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Novel artificial intelligence-based ensemble learning for optimized software quality
Artificial intelligence (AI) contributes towards improving software engineering quality; however, existing AI models are witnessed to deploy learning-based approaches without addressing various complexities associated with datasets. A literature review showcases an unequilbrium between addressing the accuracy and computational burden. Therefore, the proposed manuscript presents a novel AI-based ensemble learning model that is capable of performing an effective prediction of software quality. The presented scheme adopts correlation-based and multicollinearity-based attributes to select essential feature selection. At the same time, the scheme also introduces a hybrid learning approach integrated with a bio-inspired algorithm for constructing the ensemble learning scheme. The quantified outcome of the proposed study showcases 65% minimized defect density, 94% minimized mean time to failure, 62% minimized processing time of the algorithm, and 43% enhanced predictive accuracy. 2025, Institute of Advanced Engineering and Science. All rights reserved. -
Novel biocompatible zinc oxide nanoparticle synthesis using Quassia indica leaf extract and evaluation of its photocatalytic, antimicrobial, and cytotoxic potentials
Prognostic research points to the necessity and relevance of revamping polluted environments. The toxic effect of textile dyes released into waterbodies can be reduced by the degradation process and alternate methods in nanotechnology are used to lessen the gravity of the situation. Compared with chemical and physical NP synthesis, plant extract-based nanoparticle synthesis is an environmentally friendly alternative method, and the use of waste leaves in this process is an added advantage. Quassia indica zinc oxide nanoparticles (QI-ZnO NPs) were synthesised in the current work employing a simple and cost-effective process using Q. indica leaf extract. The surface plasmon peak was visible in the UV-Vis absorption spectrum of the decreased reaction mixture at 346 nm. The average crystallite size of the QI-ZnO NPs was found to be 16.66 nm. The QI-ZnO NPs were found to have a stable zeta potential of ?28.4 mV. The surface morphology of the optimised QI-ZnO NPs was observed to be hexagonal using field emission scanning electron microscopy and high-resolution transmission electron microscopy. Under UV light irradiation, the photocatalytic degradation of industrial textile dyes Reactive Blue-220, Reactive Yellow-145, Reactive Red-120, and Reactive Blue-222 showed degradation efficiency of 8090%. Antibacterial and antifungal activity was assessed using well diffusion on gram-positive and gram-negative microorganisms. When administered to the A549 and MDA-MB-231 cancer cell lines, QI-ZnO NPs displayed significant anticancer activities. Limited studies in the area of plant extract-based nanoparticle synthesis mark the novelty of this attempt and this trailblazing and pioneering approach using non-toxic QI-ZnO NPs synthesised through green synthesis is futuristic and sustainable helping in effective wastewater treatment. Graphical abstract: [Figure not available: see fulltext.] 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Novel biocompatible zinc oxide nanoparticle synthesis using Quassia indica leaf extract and evaluation of its photocatalytic, antimicrobial, and cytotoxic potentials
Prognostic research points to the necessity and relevance of revamping polluted environments. The toxic effect of textile dyes released into waterbodies can be reduced by the degradation process and alternate methods in nanotechnology are used to lessen the gravity of the situation. Compared with chemical and physical NP synthesis, plant extract-based nanoparticle synthesis is an environmentally friendly alternative method, and the use of waste leaves in this process is an added advantage. Quassia indica zinc oxide nanoparticles (QI-ZnO NPs) were synthesised in the current work employing a simple and cost-effective process using Q. indica leaf extract. The surface plasmon peak was visible in the UV-Vis absorption spectrum of the decreased reaction mixture at 346 nm. The average crystallite size of the QI-ZnO NPs was found to be 16.66 nm. The QI-ZnO NPs were found to have a stable zeta potential of ?28.4 mV. The surface morphology of the optimised QI-ZnO NPs was observed to be hexagonal using field emission scanning electron microscopy and high-resolution transmission electron microscopy. Under UV light irradiation, the photocatalytic degradation of industrial textile dyes Reactive Blue-220, Reactive Yellow-145, Reactive Red-120, and Reactive Blue-222 showed degradation efficiency of 8090%. Antibacterial and antifungal activity was assessed using well diffusion on gram-positive and gram-negative microorganisms. When administered to the A549 and MDA-MB-231 cancer cell lines, QI-ZnO NPs displayed significant anticancer activities. Limited studies in the area of plant extract-based nanoparticle synthesis mark the novelty of this attempt and this trailblazing and pioneering approach using non-toxic QI-ZnO NPs synthesised through green synthesis is futuristic and sustainable helping in effective wastewater treatment. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. -
Novel biogenic CNS@AgNPs hybrid nanostructures for electrochemical detection of sucralose: Experimental and in silico strategies
Carbon, the most abundant and versatile element, has played a significant role in scientific innovations, forming the backbone of material science and nanotechnology. This study presents the first reported simultaneous photogenic synthesis of carbon nanospheres (CNS) integrated with silver nanoparticles (CNS@AgNPs) using Coriander sativum seed extract for sucralose detection. The CNS@AgNPs formation, mediated by oleic acid from the extract, was confirmed with GCMS analysis. The morphology of the CNS@AgNPs was characterized using SEM, TEM, XPS, XRD, Raman, BET, Diffuse Reflectance Spectroscopy (DRS), and Thermogravimetric analysis (TGA). The fabricated GE/Nafion/CNS@AgNPs electrode demonstrated an intense oxidation peak current at +0.7V, with Differential Pulse Voltammetry (DPV) showing a linear response from 2.0 to 14?M, with a LOD and LOQ of 0.2?M and 0.62?M (R2=0.998), respectively. The Density Functional Theory (DFT) studies revealed key mechanistic insights, including the methanol cleavage energy (~3.135נ103eV) and HOMO-LUMO differences between neutral sucralose and its cationic form. Monte Carlo (MC) simulations confirmed favourable adsorption energy (?52.739kcal/mol) with specific binding interactions (3.3578.653 influencing electron transfer pathways. This eco-friendly approach presents the potential of sustainable materials for developing efficient electrochemical sensors for detecting artificial sweeteners in real samples. 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Novel booster control system for fully automotive driverless vehicle /
Patent Number: 201941039882, Applicant: Dr. Debabrata Samanta.
The present disclosure presents a novel electronic booster control system for fully automotive driverless vehicle. The it discloses a system of vacuum booster with an automotive air compressor system which comprises a compression piston, a power transmission component and a power input part with integrated to plurality of sensors , servo controllers and a central control module of the fully automotive driverless vehicle. -
Novel carbon nano-onions from paraffinum liquidum for rapid and efficient removal of industrial dye from wastewater
Carbon nano-onions (CNOs) are fascinating zero-dimensional carbon materials owning distinct multi-shell architecture. Their physicochemical properties are highly related to the parent material selected and the synthesis protocol involved. In the present work, we report for the first time novel CNO structures encompassing discrete carbon allotropes, namely, H18 carbon, Rh6 carbon, and n-diamond. These structures were cost-effectively synthesized in gram scale by facile flame pyrolysis of paraffinum liquidum, a highly refined mineral oil. The as-synthesized and chemically refashioned CNOs are quasi-spherical self-assembled mesopores, manifesting remarkable stability and hydrophilicity. The CNO structures exhibit excellent dye adsorption characteristics with high removal capacity of 1397.35mg/g and rapid adsorption kinetics with a minimal adsorbent dosage of 10mg/L, for a low concentration of 20mg/L methylene blue dye. The novel CNOs assure potential implementation in the remediation of low concentration and high volume of dye-contaminated wastewater. Graphical abstract [Figure not available: see fulltext.] 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Novel deep eutectic solvent catalysed Single-Pot open flask synthesis of Tetrasubstituted-1H-Pyrroles
Pyrrole and its analogs have garnered immense attention due to their multifaceted biological significance and versatile applications, ranging from medicinal agents to fundamental biological pigments. Despite their prominence, pyrrole synthesis with multiple substituents is complex and calls for innovative approaches to green chemistry. This study delves into synthesizing novel 3,5-dimethyl-1H-pyrroles via multicomponent reactions (MCRs) employing deep eutectic solvents (DES). Due to their eco-friendly nature, these DESs provide a safer substitute for traditional solvents. Specifically, a novel three-component DES (3CDES) was formulated, showcasing promising catalytic activity for multiple cycles with excellent product generation. The synergy between MCR and DES elucidates their combined potential in fostering a sustainable and efficient green synthesis route with the E-factor of 0.1699. 2024 Elsevier B.V. -
Novel Deep Neural Network Based Stress Detection System
Stress is a state of tension on an emotional or bodily level. Frustration, despair, anxiety, and other mental health problems can all be brought on by Stress. Strain is a side effect of Stress. People can openly share their views and opinions on social media networking sites like Twitter and Facebook, which are highly popular. The COVID 19 pandemic has wreaked havoc on millions of peoples lives all across the world. The public has experienced Stress as a result of the various measures employed to stop the spread of COVID 19, including confinement and social isolation. The current research seeks to develop an unique COVID 19 scenario-based deep neural network-based Stress detection system using tweets related to COVID 19. We use deep learning to create three models. RNN with single LSTM layer, two layers of LSTM with RNN followed by bidirectional LSTM layer is built to detect Stress for the considered dataset. A number of recurrent neural networks are built upon the Keras layers. The optimization algorithm called RMSProp and Sigmoid activation function is used. It is observed that RNN with 2 layers of LSTM outperforms the other deep learning architectures constructed. 2023 American Institute of Physics Inc.. All rights reserved. -
Novel dioxidomolybdenum complexes containing ONO chelators: Synthesis, physicochemical properties, crystal structures, Hirshfeld surface analysis, DNA binding/cleavage studies, docking, and in vitro cytotoxicity
A series of dioxidomolybdenum (VI) complexes, [MoO2(ESB)H2O]DMF (1), [MoO2(ESB)MeOH] (2), and [MoO2(ESB)H2O]EtOH (3), containing 3-ethoxysalicylaldehyde benzoylhydrazone have been synthesized and analysed using various spectral and analytical techniques such as elemental analyses, IR spectra, UVVis absorption spectra, X-ray crystallography, and Hirshfeld surface analysis. Based on the elemental and spectral analysis, six-coordinate geometry was assigned for these complexes wherein the hydrazone ligand binds to the metal centre in its dianionic enolate form through ONO donor set. Distorted octahedral geometry of complexes 1 and 2 was evidenced from their crystal structures, which is typical for many cis-dioxido complexes of MoVI. The proligand and the new complexes were examined for their DNA binding, DNA cleavage, and cytotoxic properties. The DNA binding efficiency of the compounds in terms of their binding constants (Kb) of the metal complexes was observed to be 1.3727 105 M?1, 3.0194 104 M?1, and 1.13206 104 M?1 for [MoO2(ESB)H2O]DMF (1), [MoO2(ESB)MeOH] (2), and [MoO2(ESB)H2O]EtOH (3), respectively, indicating that these complexes strongly bind to DNA. To determine the binding interactions of the complexes with DNA and protein (BSA), molecular docking studies were carried out. Gel electrophoresis study reveals the fact that the complexes cleaved supercoiled pUC-18 DNA to nicked form (Form II) in the presence and absence of H2O2. The complexes showed significantly high cytotoxicity against MCF-7 (breast cancer cells). 2021 John Wiley & Sons, Ltd. -
Novel electrochemical biosensor key significance of smart intelligence (IoMT & IoHT) of COVID-19 virus control management
Recent outbreak of COVID-19 pandemic has led to the different possibilities of the development of treatment against corona virus. To know the phylogenicity of SARS-CoV, various studies have been conducted with the outcome of the results showing virulence is caused due to spike protein. Various detection techniques with clinical approach like imaging technology, RT-PCR etc. are comparatively expensively than the use of biosensors. Nano-biosensors have an excellent way of approach to track the conditions of individual and public providing information about the existing condition and treatment status. Electrochemical nano-biosensors are referred as an excellent way of detection. The use of graphene based electrochemical nano-biosensors are most advantageous due to its elevated properties. Fluorescence investigation is one of the precise ways of sensing, optical biosignals that helps in obtaining real time results with high accuracy and negligible changes. The potential application of nano-biosensors are very wide, improvised and advanced Nanotechnology helps in the use of nano-biosensors detect all possible biosignals. Significant ubiquitous IoT-enabled novel sensor technologies that can be potentially utilized to respond various facets the growing COVID-19 pandemic from diagnostic and therapeutics to the prevention stage. 2022 Elsevier Ltd -
Novel heterocyclic thiosemicarbazones derivatives as colorimetric and "turn on" fluorescent sensors for fluoride anion sensing employing hydrogen bonding
(Chemical Equation Presented) Two novel heterocyclic thiosemicarbazone derivatives have been synthesized, and characterized, by means of spectroscopic and single crystal X-ray diffraction methods. Their chromophoric-fluorogenic response towards anions in competing solvent dimethyl sulfoxide (DMSO) was studied. The receptor shows selective recognition towards fluoride anion. The binding affinity of the receptors with fluoride anion was calculated using UV-visible and fluorescence spectroscopic techniques. 2013 Elsevier B.V. All rights reserved. -
Novel HGDBO: A Hybrid Genetic and Dung Beetle Optimization Algorithm for Microarray Gene Selection and Efficient Cancer Classification; [Nuevo HGDBO: Un Algoritmo Hrido de Optimizaci Genica y de Escarabajos Peloteros para la Selecci de Genes en Microrrays y la Clasificaci Eficiente del Ccer]
Introduction: ovarian cancer ranked as the seventh most common cancer and the eighth leading cause of cancer-related mortality among women globally. Early detection was crucial for improving survival rates, emphasizing the need for better screening techniques and increased awareness. Microarray gene data, containing numerous genes across multiple samples, presented both opportunities and challenges in understanding gene functions and disease pathways. This research focused on reducing feature selection time in large gene expression datasets by applying a hybrid bio-inspired method, HGDBO. The goal was to enhance classification accuracy by optimizing gene subsets for improved gene expression analysis. Method: the study introduced a novel hybrid feature selection method called HGDBO, which combined the Dung Beetle Optimization (DBO) algorithm with the Genetic Algorithm (GA) to improve microarray data analysis. The HGDBO method leveraged the exploratory strengths of DBO and the exploitative capabilities of GA to identify relevant genes for disease classification. Experiments conducted on multiple microarray datasets showed that the hybrid approach offered superior classification performance, stability, and computational efficiency compared to traditional methods. Ovarian cancer classification was performed using Nae Bayes (NB) and Random Forest (RF) algorithms. Results and Discussion: the Random Forest model outperformed the Nae Bayes model across all metrics, achieving higher accuracy (0,96 vs. 0,91), precision (0,95 vs. 0,91), recall (0,97 vs. 0,90), F1 score (0,95 vs. 0,91), and specificity (0,97 vs. 0,86). Conclusions: these results demonstrated the effectiveness of the HGDBO method and the Random Forest classifier in improving the analysis and classification of ovarian cancer using microarray gene data. 2024; Los autores. -
Novel Hybrid Machine-Learning Algorithms for Resource Optimization in Cloud
The resource optimization process in the cloud is crucial and can be achieved through the ideal Load Balancing (LB) mechanism. The cloud undergoes several challenges with resource optimization due to poor LB mechanism, where its Virtual Machines (VMs) are either overloaded or idle. The main aim of this experimental-based research is to enhance the LB mechanism of the cloud by implementing and comparing the performance of novel hybrid LB algorithms RLFCFS and RLSJF to optimize the resources. The RLFCFS and RLSJF novel LB algorithms are designed by combining the Reinforcement Learning (RL) technique with the heuristic FCFS and SJF algorithms. The proposed algorithms improve resource optimization in terms of cost and time by facilitating enhanced LB mechanism through RL intelligence mechanism. The performance of RLFCFS and RLSJF LB algorithms is compared with respect to the average (avg.) load managed by the VMs and the avg. percentage (perc.) of deviation observed against the expected load in each experimental stage. The experimental throughput conveys that the RLFCFS LB algorithm managed an aggregate avg. load of 968.77 tasks against the RLSJF LB algorithm, which managed 999.08 tasks aggregately across all experimental stages. Concerning the avg. perc. of deviation, the RLFCFS LB algorithm deviated by 63.44% against the ideal expected load to manage against the RLSJF LB algorithm, which deviated by 64.60%. This shows that the RLFCFS LB algorithm gave better resource optimization results than the RLSJF LB algorithm. Lastly, these results are mathematically validated using the Simple Linear Regression model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Novel hybrid metamaterial to improve the performance of a beamforming antenna
This paper investigates the design and implementation of a novel hybrid metamaterial unit cell to improve a beamforming Wi-Fi antenna's performance. The proposed metamaterial unit cell is created on an FR-4 substrate (?? = 4.4) and a thickness of 1.6 mm. The metallization height of the unit cell is maintained at 0.035 mm. The designed metamaterial unit cell is simulated using HFSS Ver. 18.2 to verify the double negative behaviour. The unit cell consists of five Split Ring Resonators (SRR's) at the bottom and a hexagonal ring of six triangles. Initially, a conventional inset fed microstrip patch antenna is designed then an array of the proposed unit cell is created and used as a superstrate to study the performance. A Three Element Antenna Array (TEAA) is designed to operate at 2.4 GHz Wi-Fi band, and the superstrate created out of the proposed unit cell is used to study its effect. Metamaterial superstrate improved the conventional Single Element Antenna (SEA) gain by approximately 2 dB. Superstrate with TEAA exhibited an improved gain of 1 dB over TEAA. Published under licence by IOP Publishing Ltd. -
Novel magnetic nanocomposites and their environmental applications
Environmental contamination by numerous emerging pollutants including pharmaceuticals, microplastics, and pesticides residues is one of the greatest problems facing the world today. The release of these pollutants into the air, water, and soil causes serious threat to plants and animals. These contaminants enter the food chain through contaminated agricultural produce and animals, posing a threat to human health. Therefore, there is an urgent need to develop novel methods to detect, degrade, and remove toxic environmental pollutants. Recently, nanomaterials have been widely used in various applications as catalysts, sensors, and adsorbents due to their unique outstanding properties. This chapter, therefore, focuses on the recent application of magnetic nanoparticles and their respective nanocomposites as degradation catalysts, adsorbents, and electrochemical sensors for detection and removal of environmental pollutants. 2024 Elsevier Ltd. All rights reserved. -
Novel mammography images approach for breast cancer diagnosis using ensemble feature extraction
By using ensemble feature extraction methods to mammography pictures, this study introduces a novel strategy for the early detection of breast cancer. Beginning with preprocessing stages that use data augmentation to improve the dataset, the technique incorporates a methodical flowchart. Following the creation and individual training of an ensemble model that incorporates CNN architectures like as DenseNet, AlexNet, and i-Alex, the final model attains an impressive level of accuracy. Optimized feature vectors are the end result of a process that begins with feature fusion and continues with dimensionality reduction methods like principal component analysis (PCA). Utilizing LASSO and ReliefF for feature selection helps to refine the collection of features, which in turn improves accuracy metrics. Utilizing cross-validated hyperparameter optimization, classifier training showcases the effectiveness of SVM, Random Forest, and XGBoost. The ensemble method is clearly better according to the performance assessment, which takes into account sensitivity, specificity, F1-score, and AUC. Integrating the chosen classifier into a mammography screening system ensures clinical interpretability by providing clear visualizations. Updating the model with fresh data on a regular basis and doing continuous monitoring ensure that it remains accurate. By working together in the clinic and taking radiologists' comments into account, we can improve the system's performance and reveal its capabilities as a cutting-edge instrument for accurate breast cancer detection. 2025 Author(s). -
Novel Ovate Antenna for Wireless Communication: Characteristic Mode and Time Domain Analyses
In this article, a novel ovate-shaped microstrip antenna (OMSA) is presented for the application in wireless communication. It covers the evolution of a new shape and delves deeper into the resonance mechanism of the proposed design using characteristic mode analysis (CMA). The OMSA resonates at 2.45 GHz and 2.69 GHz with the return loss of ?18.82 dB and ?31.84 dB, respectively. It offers an ultra-wideband performance with 91.46% measured bandwidth. The characteristic impedance and VSWR at 2.4 GHz are 49 ? and 1.3, respectively. By introducing performance enhancement techniques such as ground truncation and a notch in the patch, the antenna resonance characteristics have been enhanced. A prototype of the proposed OMSA has been fabricated and validated experimentally. The time domain characteristics of the proposed OMSA have been simulated for both face-to-face (FtF) and side-by-side (SbS) configurations. The FtF configuration offers better performance, showcasing the group delay of the OMSA < 2 ns and minimal variation along the operating band. The phase linearity is also maintained, minimizing any distortions. The time domain results demonstrate a maximum fidelity factor of 90.62%, reaffirming the suitability of the antenna for wireless communication. The suitability of the proposed OMSA for wireless applications is also validated experimentally by analyzing the group delay and S21 phase linearity of the received signal. 2026, Electromagnetics Academy. All rights reserved. -
Novel PAPR Reduction in UFMC system for 5G Wireless Networks Using Precoding Algorithm
The Universal Filtered Multi-carrier (UFMC) system is promising alternative multicarrier modulation scheme for fifth generation (5G) cellular networks. UFMC systems offer many advantages such as larger spectral efficiency, robustness, lower latency and minimizing out of band emission. However, the most serious problem in the UFMC system is high peak to average power ratio (PAPR). This high peak signal is seriously harmed by the high power amplifier (HPA). Therefore, this research presents a novel Square Root raised Cosine function (SRC)-Precoding method introduced to reduction of PAPR. A performance analysis of various methods being examined upon in terms of CCDF of PAPR and the BER. The Simulation result shows that the proposed approach can effectively reduce the PAPR 6dB compared to standard UFMC. Moreover, the bit error rate (BER) study of the UFMC model indicates that the proposed approach significantly improves 15 dB compared with conventional UFMC systems. 2022 IEEE. -
Novel Pooling-Based VGG-Lite for Pneumonia and Covid-19 Detection From Imbalanced Chest X-Ray Datasets
This paper proposes a novel pooling-based VGG-Lite model in order to mitigate class imbalance issues in Chest X-Ray (CXR) datasets. Automatic Pneumonia detection from CXR images by deep learning model has emerged as a prominent and dynamic area of research, since the inception of the new Covid-19 variant in 2020. However, the standard Convolutional Neural Network (CNN) models encounter challenges associated with class imbalance, a prevalent issue found in many medical datasets. The innovations introduced in the proposed model architecture include: (I) A very lightweight CNN model, VGG-Lite, is proposed as a base model, inspired by VGG-16 and MobileNet-V2 architecture. (II) On top of this base model, we leverage an Edge Enhanced Module (EEM) through a parallel branch, consisting of a negative image layer, and a novel custom pooling layer 2Max-Min Pooling. This 2Max-Min Pooling layer is entirely novel in this investigation, providing more attention to edge components within pneumonia CXR images. Thus, it works as an efficient spatial attention module (SAM). We have implemented the proposed framework on two separate CXR datasets. The first dataset is obtained from a readily available source on the internet, and the second dataset is a more challenging CXR dataset, assembled by our research team from three different sources. Experimental results reveal that our proposed framework has outperformed pre-trained CNN models, and three recent trend existing models Vision Transformer, Pooling-based Vision Transformer (PiT) and PneuNet, by substantial margins on both datasets. The proposed framework VGG-Lite with EEM, has achieved a macro average of 95% accuracy, 97.1% precision, 96.1% recall, and 96.6% F1 score on the Pneumonia Imbalance CXR dataset, without employing any pre-processing technique. 2017 IEEE. -
Novel preemptive intelligent artificial intelligence-model for detecting inconsistency during software testing
The contribution of artificial intelligence (AI)-based modelling is highly significant in automating the software testing process; thereby enhancing the cost, resources, and productivity while performing testing. Review of existing AI-models towards software testing showcases yet an open-scope for further improvement as yet the conventional AI-model suffers from various challenges especially in perspective of test case generation. Therefore, the proposed scheme presents a novel preemptive intelligent computational framework that harnesses a unique ensembled AI-model for generating and executing highly precise and optimized test-cases resulting in an outcome of adversary or inconsistencies associated with test cases. The ensembled AI-model uses both unsupervised and supervised learning approaches on publicly available outlier dataset. The benchmarked outcome exhibits supervised learning-based AI-model to offer 21% of reduced error and 1.6% of reduced processing time in contrast to unsupervised scheme while performing software testing. 2025, Institute of Advanced Engineering and Science. All rights reserved.