a variety of analyte molecules. Herein, the readers are introduced to the historical development of g-C3N4 and escorted to the present findings of its electrochemical sensing applications. Along with its sensing utilities, the review shares some exciting insights into the synthesis, structural, and surface chemistry modulations of g-C3N4. -
Digital Transformation Initiatives for Enhancing Customer Experience in OTT Video Platforms
The rise of Over-the-Top (OTT) video platforms has transformed the way users access and interact with video content. With growing competition, it is crucial for OTT providers to improve customer experience. This chapter integrates digital transformation initiatives tailored to OTT video platforms to enhance the customer experience in the agile market. The growth of OTT video platforms depends on their ability to formulate digital transformation strategies to enhance customer experience. The key digital transformation initiatives implemented by OTT platforms to enhance the customer experience are personalisation, content curation, social media interactivity, user interface optimisation, community building, technological innovation, optimisation of monetisation plans, multi-device integration, interactive initiatives, and customer support. This chapter highlights the importance of digital transformation initiatives for meeting consumers evolving preferences and demands. This chapter offers actionable guidelines for OTT video platforms seeking to thrive in a dynamic digital landscape. 2025, IGI Global.