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Sustainable materials for urban streets: trends, challenges, and case studies
Urban planners face a growing need for efficient, smart, and sustainable projects. One of the dynamic urban elements of cities is its streets, which accommodate the majority of the public realm. This study aims to identify sustainable materials that are employed in the construction of urban streets and analyze the potential for other sustainable materials in future street design. We conduct a thorough literature review through case studies and identify sustainable materials currently in use in the construction of urban streets across the world. This study focuses on existing and potential sustainable materials for urban streets suitable for Qatar. Hence, the objectives of this study are: (1) to identify sustainable materials in the construction of urban streets; (2) to analyze challenges to using sustainable materials in making urban streets more sustainable; (3) comparative analysis of the case studies. The study concludes with sustainable urban street design guidelines derived from Qatar. 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Forecasting Stock Market Indexes Through Machine Learning Using Technical Analysis Indicators and DWT
In recent years, the stock market prices have become more volatile due to refinement in technology and a rise in trading volume. As these seemingly unpredictable price trends continue, the stock market investors and consumers refer to the security indices to assess these financial markets. To maximise their return on investment, the investors could employ appropriate methods to forecast the stock market trends, taking into account the nonlinearity and nonstationarity of the stock market data. This research aims to assess the predictive capability of supervised machine learning models for the stock market regression analysis. The dataset utilised in this research includes the daily prices and additional technical indicator data of S&P 500 Index of US stock exchange and Nifty50 Index of Indian stock exchange from January 2008 to June 2016; both the indexes are weighted measurements of the top companies listed on respective stock exchanges. The model proposed in this research combines the discrete wavelet transform and support vector regression (SVR) with various kernels such as Linear, Poly and Radial basis function kernel (RBF) of the support vector machine. The results show that using the RBF kernel on Nifty 50 index data, the proposed model achieves the lowest MSE and RMSE error during testing are 0.0019 and 0.0431, respectively, and on S&P 500 index data, it achieves 0.0027 and 0.0523, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Ethylenediamine modified carbon nanospheres from biomass for selective membrane filtration
The present work investigates the antifouling properties dye and antibiotic removal efficiency of PVDF/E-CNS membranes. Carbon nanospheres (CNS) derived from rice husk (RH) were pyrolyzed at 800 C. Further, ethylenediamine functionalized carbon nanospheres (E-CNS) were obtained via in situ decoration of ethylenediamine on acid-functionalized carbon nanospheres (O-CNS). The synthesized E-CNS were characterized by techniques such as XRD, FESEM, Raman spectroscopy, FTIR and BET. The membranes were fabricated by integrating E-CNS at varying loadings (0.10.7 wt%) via a non-solvent induced phase separation (NIPS) technique. The membrane properties were assessed through FESEM, water contact angle measurements, pure water flux, antifouling studies and membrane rejections. In comparison to the other developed membranes, PVDF-2 with 0.3 wt% E-CNS loading displayed optimal performance, pure water flux (PWF) of ?318.90 L m?2 h?1, flux recovery ratio (FRR) > 90% up to three cycles, improved contact angle (80.24 to 68.44) and reduced roughness. Furthermore, PVDF-2 achieved dye rejection of methyl orange (MO 93.2%) and rhodamine B (RB 94.6%), and antibiotic rejection of amoxicillin (AM 93.8%) and tetracycline (TC 94.1%), respectively. These findings demonstrate the integration of E-CNS derived from a bio-source, making them a promising additive to improve PVDF membrane performance. This journal is The Royal Society of Chemistry -
Functional carbon nanospheres from mulberry leaves for improved flux, fouling resistance and dye rejection
Carbon-based nanomaterials obtained from biological sources have emerged as promising candidates for advanced water remediation. In this research, carbon nanospheres (CNS) were synthesized using a one-step pyrolysis method, utilizing mulberry leaves as a precursor. The CNS were further modified through surface functionalization with the addition of carboxyl groups to enhance surface hydrophilicity, contributing to improved membrane performance. The functionalized carbon nanospheres (FCNS) ranging from 0.1 to 0.6 wt% were incorporated into Polysulfone (PSF) membranes. Scanning electron microscopy (SEM) analysis revealed the spherical nature and average size found to be 6070 nm. The PSF/FCNS composite membranes were casted via phase inversion method. The fabricated PSF/FCNS composite membranes with 0.0, 0.1, 0.2, 0.4 and 0.6 wt% FCNS concentrations were defined as M-0, M-1, M-2, M-3 and M-4 respectively. SEM, Contact angle, Pure water flux and Antifouling ability using Bovine serum albumin (BSA) were conducted to achieve the target of this paper. Among the developed membranes, the M-2 variant, with 0.2 wt% FCNS, demonstrated optimal performance, exhibiting improved hydrophilicity from 77.49 to 65.48 and reduced surface roughness. This resulted in an increased pure water flux (PWF) of 632.5 L m?2 h?1 and a flux recovery ratio (FRR) of 93.54 %. Furthermore, M-2 achieved dye rejection rates 96.5 % and 95 % for methylene blue and congo red, respectively. These findings underscore the potential of low-cost, bio-derived FCNS as efficient nanofillers for developing high-performance membranes, offering a suitable approach to tackle the multifaceted challenges of water treatment applications. 2025 Elsevier Ltd -
Leveraging ML Based Technique for Mobile Sales Forecasting
The mobile phone industry is very competitive, so mobile sales forecasting is now imperative for businesses to forecast demand and order inventory in advance to plan strategically. This research focuses on the higher accuracy of mobile sales prediction and studies several machine learning models like Brand, Ratings, RAM, ROM, Battery- Power, pixel- height- and width, and targets alongside Camera Details as an alternate set to association rule mining. A real-time dataset that covers real-world mobile phone sales data has been collected and had its features pre-processed to fill in missing values and do the definite column encoding. Dataset were tested to understand the model performance of several predictive models, such as Decision Trees, Support Vector Machine (SVM), and ensemble methods (Random Forest and Gradient Boosting). The performance of each model was measured by accuracy, precision, recall, and F1-score. To address the issue of class in the sales categories (Low, Medium, High), stratified sampling and Synthetic Minority Over-sampling Technique (SMOTE) techniques were used. The results showed the predictive solid abilities of all the models in forecasting sales for different segments, with ensemble models performing better than individual classifiers in terms of prediction accuracy and robustness. This approach was further strengthened by applying hyperparameter tuning and cross-validation to improve the model's performance. The results are predicted to drive mobile retailers in the direction of improving demand forecasting and making data-driven decisions towards operational efficiency. 2025 Bharati Vidyapeeth, New Delhi. -
Introduction to brand management in the digital age
The rapid development of digital technology has introduced a new order of large-scale change within brand management in the fast-changing landscape of the digital marketplace. This chapter provides the fundamental principles of brand management within the digital era and demonstrates the transformation of building brands via online platforms, data analytics, and digital technologies. Others might state that social media, e-commerce, and AI integration offer brands a new horizon for managing brand equity, improving customer engagement, and creating experiences to adapt to changing tastes. Adjustment towards this change can prove challenging for the brands, but on the other hand, they are opening new possibilities that drive innovation in performance and strategy about their brands. 2025, IGI Global Scientific Publishing. All rights reserved. -
Design and experimental validation of multi-section directional coupler with arbitrary coupling and high directivity for sub-6 GHz UWB applications
This work presents a geometrically simple topology for developing an ultra-wideband directional coupler with improved coupling and directivity. A short-ended coupled-line structure is used to achieve an ultra-wideband, tightly coupled symmetric three-section coupler using the microstrip line technology. The proposed design demonstrates an explicit improvement of approximately 1.2 dB in coupling compared to conventional multi-section directional couplers. Calculated, simulated, and measured responses validate the effectiveness of the proposed configuration in terms of low-ripple coupling bandwidth, low insertion loss, and improved directivity performance compared to respective responses of the conventional structure. Couplers featuring a higher number of sections to implement different bandwidths and couplings can be fabricated using the presented structure due to its transmission line-based approach. A prototype of the three-section directional coupler with coupling of 7.6 dB, 8.1 dB, and 8.3 dB and corresponding bandwidths of 104%, 123% and 133% is designed, fabricated, and measured. The experimental results confirm that the coupler can reliably achieve higher coupling with ultra-wideband response from 0.75 GHz to 3.75 GHz (5:1) with 8.3 1.4 dB (ripple). Additionally, the design yields promising performance with return loss > 16 dB, isolation > 20 dB, a phase difference of 90 4, and directivity > 30 dB, and the maximum circuit size is 0.067?02. This work aligns with SDG 9: Industry, Innovation and Infrastructure by advancing high-performance microwave components that support efficient, reliable, and scalable communication infrastructure. 2026 Patel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. -
Mobile-Based Indian Currency Detection Model for the Visually Impaired
According to surveys held in 2019, India holds the largest population standing just after China, but when it comes to visually impaired people, India ranks number one. There are approximately 37 million people across India who are suffering from visual impairment. Special care and measures are taken to help these people live a peaceful life as any other citizen of India, but with the demonetization that happened in the recent years, the Indian economy was replaced with newer currency notes as an attempt to stop black money and fight corruption. Even though the objectives were clear and attainable, with the newer currency notes, the visually impaired people are facing various problems, as there is no provision for them to actually check the currency as the notes are not equipped with Braille system and the sizes of each and every currency is also the same in many cases. To counteract this problem, a mobile-based Indian currency detection model would be a better solution as it enables a visually impaired person to identify the value of specific currency he is holding. The mobile-based Indian currency detection model is the proposed model which will be using image processing for feature extraction and a basic CNN (convolutional neural network) for identification of currency with the given feature inputs. This model is being made into a mobile-based application so as to enable a visually impaired person to check for any possible frauds as fast as possible. 2020, Springer Nature Switzerland AG. -
Porous carbon nanoparticles dispersed nematic liquid crystal: influence of the particle size on electro-optical and dielectric parameters
Porous carbon nanoparticles (PCNPs) of four different sizes (~180nm, ~51nm, ~41 and ~25nm) were dispersed into a nematic liquid crystal (NLC) in 0.25wt% concentration. PCNPs were derived from biowaste materials and pyrolysed at elevated temperatures to get the porous structure. Polarising optical microscopic observations were carried out in dark and bright states on both the pure NLC as well as NLC-PCNPs composites. Homogeneous alignment was well maintained in all the composites except the one with the highest sized (~180nm) PCNPs. Birefringence, relative permittivity and dielectric anisotropy, increases as the size of the PCNPs is decreased in the composites. The threshold voltage was also found to decrease with the decrease in the size of the PCNPs. Such investigations may be useful for the fabrication of display devices such as flat panel displays (FPDs) and phase shifters. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Impact of porous nanoparticles on the electro-optical and dielectric parameters of nematic liquid crystals for display applications: Cost effective approach
In this study, several vital electro-optical and dielectric properties of nematic liquid crystal (NLC) dispersed with porous carbon nanoparticles (PCNP) with three different concentrations were measured. NLCs are birefringent materials. Increased birefringence was observed for NLC-PCNP composites. Dielectric study was also performed for NLC dispersed with PCNPs. Dielectric anisotropy was found to be increased for PCNP dispersed NLC system. Contrast ratio was also measured for NLC dispersed with PCNP, and it is found to be enhanced. Decreased threshold voltage was observed after dispersing PCNP into NLC. High birefringence reduces the cell gap so this work may be applicable in the making of flat panel displays (FPDs). 2024 Taylor & Francis Group, LLC. -
Influence of composite mixtures between nematic liquid crystal and porous carbon nanoparticles towards photoluminescence and UV absorbance
The optical parameters of the liquid crystalline materials can be tuned by the dispersion of nanoparticles. Concentration of dopant in the host LC material affects its optical properties significantly, which makes the dispersed system suitable for LC-based devices. In the present investigation, we have studied the effect of different concentrations of nanoparticles on the optical properties of LC, as a guesthost system, where PCNP is guest material and NLC is host material. Porous carbon nanoparticles (PCNPs) were dispersed into the nematic liquid crystal (NLC) in three different concentrations. Optical parameters were measured for pure NLC and NLC-PCNP composites. Photoluminescence (PL) study was performed and it was found that the PL intensity increased for the PCNP dispersed system. High photoluminescence has much importance in the luminescent displays. Full width half maxima (FWHM) were also determined by the Gaussian fitting of PL intensity data. UV absorbance was also measured which gets increased for the PCNP dispersed NLC system when compared to pure NLC. Optical bandgap was found to be reduced after the dispersion of PCNP into NLC. Several other parameters such as absorption coefficient and optical density were also determined. The proposed work may be significant for the liquid crystal displays (LCDs) and other devices which require less bandgap materials. This work may also put some light on the effect of dopants on the LC material in the research based on guesthost system. Increasing the photoluminescence and creating less bandgap materials using carbon nanoparticles is a real challenge, and porous nanoparticles used here overcome this challenge. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature. -
Does Sentiment and Emotion Affect Mental Health? A Multi-task Classification Framework for Comprehensive Understanding of Mental Health, Emotion, and Sentiment from Motivational Conversations
In light of the escalating challenges posed by our modern lifestyle, cultivating a nuanced approach for addressing mental health issues becomes imperative. Navigating the complexities of daily life demands a thoughtful and comprehensive strategy to identify and address the diverse array of mental health issues that may manifest. The challenges in accurately identifying mental health expressions stem from their complex character of communication, which frequently shares linguistic patterns and similar expressive nuances as communicated by humans. However, we hypothesize that mental health conditions are closely associated with affective factors in particular feelings, moods, and emotions. These states define how we think, feel, and behave. Thus, in this article, we aim to explore and analyze the association of the affective states such as sentiment and emotion with mental health in the view of identifying mental health conditions accurately once the feelings and emotions of humans are understood. In this regard, this article investigates multi-task classification encompassing mental health disorder identification (MHDI), emotion recognition (ER), and sentiment analysis (SA) in non-clinical conversations where MHDI forms the primary task and ER-SA the auxiliary tasks boosting the identification of the primary one. To demonstrate our hypothesis, we propose Core Fusion Network (CFN), a variation of multi-tasking in light of the significance that sentiment and emotion plays in understanding mental health. This method adeptly considers private and shared features across tasks, significantly enhancing classification precision. For our study, we extend the recently released MotiVAte dataset containing dyadic conversations between support seekers and a virtual assistant imparting hope and motivation to enclose emotion and sentiment tags for each conversation in a semi-supervised manner. Our hypothesis is reinforced by an extensive ablation study with state-of-the-art multi-task models and the proposed Core Fusion Network (CFN), which exhibits increased accuracy of 89.12% for MHDI, 64.24% for ER, and 79.04% for SA in the tri-task variant as opposed to its corresponding uni-task and bi-task variants. These outcomes underscore the potential of multi-task learning in streamlining mental health classification by integrating emotional and sentiment dimensions. 2025 Copyright held by the owner/author(s) Publication rights licensed to ACM. -
Impact of porous nanoparticles on the electro-optical and dielectric parameters of nematic liquid crystals for display applications: Cost effective approach
In this study, several vital electro-optical and dielectric properties of nematic liquid crystal (NLC) dispersed with porous carbon nanoparticles (PCNP) with three different concentrations were measured. NLCs are birefringent materials. Increased birefringence was observed for NLC-PCNP composites. Dielectric study was also performed for NLC dispersed with PCNPs. Dielectric anisotropy was found to be increased for PCNP dispersed NLC system. Contrast ratio was also measured for NLC dispersed with PCNP, and it is found to be enhanced. Decreased threshold voltage was observed after dispersing PCNP into NLC. High birefringence reduces the cell gap so this work may be applicable in the making of flat panel displays (FPDs). 2024 Taylor & Francis Group, LLC. -
Smart Textiles and Wearables for Health and Fitness
Smart Textiles and Wearables for Health and Fitness provides an in-depth exploration of how innovative technologies and materials are reshaping healthcare, making it an essential resource for anyone looking to understand the transformative power of smart textiles and wearables in patient monitoring, diagnosis, and rehabilitation. Smart Textiles and Wearables for Health and Fitness explores the transformative influence of flexible electronics on the healthcare field. The books chapters include a broad spectrum of topics, each offering valuable perspectives on the intersection of textiles, wearables, and health technology. Smart Textiles and Wearables for Health and Fitness delves into the unique technologies and materials driving the flexible electronics revolution, offering insights into their development and applications. The study explores the diverse uses of intelligent textiles and wearable devices in healthcare, encompassing activities such as monitoring patients, diagnosing conditions, aiding rehabilitation, and administering therapeutic interventions. In this volume, we will explore the incorporation of sensors, biometrics, and biomarkers into textiles to showcase their capacity for immediate health monitoring and data collection. Additionally, we will explore the possible uses of smart textiles and wearables in managing chronic conditions, tracking sports and fitness activities, and facilitating human-computer interaction in medical settings. This book promises an engaging journey through the frontiers of technology, offering a comprehensive understanding of the transformative potential of smart textiles and wearables in revolutionizing healthcare delivery and improving patient outcomes. 2025 Scrivener Publishing LLC. -
CaSi2O5:Sm3+ Orange -Red Emitting Phosphors for Latent Fingerprint Detection Application
Orange-red emitting CaSi2O5:xSm3+ (x = 0.1, 0.2, 0.5, 1, 1.5, 2, and 2.5mol% of Sm3+) phosphors were synthesized by a high-temperature solid-state reaction. In this study, the crystal structure, phase purity, functional group presence, and their bending and stretching vibrations, photoluminescence (PL) spectra, thermoluminescence (TL) spectra, and colour purity was systematically investigated. The phosphor exhibits a strong excitation with the charge transfer band (CTB) of O2? and Sm3+ at 263nm. Under 263nm excitation, the CaSi2O5:Sm3+ phosphor shows characteristic peaks at 595nm and 629nm, which are attributed to the characteristic 4G5/2?6H7/2 and 4G5/2?6H9/2 transitions of the Sm3+ ions, respectively. The doping concentration x = 2mol% is found to be the optimal doping concentration. The CIE coordinates of the optimal concentration phosphor CaSi2O5:2Sm3+ are found to be (0.589, 0.41) in the orange-red region with a colour purity percentage of 96.93%. Judd-Ofelt analysis was also carried out with the photoluminescence emission spectrum, in order to investigate the transition dynamics. Fingerprints were developed on non-porous glass and aluminium foil substrates. The experimental results display that the CaSi2O5:xSm3+ phosphors have a huge potential for practical applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Electronic Voting Systems Using a Blockchain-Based Encrypted Identity Management
The use of electronic voting technologies has grown in popularity as a way to make elections more secure and accessible. The implementation of blockchain-based encrypted identity management in electronic voting is explored in this study, which also offers a solid option to improve the reliability and credibility of voting systems. This study explores the possibilities for anonymous and transparent electronic voting while preserving voter privacy and anonymity by incorporating blockchain technology. It has always been challenging to create an electronic voting system that properly satisfies the requirements of administrators. This problem is now being resolved by blockchain technologies, which provide a distributed database with irreversible, encrypted identity management and secure transactions. A fascinating advancement in the realms of data innovation, dependability, and transparency is distributed ledger technology. Distributed ledger technology is commonly used in public blockchain. Virtually limitless potential for earning from sharing economies are provided by blockchain technology. This project aims to determine whether blockchain technology can be used to create electronic voting devices are used as a service. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Interface between the brain and computer to improve the e-commerce user security experience
Using brain-computer interface (BCI) technology for online shopping offers a state-of-the-art way to address the issue of enhancing user experience by creating more personalized and user-friendly shopping environments. This study explores how brain-computer interfaces (BCIs) can improve e-commerce platforms by better understanding customer preferences, emotional responses, and decision-making tendencies through the collection of real-time neural data. Brain-computer interfaces, or BCIs, use brain activity analysis to provide personalized recommendations, streamline navigation, and improve product displays-all of which contribute to a more enjoyable and rewarding shopping experience. This research examines the effects of BCI-enhanced e-commerce systems on user involvement, contentment, and purchase decisions. An extensive assessment of BCIs' effectiveness in improving the overall shopping experience is conducted through experimental analysis and customer feedback. The results show that adding BCIs to e-commerce systems can significantly boost user engagement and make. 2025, IGI Global Scientific Publishing. -
Cost effective porous areca nut carbon nanospheres for adsorptive removal of dyes and their binary mixtures
Novel porous nanospheres from areca nuts (ACNPs) were synthesized via one-step pyrolysis without the use of any chemical treatment and the materials were used as adsorbents for the removal of cationic methylene blue (MB) and anionic methyl orange (MO) as well as their binary mixtures. Around, 67 tonnes of areca nut biowaste is generated every year which are then burnt due to their slow rate of decomposition resulting in higher carbon footprints. Biosorbents are generally a preferable alternative for dye adsorption but involve chemical modification for surface enhancement and complex sample treatment. In this work, ACNPs, were investigated for their efficiency in the raw form and were characterized by SEM, EDS, FTIR, XRD, and BET techniques before and after subjecting to the dye adsorption studies. The BET analysis of the adsorbents showed a high specific surface area of 693.8 m2/g when prepared at 1000 C, while the N2 adsorption-desorption plot showed type-IV isotherm, suggesting the microporous nature of the carbon matrix. Batch equilibrium studies showed the removal efficiency of >95% for both the dyes and their binary mixtures under the optimum conditions of 0.15 g/L dosage, 10 ?M concentration and contact time of 70 min. Due to the synergistic effects of the binary dyes, higher removal efficiency of MB compared to MO was observed in the binary mixture. Adsorption results were tested using Langmuir, Freundlich, Temkin, Redlich-Peterson, and Elovich isotherms to assess the best fit of the models. The qm value of MB was found to be 97.37 mg/g, while that of MO was 71.22 mg/g which is higher compared to individual dye components having lower values of 86.12 mg/g and 50.35 mg/g, respectively. Extended Langmuir and Jain and Snoeyink isotherms were used for binary data interpretation. The kinetic results showed good agreement with the Pseudo-second order equation, indicating internal diffusion. The possible mechanism involved electrostatic and ?-? interactions between the dye molecules and ACNPs. This approach is comprehensible and cost effective and can be utilized for dye removal in textile industries. 2023 Elsevier Inc. -
Garlic peel based mesoporous carbon nanospheres for an effective removal of malachite green dye from aqueous solutions: Detailed isotherms and kinetics
Biowaste based nanoadsorbents have gained much attention in the recent times for wastewater decolourization owing to their low cost, high surface area and high adsorption capacities. In the present research, garlic peel based nanoparticles (GCNP) were synthesized at different temperatures by a one step pyrolytic green approach for the effective removal of cationic dye, malachite green from the aqueous medium. The surface properties of Garlic nanoparticles were elucidated by N2 adsorption- desorption and all the GCNP samples were found to exhibit Type IV(a) isotherm indicating the presence of mesopores in carbon matrix. Using BET calculations, highest surface area (380 m2/g) was obtained for GCNP synthesized at 1000 ?C. Characterization of nanoparticles was done by XRD, EDAX, SEM and FTIR studies before and after the dye treatment. Adsorption studies conducted using different parameters like contact time, concentration and pH and dosage of adsorbent showed removal efficiency above 90% for the contact time of 70 min. Best adsorption experimental results were obtained for GCNP synthesized at 1000 C ascribable to its high surface area, higher total pore volume (0.26 cm2/g) and higher carbon content. Four adsorption isotherm models were used to validate batch equillibrium studies and the results showed data in good agreement with Langmuir and Freundlich isotherms with maximum Langmuir adsorbtion capactiy to be 373.7 mg/g. Kinetic modelling of the data showed best fit with the Pseudo second order model with rate constant value of 48.726 g mg?1 min?1. Regenerative studies were conducted conducted upto 6 cycles. Also the GC nanoparticles were tested for their compatibility in membrane form wherein, removal efficiency results were obtained for GCNP anchored in polyvinyl difluoride (PVDF) and polysulfone (PSF) membrane matrix for dye adsorption. 2022 Elsevier B.V. -
Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture
Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days. To overcome such issues in e-Healthcare systems, Internet of Things (IoT) applications have been deployed using cloud computing (CC) approaches. There are still a number of disadvantages to using CC, including latency, bandwidth, energy usage, and security and privacy concerns. Fog computing (FC), a CC development, may be able to overcome these obstacles. DiaFog enabling remote users for real-time diagnosis of diabetic mellitus disease (DMD) has been proposed in this study, which is based on the combined ideas of IoT, cloud, and fog computing, as well as an ensemble deep learning (EDL) technique. The proposed system is trained with EDL approaches on the integrated dataset of two diabetes mellitus disease datasets (DMDDs), namely, Pima Indians Diabetes Dataset (PIDD) and Hospital Frankfurt Germany Diabetes Dataset (HFGDD), obtained from the UCI-ML and Kaggle repository, respectively, and the integrated dataset of these two. The suggested system has been used to demonstrate accuracy, precision, recall, F-measure, latency, arbitration time, jitter, processing time, throughput, energy consumption, bandwidth utilization, network utilization, scalability, and more. In the remote instantaneous diagnosis of diabetic patients, the integration of IoT-fog-cloud is useful. The results of the trials show the value of employing FC principles and their applicability for speedy diabetic patient remote diagnosis. PACS-key is describing text of that key PACS-key describing text of that key. 2022 Abhilash Pati et al.