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LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images
Mitosis detection, a crucial biomedical process, faces challenges like cell morphology variability, poor contrast, overcrowding, and limited annotated dataset availability. This research presents a novel method for mitosis detection in histopathological images highlighting two important contributions using a Bi-wolf optimization-based LP norm regularized deep Convolutional neural network (CNN) model. This hybrid optimization protocol is the key to the precise calibration of model parameters and effective training, which translates into optimal classifier performance. The results reveal that this model achieves high accuracy, sensitivity, and specificity values of 96.69%, 91.89%, and 97.74% respectively. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Assessment of artificial intelligence-based digital learning systems in higher education amid the pandemic using analytic hierarchy
The devastating effects of the 2020 worldwide COVID-19 virus epidemic prompted widespread lockdowns and restrictions, which will continue to be felt for decades. The repercussions of the pandemic have been most noticeable among educators and their students, which boosts the effectiveness of various AI-based learning systems in the education system. This study examines the AI-based digital learning platforms in higher education institutions based on various characteristics and uses of these systems. Several significant aspects of AI-based digital learning systems were obtained from the available literature, and significant articles were selected to properly examine various characteristics and functions of AI-based digital learning platforms used by multiple higher education institutions. The analytical hierarchy process (AHP) is employed to rank multiple AI-based learning systems based on key factors and their sub-factors. The studys outcome revealed which AI systems are effectively used in developing digital learning systems by various higher education institutions. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024. -
I Dont Play Games: Migrant Workers and Digital Media in Bengaluru
The great impact of media technologies in reordering almost every facet of modern life has been noted by theorists for over a century now, particularly since the idea of the global village imagined by media theorists, and enabled by globalisation and digital technology has become an inescapable reality. The new experience of time and space bears upon various dimensions of life, including the nature of work, the organisation of time and the place of leisure within these rhythms. This article attempts to engage with this very weighty body of scholarship in a modest way, through ethnographic research, to understand how mobile phones and internet technologies structure the experience of everyday life for low-income migrant workers in Bengaluru. The sites include a construction site and a hookah bar, and the study focuses on mobile gaming and the structuring of migrant social networks. 2024 South Asian University. -
Optical Resonator-Enhanced Random Lasing using Atomically Thin Aluminium-based Multicomponent Quasicrystals
Photon trapping inside a gain medium using a dispersed two-dimensional (2D) passive scatterer is an impetus to obtain incoherent random lasing (ic-RL) emission due to non-resonant feedback. An optical resonator (OR) can be used to influence such lasing thresholds. Non-noble nanomaterials-based quasicrystals (QCs) are an intriguing research prospect due to their potential surface plasmon resonance (SPR) property and ability to be exfoliated into 2D. In this work, an aluminium-based multicomponent alloy (Al70Co10Fe5Ni10Cu5) has been synthesized via the arc melting method. Thereafter, ultrasonication-based liquid phase exfoliation was used to obtain 2D quasicrystals (2D-QCs). The SPR-induced light scattering properties of synthesized 2D-QCs were exploited to obtain ic-RL from DCM dye gain medium under 532 nm, 10 ns, 10 Hz pulsed laser pumping. The plasmonic field enhancement property of 2D-QCs which enables the gain medium to absorb photons outside its peak absorption band has been demonstrated. The transition from ic-RL to OR-enhanced ic-RL and vice versa in the presence of resonator walls has been achieved by tweaking the device architecture. In this way, the ability of 2D-QCs to be potential passive scatterers and the controllability of lasing thresholds in the presence of an OR has been demonstrated. 2024 Elsevier Ltd -
Memorialisation and Identity in Mah India: Revealing French Colonial Legacies
Mah nestled in the Mahdistrict of the Puducherry Union Territory in India, holds profound historical ties to French colonial India. Unlike the broader Indian subcontinent, which witnessed fervent anti-colonial movements against British rule leading to political decolonisation in 1947, Mahexperienced a belated political awakening, reluctantly integrating into the Indian Union in 1954. Despite the withdrawal of the French, the enduring legacy of French colonial ideology and culture continued to shape the ethos of Mah In contemporary times, a significant presence of French nationals in India, particularly in Pondicherry, Karaikal, and Mah has fostered the evolution of a unique linguistic identity known as Indian French. Within Mah landmarks such as St. Teresas Shrine, the Statue of Marianne in Tagore Park at Cherukallayi, remnants of St. George Fort, and sculptures inspired by M. Mukundans novel On the Banks of the Mayyazhi stand as tangible vestiges of the erstwhile French presence. Serving as repositories of bygone French culture, these sites emerge as dynamic arenas of memory production. Notably, Tagore Park in Mah adorned with fictional documentation through sculptures, assumes a pivotal role as a space that harmonizes memory and history, functioning as a reservoir for collective memory concerning French colonial rule. Mah deliberate urban planning reflects a nuanced approach, embodying the concept of a living testament to French colonialism rather than a conventional museum. This architectural strategy underscores the deliberate preservation and commemoration of Mah historical past. Through interviews with French nationals residing in Mah this research explores how these landmarks have become pivotal in the production of memories and the construction of identities for the French community in India and Mah Leveraging Maurice Halbwachs theoretical framework, the study unveils the intricate interplay between collective memory and present-day identity formation, shedding light on the transformation of personal memory into historical memory and its subsequent amalgamation into collective memory. With close to 50 French families residing in and around Mahstill, the study involves interviews with ten families, focusing on landmarks like St. Teresas Shrine, the Statue of Marianne, the ruins of St. George Fort, and sculptures based on one of M. Mukundans novels. So, through interviews of the French citizens of Mah this paper highlights how the cultural artefacts and popular landmarks of Mahbecome sites of memory of the French colonisation. 2024, The International Academic Forum (IAFOR). All rights reserved. -
An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders
Mental health disorders are primarily life style driven disorders, which are mostly unidentifiable by clinical or direct observations, but act as a silent killer for the impacted individuals. Using machine learning (ML), the prediction of mental ailments has taken significant interest in medical informatics community especially when clinical indicators are not there. But, majority studies now focus on usual machine learning methods used to predict mental disorders with few organized health data, this may give wrong signals. To overcome the drawbacks of the conventional ML prediction models, this work presents Deep Learning (DL) trained prediction model for automated feature extraction to realistically predict mental health disorders from the online textual posts of individuals indi cating suicidal and depressive contents. The proposed model encompasses three phases named pre-processing, feature extraction and optimal prediction phase. The developed model utilizes a novel Sparse Auto-Encoder based Optimal Bi-LSTM (SAE-O-Bi-LSTM) model, which integrates Bi-LSTM and Adaptive Harris-Hawk Optimizer (AHHO) for extracting the most relevant mental illness indicating features from the textual content in the dataset. The dataset utilized for training consist of 232074 unique posts from the "SuicideWatch" and "Depression" subreddits of the Reddit platform during December 2009 to Jan 2021 downloaded from Kaggle. In-depth comparative analysis of the testing results is conducted using accuracy, precisions, F1 score, specificity, and Recall and ROC curve. The results depict considerable improvement for our developed approach with an accuracy of 98.8% and precision of 98.7% respectively, which supports the efficacy of our proposed model. The Author(s) 2024. -
Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem
Portfolio optimization has long been a challenging proposition and a widely studied topic in finance and management. It involves selecting and allocating the right assets according to the desired objectives. It has been found that this nonlinear constraint problem cannot be effectively solved using a traditional approach. This paper covers and compares quantum-inspired versions of four popular evolutionary techniques with three benchmark datasets. Genetic algorithm, differential evolution, particle swarm optimization, ant colony optimization, and their quantum-inspired incarnations are implemented, and the results are compared. Experiments have been carried out with more than 10 years of stock price data from NASDAQ, BSE, and Dow Jones. This work proposes several enhancements to allocate funds efficiently, such as improved crossover techniques and dynamic and adaptive selection of parameters. Furthermore, it is observed that the quantum-inspired techniques outperform the classical counterparts. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Forecasting the Volatility of Indian Forex Market: An Evidence from GARCH Model
Forecasting the volatility of forex market will create more trading opportunities to investors, despite of ups and downs in the forex market. The present study attempted to examine how the volatility in the exchange rate between Indian rupee and selected four foreign currencies, such as US dollar, euro, Japanese yen and British pound, can influence the market return. The data, used in the present study, covered the daily price observation of four foreign currencies, for a period of 5 years, from 2019-2023. The GARCH (1, 1) (generalized autoregressive conditional hetero skedasticity) was used for develop the model for foreign exchange (FX) rates volatility. Mean equation model confirmed that the series had attained stationary and previous price did influence the current price. It was also supported by co-efficient values in the variance equation. The co-efficient value, in the variance equation, was around one, which showed that the forex market was efficient. Further, it was validated that the volatility shocks in forex market were quite persistent. The active investors in the market may use this opportunity immediately. The policy maker may correct this deviation through timely intervention in the currency market. 2024, Iquz Galaxy Publisher. All rights reserved. -
Machine Learning in Financial Distress: A Scoping Review
Predicting financial distress is crucial for stakeholders, policymakers, governments, and management in decision-making processes. Researchers have developed various prediction models encompassing both traditional and machine-learning approaches. Notably, recent attention has shifted towards employing machine learning models to address the limitations of traditional methods. This study seeks to offer insights into current trends, identify gaps, and suggest future research directions using machine learning models for financial distress prediction, employing the PRISMA Extension for Scoping Reviews methodology. To achieve this, a comprehensive search was conducted across three databasesScience Direct, EBSCO, and ProQuestspanning from 2020 to 2023, identifying 34 relevant articles for analysis. The findings underscore the prevalent use of Support Vector Machine in financial distress prediction, followed by the Random Forest Classifier and Artificial Neural Network, with little attention paid to other models. Furthermore, the study underscores the necessity for more research in developing countries, noting the predominance of studies from developed nations. While machine learning models hold promise for enhancing the accuracy and efficiency of financial distress prediction, additional research is imperative to evaluate their effectiveness and applicability across diverse contexts. This scoping review aims to furnish researchers, policymakers, and institutions with valuable insights and policy recommendations, shedding light on underexplored machine-learning techniques. 2024, Iquz Galaxy Publisher. All rights reserved. -
Magnetohydro-convective instability in a saturated DarcyBrinkman medium with viscous dissipation
The influence of dissipation with viscosity on magnetohydro-convective instability in a saturated DarcyBrinkman medium is examined. The bottom boundary is designated as adiabatic, whereas the top boundary is isothermal. Numerical linear stability analysis investigates normal modes that disturb the horizontal base flow at different inclinations. The case study shows that the most unstable disturbances are horizontal rolls, normal modes characterized by a wave vector perpendicular to the main flow direction. The horizontal rolls are the favored instability mode. Barletta et al. also showed that horizontal rolls are more unstable than any other oblique roll mode in the hydromagnetic scenario. This finding provides insights into the behavior of MHD fluid flow and heat transfer in porous media, with implications for applications in geoscience, engineering, and environmental science. Graphical abstract: (Figure presented.) The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Experimental Investigation of Uniaxial Compressive Behavior of Composite Columns without and with Full and Partial CFRP Wraps
Concrete columns are the backbone of any major structure, and their strengthening, repair, and retrofit have always drawn special research attention. One of the techniques for strengthening and improving the ductility of concrete columns has been the application of carbon fiber-reinforced polymer (CFRP) materials. A total of 43 columns of different configurations were experimentally investigated to evaluate the uniaxial compressive behavior of composite columns. Experimental and international code-recommended load-carrying capacities, stress-strain relations, axial stiffness, ductility factor, and failure modes were examined in the study. When fully wrapped, the strength of both plain cement concrete and reinforced cement concrete columns improved by 21% each with reference to the unwrapped columns. In addition to providing the advantages of external confinement to the columns, full wrapping contributed to a strength increment of 21%, which compared well with the steel reinforcement contribution to a strength increment of 28% to 39%. The partial wrapping technique was found to be an economical alternative to the full wrapping technique, with strength enhancements of 6% to 12% in the case of both plain cement concrete and reinforced cement concrete partially wrapped columns. Two regression models for the load-carrying capacity for columns with and without wraps were developed with four key performance parameters: percentage steel reinforcement, percentage concrete, percentage carbon fiber-reinforced polymer wrap, and the weight of the specimen. The formulated models were validated and found to be robust and consistent with the results. 2024 American Society of Civil Engineers. -
Online cooperative learning: exploring perspectives of pre-service teachers after the pandemic
Mainly, research has explored pre-service teachers perspectives toward cooperative learning within face-to-face teaching. However, in a post-pandemic scenario, previous research has yet to effectively explore pre-service teachers (PSTs) perspectives toward online cooperative learning (OCL) in teacher education programs. So, recognizing the gap in the literature, this paper aims to explore the perspectives of PSTs towards OCL. The researchers employed a qualitative research design for the present study. The researchers conducted semi-structured interviews with 10 PSTs who underwent OCL during the pandemic. These PSTs may possess digital proficiency, virtual collaboration abilities, flexibility in evolving educational environments, and an enhanced understanding of online cooperative learning methodologies within modern education. Researchers employed a thematic analysis to analyze the qualitative data obtained. The various themes that emerged from the study are perceived benefits of OCL, challenges to OCL, technological proficiency, learning strategies and support, and building a supportive online learning community. Future researchers may contribute to advancing effective online learning practices by gaining a deeper understanding of pre-service teachers perspectives towards OCL through research on a larger scale, including various teacher education programs in various countries. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
A versatile sensor capable of ratiometric fluorescence detection of trace water and turn-on detection of Cu2+ modulating the binding interaction of a Cu(ii) complex with BSA and DNA complemented by docking studies
A fluorescent molecule, pyridine-coupled bis-anthracene (PBA), has been developed for the selective fluorescence turn-on detection of Cu2+. Interestingly, the ligand PBA also exhibited a red-shifted ratiometric fluorescence response in the presence of water. Thus, a ratiometric water sensor has been utilized as a selective fluorescence turn-on sensor for Cu2+, achieving a 10-fold enhancement in the fluorescence and quantum yield at 446 nm, with a lower detection limit of 0.358 ?M and a binding constant of 1.3 106 M?1. For practical applications, sensor PBA can be used to detect Cu2+ in various types of soils like clay soil, field soil and sand. The interaction of the PBA-Cu(ii) complex with transport proteins like bovine serum albumin (BSA) and ct-DNA has been investigated through fluorescence titration experiments. Additionally, the structural optimization of PBA and the PBA-Cu(ii) complex has been demonstrated by DFT, and the interaction of the PBA-Cu(ii) complex with BSA and ct-DNA has been analyzed using theoretical docking studies. 2024 The Royal Society of Chemistry. -
Dirty Tracks Across the Border: Global Operations of Extraction, Labour and Migration at a Railway Station on the BiharNepal Border
This article is based on an ethnography of the railway siding at Raxaul Junction railway station, a town on the BiharNepal border, which finds itself at the intersection of a massive logistical exercise by China in the form of the Belt Road Initiative, counter-logistical apparatus building by India and incremental hardening of an otherwise open border by Nepal. The article will analyse in detail the intricate network of the labour market that operates at and through the railway siding. It will also trace the origins of commodities used in the cement factories in the industrial corridor of Nepal that are extracted from some of the most deprived regions of India at great human and social costs. Finally, I will describe some of the latest exercises in logistical operations such as containerisation, opening of a new land port, the Integrated Check Post in Raxaul and operationalisation of a new dedicated freight corridor from Vishakhapatnam port to Raxaul, which is reconfiguring the logistical arrangements away from Kolkata and Haldia port and their implications on labour and labour practices. The Raxaul railway siding will be, hence, studied on multiple scales: global, national and local. The article will also try to understand the transformation of this very peculiar border town located on a unique border. This transformation is creating new labour processes, migratory processes and networks, and new modes of production of workers subjectivities and resistance along the global logistical apparatus and supply chains. It will also open up the possibilities of thinking conceptually about South Asian Border Systems. 2024 South Asian University. -
Hazard identification of endocrine-disrupting carcinogens (EDCs) in relation to cancers in humans
Endocrine disrupting chemicals or carcinogens have been known for decades for their endocrine signal disruption. Endocrine disrupting chemicals are a serious concern and they have been included in the top priority toxicants and persistent organic pollutants. Therefore, researchers have been working for a long time to understand their mechanisms of interaction in different human organs. Several reports are available about the carcinogen potential of these chemicals. The presented review is an endeavor to understand the hazard identification associated with endocrine disrupting carcinogens in relation to the human body. The paper discusses the major endocrine disrupting carcinogens and their potency for carcinogenesis. It discusses human exposure, route of entry, carcinogenicity and mechanisms. In addition, the paper discusses the research gaps and bottlenecks associated with the research. Moreover, it discusses the limitations associated with the analytical techniques for detection of endocrine disrupting carcinogens. 2024 Elsevier B.V. -
Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms
The prediction of malicious nodes in Internet of Things (IoT) networks is crucial for enhancing network security. Malicious nodes can significantly impact network performance across various scenarios. Machine learning (ML) classification algorithms provide binary outcomes ("yes" or "no") to accurately identify these nodes. This study implements various classifier algorithms to address the problem of malicious node classification, using the SensorNetGuard dataset. The dataset, comprising 10,000 records with 21 features, was preprocessed and used to train multiple ML models, including Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Performance evaluation of these models followed the ML workflow, utilizing Python libraries such as scikit-learn, Seaborn, Matplotlib, and Pandas. The results indicated that the Naive Bayes classifier outperformed others with an accuracy of 98.1%. This paper demonstrates the effectiveness of ML classifiers in detecting malicious nodes in IoT networks, providing a robust predictive model for real-time application. The SensorNetGuard dataset is available on the IEEE data port and Kaggle platform. 2024, Prof.Dr. ?skender AKKURT. All rights reserved. -
Neuropalliative Care Needs Checklist for Motor Neuron Disease and Parkinson's Disease: A Biopsychosocial Approach
Objectives: Neurodegenerative disorders necessitate comprehensive palliative care due to their progressive and irreversible nature. Limited studies have explored the comprehensive assessment needs of this population. This present study is designed to develop a checklist for evaluating the palliative care needs of individuals with motor neuron disease (MND) and Parkinson's disease (PD). Materials and Methods: The checklist was created through an extensive literature review and discussions with stakeholders in neuropalliative. Feedback from six field experts led to the finalisation of the checklist, which comprised 53 items addressing the unique biopsychosocial needs of MND and PD. Sixty patient-caregiver dyads receiving treatment in a tertiary referral care centre for neurology in south India completed the checklist. Results: People with MND had more identified needs with speech, swallowing, and communication, while people with PD reported needs in managing tremors, reduced movements, and subjective feelings of stiffness. People denying the severity of the illness was found to be a major psychosocial issue. The checklist addresses the dearth of specific tools for assessing palliative care needs in neurodegenerative disorders, particularly MND and PD. By incorporating disease-specific and generic items, the checklist offers a broad assessment of patients' multidimensional needs. Conclusion: This study contributes to the area of neuropalliative care by developing the neuropalliative care needs checklist (NPCNC) as a valuable tool for assessing the needs of individuals with neurodegenerative diseases. Future research should focus on refining and validating the NPCNC with larger and more diverse groups, applicability in different contexts, and investigating its sensitivity to changes over time. 2024 Published by Scientific Scholar on behalf of Indian Journal of Palliative Care. -
Quantum computational, solvation and in-silico biological studies of a potential anti-cancer thiophene derivative
Heterocyclic molecules display a wide spectrum of properties that span both material and biological domains. Material properties stem from their interactions in the bulk, where a large number of molecules of the same type get together resulting in an enhancement of properties. However, biological properties emanate from the interaction of a single or a few molecules with a biologically functional macromolecule. Computational tools offer a particularly useful way of theoretically studying molecules to arrive at a conclusion regarding such properties, even though they may vary when experimentally evaluated. This study concerns itself with the theoretical investigation comprising density functional theory calculations, topological analyses and in-silico biological evaluation of a thiophene compound, i.e. the title compound. Density functional theory was used to compute properties of the title molecule and their variations in unsolvated and solvated phases using Gaussian 09. The molecule in solvent phases encompassing organic polar protic, organic polar aprotic and inorganic polar protic nature have been subjected to theoretical investigations. The suitability of the molecule for deployment as a modern optical material is examined with positive results. Topological characteristics of the molecule were evaluated using Multiwfn 3.8 to examine electron density distribution and the possible resulting covalent, non-covalent and weak interactions because of such distribution. The potency of the molecule towards brain cancer was evaluated by molecular docking with Auto Dock Tools against two brain cancer protein targets 6ETJ and 6YPE with a good docking score of ?6.63 and ?6.21 kcal mol?1 respectively and the resulting interactions visualized and its pharmacokinetic properties obtained using online tools. 2024 Elsevier B.V. -
Enhancement of the Electrochemical behaviour of Carbon Black via a defect induced approach
In order to address the rising global concern of energy storage, carbon-based materials have established themselves due to their distinct features. Despite the demand for the fabrication of supercapacitors from natural, inexpensive carbonaceous materials is on the rise, the intrinsic disorders present in such materials hinder their performance, and hence, tuning these defects can aid in the improvement of their electrochemical performance. In this study, carbon black is introduced with defects in the form of oxygen functional groups via oxidation and thermal exfoliation and the impact on its electrochemical performance is studied. Careful tuning of the type of oxygen functional moieties at the basal plane of the carbon lattice is observed to be the contributing factor for the electrochemical behaviour. The distortion in the graphitic lattice caused by the epoxy and hydroxyl groups alters the specific surface area, porosity, and thermal stability, facilitating easier ion diffusion rates and enhanced faradaic reactions. The obtained specific capacitance of the thermally exfoliated carbon black is as high as 246.49 Fg?1 in a three-electrode system and 82.85 F/g in a two-electrode setup, owing to an energy density of 5.63 Whkg?1 and a power density of 189.75 Wkg?1. It has also exhibited excellent cyclic stability and capacitance retention up to 4000 cycles. The equivalent series resistance is found to decrease from 5.67 to 4.96 ? making the material conductive. As a result, the electrochemical properties of carbon black can be enhanced by tuning the oxygen functional groups, making it a promising supercapacitive material. Graphical Abstract: (Figure presented.). Qatar University and Springer Nature Switzerland AG 2024. -
Integrating machine learning techniques for Air Quality Index forecasting and insights from pollutant-meteorological dynamics in sustainable urban environments
Air pollution poses a significant environmental and health challenge in Delhi, India. This research focuses on predicting the Air Quality Index (AQI) for Delhi utilizing machine learning techniques. The research methodology encompasses comprehensive steps such as data collection, preprocessing, analysis, and modeling. Data comprising various pollutants and meteorological parameters were gathered from the Central Pollution Control Board (CPCB) spanning from January 1, 2016, to December 30, 2022. Missing values were imputed using the IterativeImputer method with RandomForestRegressor as the estimator. Data normalization and variance reduction were achieved through Box-Cox transformation. Spearman Rank Correlation analysis was employed to explore relationships between features and AQI. Initial evaluation of nine machine learning algorithms identified Random Forest and XGBoost as the top performers based on accuracy. These algorithms were further optimized using 5-fold cross-validation with RandomizedSearchCV. The results demonstrated the efficacy of both algorithms in AQI prediction. Notably, PM2.5 and CO concentrations emerged are most influential features, highlighting the potential for AQI improvement in Delhi through the reduction of these pollutants. This research distinguishes itself through a meticulous examination of the complex interconnections between pollutants and AQI, providing invaluable insights to inform targeted interventions and enduring policies geared towards improving air quality in Delhi. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.