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A review on ensembles-based approach to overcome class imbalance problem
Predictive analytics incorporate various statistical techniques from predictive modelling, machine learning and data mining to analyse large database for future prediction. Data mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. With the improvement in technology day by day large amount of data are collected in raw form and as a result necessity of using data mining techniques in various domains are increasing. Class imbalance is an open challenge problem in data mining and machine learning. It occurs due to imbalanced data set. A data set is considered as imbalanced when a data set contains number of instance in one class vastly outnumber the number of instances in other class. When traditional data mining algorithms trained with imbalanced data sets, it gives suboptimal classification model. Recently class imbalance problem have gain significance attention from data mining and machine learning researcher community due to its presence in many real world problem such as remote-sensing, pollution detection, risk management, fraud detection and medical diagnosis. Several methods have been proposed to overcome the problem of class imbalance problem. In this paper, our goal is to review various methods which are proposed to overcome the effect of imbalance data on classification learning algorithms. Springer Nature Singapore Pte Ltd 2019. -
A Comprehensive Methodical Strategy for Forecasting Anticipated Time of Delivery in OnlineFood Delivery Organizations
Determining the cost of shipping has long been a cornerstone of urban logistics, but today's effective outcomes need acceptable precision. Around the globe, internet-based meal ordering and distribution services have surpassed public expectations; for example, in India, platform-to-consumer distributions and delivery of food and drinks reached an astounding amount of more than 290 million transactions in 2023. Businesses are required to provide customers with precise details on the time it will take for their food to be delivered, starting from the moment the purchase is placed until it reaches the customer's door. Customers won't place orders if the result measure is greater than the actual delivery date, but a greater number of consumers are going to contact the customer service line if the period of waiting falls shorter than their actual shipment period. This study's primary goals are to identify critical variables that affect the availability of nutritious food inspiring leaders as well as to provide an approach for making accurate predictions. Analyzing and contrasting the primary effects and challenges of distribution and shipping in the nation's many different sectors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Forecasting the Stock Market Index Using Artificial Intelligence Techniques
If the stock market would have a predictable to maximum accuracy, then every stockbroker and investor would have been billionaire. But it is not the ground truth. In a one-to-one interaction with stock analysts, who mention that the stock market is unpredictable and that is why their role is important, else everything would have been black and white. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Importance of Artificial Intelligence (AI) and Robotic Process Automation (RPA) in the Banking Industry: A Study from an Indian Perspective
The papers primary focus is intelligence creation by AI and fast implementation by RPA. It starts with the applicability of the Turing test propounded by Alan Turing, the father of Artificial Intelligence (AI). At the backdrop lies various events, namely the recent motivation addition of various bank account holders. These factors fuelled the demand for AI and RPA implementation in the banking industry. It pitched how AI and RPA work in real-time scenarios such as financial fraud and money laundering. It discusses how AI builds the knowledge graph and recommends products and services for each customer. This knowledge is implemented and delivered using RPA. The AI application gained prominence in every banking business segment, such as equity, personal, investment and loan. The application of RPA is present in all business segments, although the percentage is increasing yearly. The AI and RPA can help banks to convert the challenges to opportunities. There have been various challenges, and the application of AI and RPA combinations is the key to solving the inefficiencies. Advanced analytical techniques on open-source data have been used in this paper. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Anomaly Detection Framework for Twitter Data
An anomaly indicates something unusual, related to detecting a sudden behavior change, and is also helpful in detecting irregular and malicious behavior. Anomaly detection identifies unusual events, suspicious objects, or observations that differ significantly from normal behavior or patterns. Discrepancies in data can be observed in different ways, such as outliers, standard deviation, and noise. Anomaly detection helps us understand the emergence of specific diseases based on health-related tweets. This paper aims to analyze tweets to detect the unusual emergence of healthcare-related tweets, especially pre-COVID-19 and during COVID-19. After pre-processing, this work collected more than 44 thousand tweets and performed topic modeling. Non-negative matrix factorization (NMF) and latent Dirichlet allocation (LDA) were deployed for topic modeling, and a query set was designed based on resultant topics. This query set was used for anomaly detection using a sentence transformer. K-means was also employed for clustering outlier tweets from the cleaned tweets based on similarity. Finally, an unusual cluster was selected to identify pandemic-like healthcare emergencies. Experimental results show that the proposed framework can detect a sudden rise of unusual tweets unrelated to regular tweets. The new framework was employed in two case studies for anomaly detection and performed with 78.57% and 70.19% accuracy. 2022 by the authors. -
Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis
Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individuals sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individuals tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed for selecting optimal text features and categorizing sentiment. The proposed method uses TextBlob and VADER for sentiment analysis, CountVectorizer, and term frequency-inverse document frequency (TF-IDF) vectorizer for feature extraction, followed by SPSO-based feature selection. The Covid-19 vaccination tweets dataset was created and used for training, validating, and testing. The proposed approach outperformed considered algorithms in terms of accuracy. Additionally, we augmented the newly created dataset to make it balanced to increase performance. A classical support vector machine (SVM) gives better accuracy for the augmented dataset without a feature selection algorithm. It shows that augmentation improves the overall accuracy of tweet analysis. After the augmentation performance of PSO and SPSO is improved by almost 7% and 5%, respectively, it is observed that simple SVM with 10-fold cross-validation significantly improved compared to the primary dataset. 2023 Tech Science Press. All rights reserved. -
Students Satisfaction with Remote Learning During the COVID-19 Pandemic: Insights for Policymakers
Purpose: This study aimed to learn more about the factors influencing student happiness and involvement in remote learning in Indian higher education institutions (HEIs). The study aims to assist administrators, strategists, and politicians in efficiently dealing with educations new normal. Methodology: The study used a quantitative research approach to fulfill the research aims. A sample of 546 students from various Indian HEIs was chosen, and data were gathered using standardized questionnaires. Structural equation modeling, confirmatory factor analysis, and importance-performance analysis (IPA) were used to calculate the student satisfaction index and examine the impact of various factors. Findings: The findings of this study revealed that institutional and faculty support emerged as the most influential factor impacting students satisfaction through remote learning. It also highlighted the need for HEIs to redesign the assessment process and evaluation techniques to adapt to the remote learning environment. Practical Implications: The findings of this study indicated the practical consequences for administrators, strategists, and policymakers at Indian HEIs. It was advised that improving institutional and teacher support should be a major concern in order to improve student happiness in remote learning situations. Furthermore, redesigning assessment procedures and evaluation processes may improve learning outcomes and student engagement. Originality: This study contributed to the existing body of knowledge by specifically investigating the factors influencing student satisfaction in remote learning within Indian HEIs. The findings shed light on the unique challenges and opportunities the shift to remote education presented. They offered valuable insights for managing and improving the quality of education during and beyond the pandemic. 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Pre Packaged Insolvency - Exploring An Alternative Framework For Bankruptcy Resolution In India
This article is a review of literatures on the need for alternative bankruptcy resolution framework in India. The study explores the context & background to the recent initiation of limited Pre-Packaged Insolvency in India. The article makes a strong case for having a private & pre-negotiated mode of debt resolution along with the existing CIRP framework in India. The article provides a comparative perspective of CIRP and Pre-pack driven resolution model in India. The research paper also addresses some of the potential challenges & concerns related to initiation of pre-pack in India & accordingly discusses the relevant safeguards for the same. Lastly, the study also provide a brief view of pre-pack model currently practised in USA. The Electrochemical Society -
The mental health climate crisis: Unveiling the hidden consequences of global warming
Today, the issue of climate change is a matter of great urgency on a global scale, with vast-reaching implications. While the environmental and physical health effects of climate changes are well-documented, its impact on mental health is an emerging area of concern. The issue of climate change necessitates a rigorous and systematic approach to comprehending, evaluating, and addressing climatic anxiety. This approach must underscore the differentiation between adaptive and maladaptive forms of anxiety, and the importance of considering the societal-level response necessary to effectively combat climate change. Studies have documented the impact of natural calamities like Hurricane Katrina on low-income people residing in those areas. The present study rests on the objectives of examining the psychological impact of climate change on individuals, communities, and vulnerable populations with the help of literature. Further, investigation is continued on the role of climate-related stressors, such as natural disasters, heatwaves, and environmental degradation on mental health outcomes. The study also identified the potential interventions and strategies for mitigating the adverse impact on mental health due to climate change. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Message from IEEE InC4 2024 Publication Chair
[No abstract available] -
Recent Advances in Analytical Techniques for Antidepressants Determination in Complex Biological Matrices: A Review
Depression is one of the most prevalent but severe of mental disorders, affecting thousands of individuals across the globe. Depression, in its most extreme form, may result in self-harm and an increased likelihood of suicide. Antidepressant drugs are first-line medications to treat mental disorders. Unfortunately, these medications are also prescribed for other in- and off-label conditions, such as deficit/hyperactivity disorders, attention disorders, migraine, smoking cessation, eating disorders, fibromyalgia, pain, and insomnia. This results in an increase in the use of antidepressant medications, leading to clinical and forensic overdose cases that could be either accidental or deliberate. The findings revealed that people who used antidepressants had a 33% greater chance of dying sooner than expected, compared to those who did not take the medications. Analytical techniques for precisely identifying and detecting antidepressants and their metabolic products in a variety of biological matrices are greatly needed to be developed and made available. Hence, this study attempts to discuss various analytical techniques used to identify and determine antidepressants in various biological matrices, which include urine, blood, oral fluid (saliva), and tissues, which are commonly encountered in clinical and forensic science laboratories. The Author(s) 2023. -
Skin cancer classification using machine learning for dermoscopy image
Skin cancer is highly ambiguous and difficult to identify and cure in the last stage. To increase the survival rate, it is important to recognize the stages of skin cancer for effective treatment. The main aim of the paper is to classify the various stages of skin cancer using dermoscopy images from the data repository of ISIC and PH2. The data is pre -processed with the help of median filter and wiener filter for removing the noise. Segmentation is processed using Watershed and Morphological. After the segmentation, features were extracted using Grey Level Co-occurrence Matrix (GLCM), Color, Geometrical shapes in order to improve the accuracy of dermoscopy image. Finally, the dataset is classified with some popular methods like KNN with 89%, Ensemble with 84% and SVM works better than the other two methods by giving the highest accuracy of 92%. BEIESP. -
Effect of food insecurity on the cognitive problems among elderly in India
Background: Food Insecurity (FI) is a crucial social determinant of health, independent of other socioeconomic factors, as inadequate food resources create a threat to physical and mental health especially among older person. The present study explores the associations between FI and cognitive ability among the aged population in India. Methods: To measure the cognitive functioning we have used two proxies, word recall and computational problem. Descriptive analysis and multivariable logistic regression was used to understand the prevalence of word recall and computational problem by food security and some selected sociodemographic parameters. All the results were reported at 95% confidence interval. Results: We have used the data from the first wave of longitudinal ageing study of India (LASI), with a sample of 31,464 older persons 60 years and above. The study identified that 17 and 5% of the older population in India experiencing computational and word recall problem, respectively. It was found that respondents from food secure households were 14% less likely to have word recall problems [AOR:0.86, 95% CI:0.310.98], and 55% likely to have computational problems [AOR:0.45, 95% CI:0.290.70]. We also found poor cognitive functioning among those experiencing disability, severe ADL, and IADL. Further, factors such as age, education, marital status, working status, health related factors were the major contributors to the cognitive functioning in older adults. Conclusion: This study suggest that food insecurity is associated with a lower level of cognition among the elderly in India, which highlight the need of food policy and interventional strategies to address food insecurity, especially among the individuals belonging to lower wealth quintiles. Furthermore, increasing the coverage of food distribution may also help to decrease the burden of disease for the at most risk population. Also, there is a need for specific programs and policies that improve the availability of nutritious food among elderly. 2021, The Author(s). -
Forensic toxicological and analytical aspects of carbamate poisoning A review
Pesticides play a pivotal role in modern agricultural practices and effective domestic pest control. Despite their advantages, pesticides pose a great danger to humans and animals due to their toxicity. Pesticides, particularly carbamates, are extensively used all over the world in crop protection and domestic pest control, however, also causing morbidity and mortality on a larger scale, which is of great significance in both clinical and criminal justice management.Carbamates are derived from a carbamic acid (NH2COOH) that are commonly used as insecticides. Ethienocarb, Sevin, Carbaryl, Fenoxycarb, Furadan, Carbofuran, Aldicarb, and 2-(1-Methylpropyl) phenyl N-methylcarbamate are examples of insecticides that include the carbamate functional group. By reversibly inactivating the enzyme acetylcholinesterase, these insecticides can induce cholinesterase inhibition poisoning.Chromatographic methods, notably gas and liquid chromatography have traditionally been employed to analyse carbamate pesticides and their metabolites in various matrices. These approaches are employed due to their ability to separate the chemicals contained in a sample; as well as identify and quantify these compounds utilizing advanced detection systems. Aside from these GC and LC conventional methods, other detection and/or hyphenated techniques such as single-quadrupole, ion-trap, triple-quadrupole, or tandem mass spectrometry, have been used in carbamate analysis to provide quick results with excellent sensitivity, precision, and accuracy.The objective of this review is to describe various analytical techniques used to detect and determine carbamate pesticides in various matrices which include urine, blood, and tissues that are commonly encountered in emergency hospital laboratories and forensic science laboratories. 2022 Elsevier Ltd and Faculty of Forensic and Legal Medicine -
Limelight in Dark Times: Jyoti Kumari's 'Cyclothon'
[No abstract available] -
Computational Intelligence for Solving Contemporary Problems
The special issue contains research papers elaborating advancements in computational intelligence. Computational intelligence mimics the extraordinary capacity of the human intellect to assert and understand in an environment of uncertainty and impre-cision. Computational intelligence is new-age multidisciplinary artificial intelligence. The main goal of computational intelligence is to develop intelligent systems to solve real-world problems that are not modelled or too hard to model mathematically. 2024, Bentham Science Publishers. All rights reserved.