Classification of Soil Images using Convolution Neural Networks
Convolution Neural Network; Soil classification; Supervised learning; Transfer learning
Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. 2021 IEEE.
Chatterjee K.; Obaidat M.S.; Samanta D.; Sadoun B.; Islam S.K.H.; Chatterjee R.
Proceedings of the 2021 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2021
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/CCCI52664.2021.9583192 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119292063&doi=10.1109%2fCCCI52664.2021.9583192&partnerID=40&md5=b3c81fb385d018bc22b556be08abcd19
Chatterjee K., CHRIST (Deemed to Be) University, Department of Computer Science, Karnataka, Bangalore, India; Obaidat M.S., University of Sharjah, College of Computing and Informatics, Sharjah, 27272, United Arab Emirates; Samanta D., CHRIST (Deemed to Be) University, Department of Computer Science, Karnataka, Bangalore, India; Sadoun B., Al-Balqa' Applied University, College of Engineering, Al Salt, Jordan; Islam S.K.H., Indian Institute of Information Technology Kalyani, Department of CSE, West Bengal, 741235, India; Chatterjee R., KIIT Deemed to Be University, School of Computer Engineering, Bhubaneswar, India
Classification of supply chain knowledge: A morphological approach
Epistemological classification; Knowledge classification; Knowledge management; Morphological analysis; Ontological classification; Supply chain knowledge
Purpose The purpose of the article is to create a knowledge classification model that can be used by knowledge management (KM) practitioners for establishing a knowledge management framework (KMF) in a supply chain (SC) network. Epistemological and ontological aspects of knowledge have been examined. SC networks provide a more generic setting for managing knowledge due to the additional issues concerning flow of knowledge across the boundaries of organizations. Design/methodology/approach Morphological analysis has been used to build the knowledge classification model. Morphological approach is particularly useful in exploratory research on concepts/ entities having multiple dimensions. Knowledge itself has been shown in literature to have many characteristics, and the methodology used has enabled a comprehensive classification scheme based on such characteristics. Findings A single comprehensive classification model for knowledge that exists in SC networks has been proposed. Nine characteristics, each possessing two or more value options, have been finally included in the model. Research limitations/implications Knowledge characteristics have been mostly derived from past research with the exception of three which have been introduced without empirical evidence. Although the article is primarily about SC knowledge, the results are fairly generic. Practical implications The proposed model would be of use in developing KM policies, procedures and establishing knowledge management systems in SC networks. The model will cater to both system and people aspects of a KMF. Originality/value The proposed knowledge classification model based on morphological analysis fills a gap in a vital area of research in KM as well as SC management. No similar classification model of knowledge with all its dimensions has been found in literature. Emerald Group Publishing Limited.
Journal of Knowledge Management, Vol-18, No. 4, pp. 812-823.
https://doi.org/10.1108/JKM-12-2013-0490 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927518827&doi=10.1108%2fJKM-12-2013-0490&partnerID=40&md5=d6b33172868a5103a0e50eb54519eea4
Sudhindra S., Institute of Management, Christ University, Bangalore, India; Ganesh L.S., Department of Management Studies, Indian Institute of Technology, Chennai, India; Arshinder K., Department of Management Studies, Indian Institute of Technology, Chennai, India
Classification of Vehicle Make Based on Geometric Features and Appearance-Based Attributes Under Complex Background
Auto correlogram; Color moments; Texture extraction
Vehicle detection and recognition is an important task in the area of advanced infrastructure and movement administration. Many researchers are working on this area with different approaches to solve the problem since it has a many challenge. Every vehicle has its on own unique features for recognition. This paper focus on identifying the vehicle brand based on its geometrical features and diverse appearance-based attributes like colour, occlusion, shadow and illumination. These attributes will make the problem very challenging. In the proposed work, system will be trained with different samples of vehicles belongs to the different make. Classify those samples into different classes of models belongs to same make using Neural Network Classifier. Exploratory outcomes display promising possibilities efficiently. 2019, Springer Nature Singapore Pte Ltd.
Arunkumar K.L.; Danti A.; Manjunatha H.T.
Communications in Computer and Information Science, Vol-1035, pp. 41-48.
Arunkumar K.L., Department of MCA, Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India; Danti A., Computer Science and Engineering, Christ University, Bangalore, Karnataka, India; Manjunatha H.T., Department of MCA, Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India
Classification of Vehicle Type on Indian Road Scene Based on Deep Learning
Deep learning; Faster R-CNN; Intelligent traffic system (ITS); Region Proposal Network (RPN)
In Recent days an intelligent traffic system [ITS] is implemented on indian traffic sytem. Different applications are widely used to improvies the performance of the system. To improve the intelligence of the system deep learning can used to classify the vehicles into three different classes. The combination of Faster RCNN classifier and RPN can used to detect the objects and classify those objects into different classes. Analysis of the experimental results shows the improved accuracy and efficiency in classifying the vehicles on indian roads into different categories. 2021, Springer Nature Singapore Pte Ltd.
Arunkumar K.L.; Danti A.; Manjunatha H.T.; Rohith D.
Communications in Computer and Information Science, Vol-1380, pp. 183-192.
Springer Science and Business Media Deutschland GmbH
Arunkumar K.L., Department of MCA, Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India; Danti A., Computer Science and Engineering, CHRIST (Deemed to be University), Bengalore, Karnataka, India; Manjunatha H.T., Department of MCA, Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India; Rohith D., NMIT, Banglore, Karnataka, India
Classification of Vitiligo using CNN Autoencoder
Auto Encoder; Convolutional Neural Network; Deep Learning; Skin disease; Vitiligo
Precise recognition of skin ailment is a time-consuming procedure even for Professionals. With the invention of deep learning and medical image processing, the identification of skin disease is possible in a time-efficient manner and accurately. Autoencoder is the generative algorithm but in the proposed work it is used as a generator and as well as a classifier. In this work, a Convolutional (CNN) autoencoder was used to classify the skin disease Vitiligo. In this work encoding and decoding layers were used but in the last layer in place of reproducing the original image, the classification layer was used to classify the image. The proposed work gave training accuracy of 87.71 % whereas validation accuracy was 90.16%. 2022 IEEE.
Proceedings - International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022, pp. 174-176.
https://doi.org/10.1109/ICAAIC53929.2022.9792650 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133469629&doi=10.1109%2fICAAIC53929.2022.9792650&partnerID=40&md5=a68ad5de4e289757ddd0d7d0fe6bcd26
Agrawal N., Christ Deemed to Be University, Department of Computer Science, Bengaluru, India; Aurelia S., Christ Deemed to Be University, Department of Computer Science, Bengaluru, India
Classification on Alzheimers Disease MRI Images with VGG-16 and VGG-19
Alzheimers disease (AD); Cognitively normal (CN); Mild cognitive impairment (MCI); Visual geometry group (VGG) and convolutional neural networks (CNN)
Balancing thoughts and memories of our life is indeed the most critical part of the human brain.Thus, its stability and sustenance are also important for smooth functioning.The changes in the structure can lead to disorders such as dementia and one such type of condition is known as Alzheimers disease.Multi modal neuroimaging like magnetic resonance imaging (MRI) and positron emission tomography (PET) is used for the early diagnosis of Alzheimers disease (AD) by providing complementary information.Different modalities like PET and MRI data were acquired from the same subject, there exists markable materiality between MRI and PET data.Mild cognitive impairment (MCI) is the initial stage with few symptoms of AD.To recognise the subjects which are capable of converting from MCI to AD is to be analysed for further treatments.In this research, specific convolutional neural networks (CNN) which are designed for classifications like VGG-16 and VGG-19 deep learning architectures were used to check the accuracy of cognitively normal (CN) versus MCI, CN versus AD and MCI to AD conversion using MRI data.The proposed research is analysed and tested using MRI data from Alzheimers disease neuroimaging initiative (ADNI). 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Smart Innovation, Systems and Technologies, Vol-312, pp. 199-207.
Antony F., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bangalore, India; Anita H.B., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bangalore, India; George J.A., Department of Life Sciences, Christ (Deemed to be University), Karnataka, Bangalore, India
Classification, source, and effect of environmental pollutants and their biodegradation
Bioremediation; Environmental pollutants; Photoremediation; Xenobiotic biotransformation
Any foreign chemical substance that is unusually present within an organism or is unexpectedly found in the environment at a higher concentration than the permissible limits can be termed a xenobiotic or a pollutant. Such substances include carcinogens, drugs, food additives, hydrocarbons, dioxins, polychlorinated biphenyls, pesticides or even some natural compounds. Pollutants are known for their higher persistence and pervasiveness, and along with their transformed products, they can remain in and interact with the environment for prolonged periods. In this article, the classification of such substances based on their nature, use, physical state, pathophysiological effects, and sources is discussed. The effects of pollutants on the environment, their biotransformation in terms of bioaccumulation, and the different types of remediation such as in situ and ex situ remediation, are also presented. 2017 Begell House, Inc.
Mathew B.B.; Singh H.; Biju V.G.; Krishnamurthy N.B.
Journal of Environmental Pathology, Toxicology and Oncology, Vol-36, No. 1, pp. 55-71.
ISSN: 7318898; PubMed ID: 28605331; CODEN: JEPOE
https://doi.org/10.1615/JEnvironPatholToxicolOncol.2017015804 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017002671&doi=10.1615%2fJEnvironPatholToxicolOncol.2017015804&partnerID=40&md5=1bab535a949718a03ec62b467a86a340
Mathew B.B., Department of Biotechnology, Sapthagiri College of Engineering, Bengaluru, 560057, Karnataka, India; Singh H., Department of Biotechnology, Sapthagiri College of Engineering, Bengaluru, 560057, Karnataka, India; Biju V.G., CUFE, Christ University, Bengaluru, 560060, Karnataka, India; Krishnamurthy N.B., Department of Biotechnology, Sapthagiri College of Engineering, Bengaluru, 560057, Karnataka, India
Classifying AI-generated summaries And Human Summaries Based on Statistical Features
AI-generated summaries; and feature-based techniques; classification models; Grid Search technique; Logistic Regression; NLG classification; Random Forest; SVM
In an age where artificial intelligence knows no bounds, it's crucial to know if the textual content is reliable. But, the task of identifying AI-generated content within vast volumes of textual data is a big challenge. The existing studies in feature-based classification only explored prompt-based text responses. This paper explores methods to identify AI-generated summaries using feature-based machine-learning techniques. This study uses the BBC News Summary dataset. The summaries for the dataset are then generated using three of the top-performing summarisation models. Different statistical features like Zipf's Law Score, Flesch Reading Ease Score, and the Gunning Fog Index are used for extracting features for the classification model. The aim is to differentiate AI-generated summaries from human-written summaries. The main part of the study involves extracting the statistical features from the summarized texts, which are then classified using different classification models. Different models like Support Vector Machine (SVM), Random Forest, Decision Tree, and Logistic Regression models are used in the paper. Grid Search is also used to fine-tune SVM for the best results. The right model depends on what the need is. Whether it's accuracy, F1 score, or a mix of both, there are different options to lead you to the truth. The feature-based approach in this paper helps in more explainable classification and can compare how statistical text features are different for human-written summaries and generated summaries. 2024 IEEE.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
https://doi.org/10.1109/TQCEBT59414.2024.10545131 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204496824&doi=10.1109%2fTQCEBT59414.2024.10545131&partnerID=40&md5=ba500df76cffd573acd9a2bb1821b969
Mathews D., Department of Data Science, Christ (Deemed to Be University), Lavasa, India; Varghese J.P., Department of Data Science, Christ (Deemed to Be University), Lavasa, India; Samuel L.C., Department of Data Science, Christ (Deemed to Be University), Lavasa, India
Classifying bipolar personality disorder (bpd) using long short-term memory (lstm)
AUC; Borderline personality disorder (BPD); Gated recurrent unit (GRU); Long short-term memory (LSTM); Recurrent neural network (RNN)
With the advancement in technology, we are offered new opportunities for long-term monitoring of health conditions. There are a tremendous amount of opportunities in psychiatry where the diagnosis relies on the historical data of patients as well as the states of mood that increase the complexity of distinguishing between bipolar disorder and borderline disorder during diagnosis. This paper is inspired by prior work where the symptoms were treated as a time series phenomenon to classify disorders. This paper introduces a signature-based machine learning model to extract unique temporal pattern that can be attributed as a specific disorder. This model uses sequential nature of data as one of the key features to identify the disorder. The cases of borderline disorder that are either passed down genetically from parents or stem from exposure to intense stress and fear during childhood are discussed in this study. The model is tested with the synthetic signature dataset provided by the Alan Turing Institute in signature-psychiatry repository. The end result has 0.95 AUC which is an improvement over the last result of 0.90 AUC. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.
Ghosh S.; Pahuja K.; Jiji J.M.; Puthussery A.; Shukla S.; Unal A.
Lecture Notes in Networks and Systems, Vol-132, pp. 169-176.
Ghosh S., CHRIST(Deemed to be University), Bengaluru, India; Pahuja K., CHRIST(Deemed to be University), Bengaluru, India; Jiji J.M., CHRIST(Deemed to be University), Bengaluru, India; Puthussery A., CHRIST(Deemed to be University), Bengaluru, India; Shukla S., CHRIST(Deemed to be University), Bengaluru, India; Unal A., Digital Monozukuri, Stanford, United States
Classifying voice-based customer query using machine learning technique
Feature extraction; Stemming; Tokenization; Vectorization; Voice recognition
Timely attention to issues raised by customers is critical. It is imperative that the average handling time is lesser, which in turn contributes to productivity. It was found from the data from the banking industry in the US that, on average, a customer service call last for seven minutes. The first two minutes are for the call to get redirected to the respective team. This study investigates a method using machine learning to classify and redirect the customers into the respective department directly based on their initial voice response or voice message. It will substantially reduce the service time. CRISP-DM methodology is being used to design the process of the study. The most frequently occurring issues and the department to which they are associated are created through machine learning from the dataset that contained product reviews and metadata of different issues. The programming languages that are used in this study are Python, HTML and Java. An interface is created by using HTML, which makes it quite user-friendly. The study tests the effectiveness of converting voice to text and interprets which department the call should be transferred to address the issue. A support vector machine and a logistic regression model were used for the prediction, and it was found that the models provided an accuracy of 83 and 84 percent, respectively. The study proves that using ML and voice recognition reduces the average handling time. 2021 Ecological Society of India. All rights reserved.
Kapse M.; Elangovan N.; Varghese A.; Joy A.; Tom C.
Indian Journal of Ecology, Vol-48, pp. 69-72.
Kapse M., School of Business and Management, Christ (Deemed to be University, Bengaluru, 560 029, India; Elangovan N., School of Business and Management, Christ (Deemed to be University, Bengaluru, 560 029, India; Varghese A., School of Business and Management, Christ (Deemed to be University, Bengaluru, 560 029, India; Joy A., St Joseph's College of Arts and Science (Autonomous), Bengaluru, 660 027, India; Tom C., St Joseph's College of Arts and Science (Autonomous), Bengaluru, 660 027, India
Classroom mathematics learning: Association of joy of learning and school connectedness among high school students in India
India; mathematics anxiety; mediation analysis; school connectedness; students
Mathematics learning experiences can influence the overall academic and socio-emotional development of a child. The present study investigates the mediating effect of mathematics anxiety and emotional engagement on the relationships between teacherstudent interaction, the joy of learning, and school connectedness. Two mediation models were tested for the dependent variables: the joy of learning and school connectedness, using Hayes' process macro in SPSS on a sample of 774 eighth-standard students from Indian schools. The study's results indicate the presence of a serial mediation effect on the relationship between teacherstudent interaction and joy of learning, teacherstudent interaction, and school connectedness through mathematics anxiety and emotional engagement. The study emphasized the role of mathematics learning within the overall framework of joy of learning and school connectedness.. 2024 Wiley Periodicals LLC.
Psychology in the Schools, Vol-61, No. 8, pp. 3087-3101.
https://doi.org/10.1002/pits.23207 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191199624&doi=10.1002%2fpits.23207&partnerID=40&md5=15e159e2fe5c7e9ef9d270cc74379e5a
Sasidharan S., School of Education, Christ University, Bengaluru, India; Kareem J., School of Education, Christ University, Bengaluru, India
Clay soil stabilization using MICP techniques by inducing microbes and bacteria in treating it /
Patent Number: 202241029830, Applicant: Dr. Periyasamy Thirunavukkarasu.Clay Soil Stabilization using MICP Technique by Inducing Microbes and Bacteria in Treating it Abstract: In general, clay soil is an expansive and fragile soil, which means that it requires improvement before it can be helpful to withstand structure. Only after this has been done will it be beneficial to endure structure. In order to improve the quality of clay soil, the authors of this study utilised Bacillus subtilis and Bacillus megaterium. Clay should have its qualities improved by the use of the MICP approach, which involves the use of microorganisms. During the bio-b augmentation process, the cementation reagent and bacteria were combined with the clay soil in varying amounts.
https://drive.google.com/file/d/1dOPBrRjYXHl4I7F5t35IXIi4yiRUF9Zv/view
Clay-based cementitious nanofluid flow subjected to Newtonian heating
clay nanoparticles; Constant proportional Caputo fractional derivative; Newtonian heating; slippage conditions; Zakian's algorithms
In recent years, a novel technique for producing robust cementitious materials, called nanocomposites, has emerged. These materials are comprised of clay minerals and polymers. As a result, a vertical flat plate has been used to evaluate a clay-based cementitious nanofluid in this research. The impacts of first-order chemical reactions, heat generation/heat absorption, and the Jeffrey fluid model are taken into account for the study of flow. Newtonian heating and the conditions for slippage velocity have also been considered. The mathematical problem for the flow analysis has been established in relations of partially coupled partial differential equations and the model has been generalized using constant proportional Caputo (CPC) fractional derivative. The problem is solved using the Laplace transform technique to provide precise analytical solutions. On the concentration, temperature, and velocity fields, the physics of a number of crucial flow parameters have been examined graphically. The acquired results have been condensed to a very well-known published work to verify the validity of the current work. It is important to note here that the rate of heat transfer in the fluid decreases by 10.17% by adding clay nanoparticles, while the rate of mass transfer decrease by 1.31% when the value of ? reaches 0.04. 2023 World Scientific Publishing Company.
Anwar M.S.; Hussain M.; Hussain Z.; Puneeth V.; Irfan M.
International Journal of Modern Physics B, Vol-37, No. 14
https://doi.org/10.1142/S0217979223501400 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143688060&doi=10.1142%2fS0217979223501400&partnerID=40&md5=4b5d4cac148fe1bfee58e4599f0f1ca9
Anwar M.S., Department of Mathematics, University of Jhang, Jhang, 35200, Pakistan; Hussain M., Department of Natural Sciences and Humanities, University of Engineering and Technology, Lahore, 54890, Pakistan; Hussain Z., Department of Mathematics, University of Baltistan, Skardu, Pakistan; Puneeth V., Department of Computational Science, Christ (Deemed to Be University), Ghaziabad, 201003, India; Irfan M., Department of Mathematical Sciences, Federal Urdu University of Arts Sciences & Technology, Islamabad, 44000, Pakistan
Click & Collect Retailing: A Study on Its Influence on the Purchase Intention of Customers
Integration; Omnichannel; Online shopping behaviour; Perceived usefulness; Retail
The retail sector, over the years, has evolved dramatically to provide better service to its customers. With the superior convenience of online shopping and tangible experience of in-store shopping, retail industries are looking forward to integrating both modes, thus embracing omni channel to provide better service to their customers. The prime objective of the research is to investigate the level of influence that using the Click & Collect online shopping mode can have on customer purchase intention and to ascertain the effects that online and offline shopping attributes have on this intention. The study emphasizes the usefulness of integrating both the shopping modes, thus embracing omni channel in the retail sector to provide a better shopping experience to the customers. The primary data were collected from 356 respondents. Secondary data were collected by reviewing articles, research papers, extant studies and newspaper articles. In the analysis, the buying behaviour through an e-commerce platform and customers purchase intentions are taken as the dependent variable. Product risk, online trust, website quality, offline experience and perceived usefulness are identified as the independent variables. The data thus collected were processed for regression tests using IBM SPSS 25 software to analyse the results. The Stimulus-Organism-Response model was deployed as the proposed model for the research. The results obtained from the research will allow retailers to understand the customer's buying behaviour towards the new Click & Collect system better by identifying the key variables that influence their purchase intention. The current study highlights the influence of the perceived usefulness of using the Click & Collect online shopping mode on the purchase intention of customers. 2021 Transnational Press London
Transnational Marketing Journal, Vol-9, No. 2, pp. 239-251.
https://doi.org/10.33182/tmj.v9i2.1561 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140289184&doi=10.33182%2ftmj.v9i2.1561&partnerID=40&md5=af4888ab3bac0aba52a7d35acc0759d7
Karthik S., School of Business and Management, Christ University, Bangalore, India; Gowda K.R., School of Business and Management, Christ University, Bangalore, India; Banerjee J., Christ University, Bangalore, India
Climate anxiety, wellbeing and pro-environmental action: correlates of negative emotional responses to climate change in 32 countries
Climate activism; Climate change; Climate change anxiety; Emotions; Pro-environmental behaviour; Wellbeing
This study explored the correlates of climate anxiety in a diverse range of national contexts. We analysed cross-sectional data gathered in 32 countries (N = 12,246). Our results show that climate anxiety is positively related to rate of exposure to information about climate change impacts, the amount of attention people pay to climate change information, and perceived descriptive norms about emotional responding to climate change. Climate anxiety was also positively linked to pro-environmental behaviours and negatively linked to mental wellbeing. Notably, climate anxiety had a significant inverse association with mental wellbeing in 31 out of 32 countries. In contrast, it had a significant association with pro-environmental behaviour in 24 countries, and with environmental activism in 12 countries. Our findings highlight contextual boundaries to engagement in environmental action as an antidote to climate anxiety, and the broad international significance of considering negative climate-related emotions as a plausible threat to wellbeing. 2022 The Authors
Ogunbode C.A.; Doran R.; Hanss D.; Ojala M.; Salmela-Aro K.; van den Broek K.L.; Bhullar N.; Aquino S.D.; Marot T.; Schermer J.A.; Wlodarczyk A.; Lu S.; Jiang F.; Maran D.A.; Yadav R.; Ardi R.; Chegeni R.; Ghanbarian E.; Zand S.; Najafi R.; Park J.; Tsubakita T.; Tan C.-S.; Chukwuorji J.C.; Ojewumi K.A.; Tahir H.; Albzour M.; Reyes M.E.S.; Lins S.; Enea V.; Volkodav T.; Sollar T.; Navarro-Carrillo G.; Torres-Mar J.; Mbungu W.; Ayanian A.H.; Ghorayeb J.; Onyutha C.; Lomas M.J.; Helmy M.; Martez-Buelvas L.; Bayad A.; Karasu M.
Journal of Environmental Psychology, Vol-84
All Open Access; Green Open Access; Hybrid Gold Open Access
https://doi.org/10.1016/j.jenvp.2022.101887 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142198148&doi=10.1016%2fj.jenvp.2022.101887&partnerID=40&md5=ab6eb9f31eaddf74f5ac5e6b1f574298
Ogunbode C.A., School of Psychology, University of Nottingham, United Kingdom; Doran R., Department of Psychosocial Science, Faculty of Psychology, University of Bergen, Norway; Hanss D., Department of Social Sciences, Darmstadt University of Applied Sciences, Germany; Ojala M., School of Law, Psychology and Social Work, ebro University, Sweden; Salmela-Aro K., Department of Educational Sciences, University of Helsinki, Finland; van den Broek K.L., Copernicus Institute of Sustainable Development, Utrecht University, Netherlands, Research Centre for Environmental Economics, Heidelberg University, Germany; Bhullar N., School of Psychology, University of New England, NSW, Australia; Aquino S.D., Psychology Department, Pontifical Catholic University of Rio de Janeiro, Brazil; Marot T., Psychology Department, Pontifical Catholic University of Rio de Janeiro, Brazil; Schermer J.A., Department of Management and Organisational Studies, Faculty of Social Science, University of Western Ontario, Canada; Wlodarczyk A., Escuela de Psicolog, Facultad de Humanidades, Universidad Catica del Norte, Chile; Lu S., School of Applied Social Sciences, De Montfort University, United Kingdom; Jiang F., Department of Human Resources and Organisational Behaviour, University of Greenwich, United Kingdom; Maran D.A., Department of Psychology, University of Torino, Italy; Yadav R., School of Business and Management, Christ University, India; Ardi R., Department of Psychology, Universitas Airlangga, Indonesia; Chegeni R., Department of Psychosocial Science, Faculty of Psychology, University of Bergen, Norway; Ghanbarian E., Department of Educational Psychology and Counselling, University of Tehran, Iran; Zand S., Department of Psychology, University of Milano-Bicocca, Italy; Najafi R., Department of General Psychology, University of Padova, Italy; Park J., Nagoya University of Commerce and Business, Japan; Tsubakita T., Nagoya University of Commerce and Business, Japan; Tan C.-S., Department of Psychology and Counselling, Faculty of Arts and Social Science, Universiti Tunku Abdul Rahman, Malaysia; Chukwuorji J.C., Department of Psychology, University of Nigeria, Nigeria; Ojewumi K.A., Department of Psychology, Obafemi Awolowo University, Nigeria; Tahir H., Department of Psychosocial Science, Faculty of Psychology, University of Bergen, Norway; Albzour M., Department of Social and Behavioural Science, Birzeit University, Palestine; Reyes M.E.S., Department of Psychology, College of Science, University of Santo Tomas, Philippines; Lins S., Faculty of Psychology and Education Sciences, University of Porto, Portugal; Enea V., Department of Psychology, Alexandru Ioan Cuza University of Iasi, Romania; Volkodav T., Faculty of Pedagogy, Psychology and Communicology, Kuban State University, Russian Federation; Sollar T., Department of Psychological Sciences, Faculty of Social Sciences and Healthcare, Constantine the Philosopher University in Nitra, Slovakia; Navarro-Carrillo G., Department of Psychology, University of Ja, Spain; Torres-Mar J., Department of Research Methods in Behavioural Sciences, University of Granada, Spain; Mbungu W., Department of Civil and Water Resources Engineering, School of Engineering and Technology, Sokoine University of Agriculture, Tanzania; Ayanian A.H., Institute for Interdisciplinary Research on Conflict and Violence, Bielefeld University, Germany; Ghorayeb J., Department of Psychology, Zayed University, Dubai, United Arab Emirates; Onyutha C., Department of Civil and Environmental Engineering, Kyambogo University, Uganda; Lomas M.J., School of Health and Society, University of Salford, United Kingdom; Helmy M., Department of Psychology, Sultan Qaboos University, Oman, Psychology Department, Faculty of Arts, Menuofia University, Egypt; Martez-Buelvas L., Universidad Tecnolica de Bolar, Cartagena, Colombia; Bayad A., Institute for Interdisciplinary Research on Conflict and Violence, Bielefeld University, Germany, Van YcYil University, Turkey; Karasu M., Institute for Interdisciplinary Research on Conflict and Violence, Bielefeld University, Germany
Climate Change Adaptation Strategies for Achieving Net-Zero Economy
Carbon emissions; Carbon neutrality; CC adaptations; Collective knowledge; Developing economies; Environment management system; Global warming; Net zero; Sustainable development
Today, net zero economy is garnering lot of interest as climate change concerns have become one of the most pressing issues for the organizations. The negative impact of climate change (CC) could be witnessed across all industries. The direct risk (i.e. impairment cost, damages, forced closure from extreme weather events) and indirect risk (i.e. disruption in the business value chain, loss of infrastructure, etc.) emanating from CC has severely impacted the business model of the companies. It is important for companies to address climate challenges in their core business model and take climate action for achieving net zero economy. The aim of this study is to explore the impact of various organizational factors on the climate change adaptation strategies (CCAS) of manufacturing companies in India. The data was collected from 241 respondents and structural equation modelling (SEM) through Smart PLS 3.0 was employed for analysis in the study. Results indicated that corporate knowledge, processes, objectives, financial resources, collective knowledge, and incentives significantly influence the CCAS for the companies. The findings provide valuable input to the managers, practitioners, and other stakeholders interested in promoting climate actions and achieving a net zero economy. This chapter contributes to the extant literature in the field of corporate CC strategies and actions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Kumar K.; Singh R.; Sharma A.; Lehri B.P.S.
Approaches to Global Sustainability, Markets, and Governance, Vol-2024, pp. 163-183.
Kumar K., School of Management, Bennett University, Greater Noida, India; Singh R., Institute of Business Management, GLA University, Uttar Pradesh, Mathura, India, Department of Management Studies, Kumaun University, Uttarakhand, Nainital, India; Sharma A., Christ University, Bengaluru, India; Lehri B.P.S., University School of Business, Chandigarh University, Mohali, India
Climate change and water insecurity: Who bears the brunt? (A case of Yelenahalli Village, Bengaluru)
BWSSB piped water; Groundwater; Intermittent supply; Price discrimination; Private water tankers; Water security
Rapid urbanisation and neglect towards traditional water bodies have aggravated water security issues in isolated pockets. In the recent past, they have also manifested into natural disasters in major urban centres such as Bengaluru (Bangalore), Mumbai, Chennai and Hyderabad. An examination of the problem is essential to achieve the Sustainable Development Goal on water security laid down by the United Nations in the context of climate change. This paper explores the various socio-economic costs of water scarcity through a case study of Yelenahalli village in Bengaluru. The study's preliminary results find that the encroachment of water bodies has led to a significant cost to households and local governments. Water scarcity is found to have a differential impact on poor and non-poor households in terms of access and the resultant water consumption due to the prevalence of price discrimination in the private water markets. The disparity in access to water is exacerbated despite the government's commencement of piped water supply due to worsening climate conditions and falling groundwater levels. The Author(s), 2023. All rights reserved.
The Palgrave Handbook of Socio-Ecological Resilience in the Face of Climate Change: Contexts from a Developing Country, pp. 429-446.
Srisha A., Bocconi University, Milan, Italy; Yogeshwari S., Christ University, Yeswanthpur Campus, Bengaluru, India
Climate Change Impact on Water Resources, Food Production and Agricultural Practices
Agriculture; Climate change; Food security; Hydrological cycle; Water resources management
The greatest threat to human health that exists today is climate change. Ecosystems, societies and biodiversity are seriously at risk from the long term effects due to change in climate, primarily brought on by human activities. Rising temperatures increase evaporation, which causes drought and decreases water availability for ecosystems, drinking water supplies and agriculture. Changed precipitation patterns exacerbate floods, storms and sea levels, contaminating the water supply and harming infrastructure. The effects of rapidly changing climate on water resources must be minimised through sustainable water management techniques, conservation initiatives and International initiatives. The effects of climate change on the long run have been the focus of research because stable weather significantly influences agricultural productivity. Due to agricultures reliance on temperature and rainfall, climate change threatens world food security. Rising temperature results in lower productivity and also promotes the growth of weeds and pests, changes precipitation patterns, which will result in more crop failures and production declines. This work summarises the outcome of climate change on crop and livestock yields, water resources and the economy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Mahadevan M.; Noel J.K.; Umesh M.; Santhosh A.S.; Suresh S.
The ClimateHealth Sustainability Nexus: Understanding the Interconnected Impact on Populations and the Environment, pp. 207-229.
Mahadevan M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Noel J.K., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Umesh M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Santhosh A.S., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Suresh S., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India
Climate Change inflicted Environmental Degradation leading to the Crumbling of Arctic Ecosystem
Arctic ecosystem; ecological disturbances; Permafrost meltdown; SDG; seismic activity
The Arctic and Antarctic regions serve as the air conditioners of planet Earth. The polar regions located thousands of miles away from us determine the climatic patterns of our geographical area. They maintain our planet at bearable temperatures which are ideal for the existence of diverse flora and fauna and to support different types of ecosystems all around the world. Apart from controlling the temperatures, they also regulate ocean currents which in turn have an effect on the monsoons, winds, hurricanes etc. The poles were pristine till a few decades back. Due to mans greed, the poles started deteriorating at an alarming scale. Climate change, biodiversity changes, oil drilling, seismic testing, toxin accumulation are a few of the challenges faced by the Arctic ecosystem having serious effects on its topography, terrestrial and marine life-forms and the whole ecosystem. Due to the alarming scale of global warming, there is also the danger of permafrost meltdown which can unleash a plethora of dangerous pathogens buried underneath and also let out the huge amounts of locked down carbon. The crumbling of the polar ecosystem is leading to rampant consequences not only in the poles but also elsewhere in the world thousands of miles away. Here, we attempt to discuss the repercussions of the crumbling Arctic ecosystem due to the physical, chemical and geological changes caused by such anthropogenic activities and look at the efforts being carried out to save the Arctic ecosystem in a frantic effort to save our planet. 2024, World Researchers Associations. All rights reserved.
Suma S.; Saranya J.; Manas B.; Souvik M.
Disaster Advances, Vol-17, No. 7, pp. 36-47.
https://doi.org/10.25303/177da036047 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197601396&doi=10.25303%2f177da036047&partnerID=40&md5=ab75c9ecbb2d9318813e7e45693ffb5f
Suma S., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Saranya J., Department of Biotechnology, Mount Carmel College, Autonomous, Karnataka, Bangalore, 560052, India; Manas B., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Souvik M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India
Climate predictors in Indian summer monsoon forecasting: a novel De-correlated RVFL ensemble strategy
Artificial intelligence; De-correlated neural network; Ensemble learning; Negative correlation learning; Predictive modeling
Excessive rainfall and droughts harshly impact India's social and economic growth. Though several statistical methods have been used in literature to predict Indian monsoons, uncertainties cannot be ruled out. The accuracy prediction of ISMR (Indian Summer Monsoon Rainfall) is scientifically demanding. From this perspective, it is essential to explore exploiting machine learning techniques. In this paper, a novel De-correlated Regularized Random Vector Functional Link Neural Network Ensemble (DRRNE) prediction approach was proposed using Climate Predictors such as Southern Oscillation Index (SOI), Sea Surface Temperature Anomaly (SST), El-Ni Southern Oscillation (ENSO), and Dipole Mode Index (DMI) to predict ISMR. The proposed work has also investigated the predictability of climate above predictors using the DRRNE approach to predict ISMR. In addition to the predictors above, the data for an 8-year training window time series for June to September is combined and analyzed for four predictors (ENSO, DMI, SOI, and SST) to derive another predictor, ENSO-DMI-SOI-SST (EDSS). It is found that the combination of these four predictors- the EDSS- produces better accuracy than using any of the individual predictors in this study. Among the individual predictors (ENSO, DMI, SOI, and SST), the DMI predictor has shown the best predictability for ISMR prediction. Thus, the suggestedstudy concludes that the DRRNE technique with negative correlation learning may be a suitable tool for predicting the ISMR using the combined outcome of the four climate predictorsas mentioned above. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Dash Y.; Abraham A.; Kumar N.; Yadav D.K.; Singhal N.
Earth Science Informatics, Vol-18, No. 1
https://doi.org/10.1007/s12145-024-01532-y https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213963871&doi=10.1007%2fs12145-024-01532-y&partnerID=40&md5=836c48c60adcd7aeb8fdf221e58f97e8
Dash Y., School of Artificial Intelligence, Bennett University, Greater Noida, India; Abraham A., School of Artificial Intelligence, Bennett University, Greater Noida, India; Kumar N., SCSET, Bennett University, Greater Noida, India; Yadav D.K., SCSET, Bennett University, Greater Noida, India; Singhal N., Christ University, Central Campus, Bangaluru, India