A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires
Aerosol; Biomass burning; Climate change; Natural hazards; Wildfires
Background: Wildfires have a substantial impact on air quality and ecosystems by releasing greenhouse gases (GHGs), trace gases, and aerosols into the atmosphere. These wildfires produce both light-absorbing and merely scattering aerosols that can act as cloud condensation nuclei, altering cloud reflectivity, cloud lifetime, and precipitation frequency. Uttarakhand province in India experiences frequent wildfires that affect its protected ecosystems. Thus, a natural resource management system is needed in this region to assess the impact of wildfire hazards on land and atmosphere. We conducted an analysis of a severe fire event that occurred between January and April 2021 in the Kumaun region of Uttarakhand, by utilizing open-source geospatial data. Near-real-time satellite observations of pre- and post-fire conditions within the study area were used to detect changes in land and atmosphere. Supervised machine learning algorithm was also implemented to estimate burned above ground biomass (AGB) to monitor biomass stock. Results: The study found that 21.75% of the total burned area burned with moderate to high severity, resulting in a decreased Soil Adjusted Vegetation Index value (> 0.3), a reduced Normalized Differential Moisture Index value (> 0.4), and a lowered Normalized Differential Vegetation Index (> 0.5). The AGB estimate demonstrated a significant simple determination (r2 = 0.001702) and probability (P < 2.2 10?16), along with a positive correlation (r ? 0.24) with vegetation and soil indices. The algorithm predicted that 17.56 tonnes of biomass per hectare burned in the Kumaun forests. This fire incident resulted in increased emissions of carbon dioxide (CO2; ~ 0.8 10?4kgcarbonh?1), methane (CH4; ~ 200 10?9mol fraction in dry air), carbon monoxide (CO; 2000 1015moleculescm?2 total column), and formaldehyde (HCHO; 3500 1013moleculescm?2 total column), along with increased aerosol optical thickness (varying from 0.2 to 0.5). Conclusions: We believe that our proposed operational framework for managing natural resources and assessing the impact of natural hazards can be used to efficiently monitor near-real-time forest-fire-caused changes in land and atmosphere. This method makes use of openly accessible geospatial data that can be employed for several objectives, including monitoring carbon stocks, greenhouse gas emissions, criterion air pollution, and radiative forcing of the climate, among many others. Our proposed framework will assist policymakers and the scientific community in mitigating climate change problems and in developing adaptation policies. The Author(s) 2024.
Tiwari A.; Nanjundan P.; Kumar R.R.; Soni V.K.
Springer Science and Business Media Deutschland GmbH
Tiwari A., Christ University, Lavasa, Pune, 412112, India, India Meteorological Department, Shimla Office, Shivajinagar, Pune, 411005, India; Nanjundan P., Christ University, Lavasa, Pune, 412112, India; Kumar R.R., India Meteorological Department, Shimla Office, Shivajinagar, Pune, 411005, India; Soni V.K., India Meteorological Department, Mausam Bhawan, Lodhi Road, New Delhi, 110003, India
A Framework for Smart Innovations in Islamic Pilgrimage: Utilizing AI and IoT within the Context of Halal Tourism
AI integration; Artificial intelligence; cultural sensitivity; faith-based travel; gray literature; halal tourism; Islamic pilgrimage; technology and religion; thematic analysis
Despite growing discourse on smart tourism and AI applications in travel, limited scholarly attention has been given to understanding the reasons for the growth of AI usage and the various facets of its application, specifically within Islamic pilgrimage. This includes the absence of a developed framework for innovative inventions. This paper addresses the research gap by exploring how AI and IoT technologies can transform halal tourism management, focusing on the Hajj and Umrah pilgrimages. The paper deals with three objectives: (1) To understand the reasons for the growth of AI usage in halal tourism with special reference to Islamic Pilgrimage, (2) to examine the various facets of AI usage in Islamic pilgrimage, and (3) to develop the framework for Smart inventions in Islamic pilgrimage. Methodologically, this is a conceptual paper grounded in a secondary literature review. It incorporates a thematic content analysis of both academic publications and gray literature. With this approach, the researchers were able to gain a holistic view of how AI can intersect with Islamic pilgrimage and halal tourism. The results are demonstrated through the ideation of the FSIIP framework. This framework highlights AIs vital role in improving pilgrims experiences through tailored services, streamlining crowd control, and advancing sustainability through astute resource management. 2026 by Priyakrushna Mohanty, M Sharanya and Saurabh Bharti.
Mohanty, Priyakrushna; Sharanya, M.; Bharti, Saurabh
Halal Tourism and Artificial Intelligence: New Approaches for a New Generation;pp.105-119
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1108/978-1-80686-165-120261009 https://www.scopus.com/pages/publications/105033797126?origin=resultslist
Mohanty P., School of Business and Management, Christ University, Bengaluru, India; Sharanya M., School of Business and Management, Christ University, Bengaluru, India; Bharti S., Welcomgroup Graduate School of Hotel Administration, Manipal Academy of Higher Education, Manipal, India
A framework for smart transportation using Big Data
Big Data; Information and Communication Technology (ICT); Internet of Things (IoT); Smart City; Smart Transportation
In the current era of information technology, data driven decision is widely recognized. It leads to involvement of the term 'Big Data'. The use of IOT and ICT deployment is a key player of the smart city project in India. It leads to smart transportation systems with huge amounts of real time data that needs to be communicated, aggregated, interpreted, analyzed and maintained. These technologies enhance the effective usage of smart transportation systems, which is economical and has a high social impact. Social applications like transportation can be benefited by using IOT, ICT and big data analytics to give better prediction. In this paper, we present how big data analytics can be used to build a smart transportation system. Increasing traffic and frequent jams in today's scenario are becoming a routine, citizens are facing various health issues due to the bad transportation systems such as high blood pressure, stress, asthma due to air and noise pollution. In smart transportation mobility can be easily implemented as most of the citizens use smartphones. It can be easily linked to smart traffic signals to achieve the objective of smart transportation. Smart transportation is a key component to attract companies as it leads to better services, business planning, support beneficial environment and social behavior. 2016 IEEE.
Shukla S.; Balachandran K.; Sumitha V.S.
Proceedings of 2016 International Conference on ICT in Business, Industry, and Government, ICTBIG 2016
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICTBIG.2016.7892720 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019102027&doi=10.1109%2fICTBIG.2016.7892720&partnerID=40&md5=a356b6447e1c3827f63387742ec93d74
Shukla S., Department of Computer Science and Engineering, Faculty of Engineering, Christ University, Bengaluru, India; Balachandran K., Department of Computer Science and Engineering, Faculty of Engineering, Christ University, Bengaluru, India; Sumitha V.S., Department of Computer Science and Engineering, Faculty of Engineering, Christ University, Bengaluru, India
A frequent itemset generation approach in data mining using transaction-labelling dynamic itemset counting method
ARM; association rule mining; complexities; data mining; DIC; dynamic itemset counting method; frequent itemset generation; labelling; minimum support threshold; scan count; TL; TL-DIC; transaction labelling; transaction-labelling dynamic itemset counting; transactional database
A significant amount of data is generated, gathered, stored, and evaluated in real-world applications as a result of technology breakthroughs. Data mining (DM) combines a number of disciplines to efficiently discover hidden patterns from vast archives of historical information. To significantly reduce complexities associated with data, the proposed method, transaction-labelling dynamic itemset counting (TL-DIC), utilises a labelling approach on the given transactional database to logically arrange and process the underlying transactions. This method generates frequent itemsets thereby improving the performance of conventional dynamic itemset counting (DIC) method. Based on experimental findings, the average scan count in DIC and M-Apriori is 4% and 3.66%, respectively higher than TL-DIC, for different support counts. TL-DIC executes 20% and 16% quicker than DIC and M-Apriori, respectively, in terms of execution time. These results validate the proposed approachs efficacy in creating frequent itemsets from large datasets. Copyright 2025 Inderscience Enterprises Ltd.
International Journal of Data Mining, Modelling and Management;Volume;17;Issue;1;pp.54-74
https://doi.org/10.1504/IJDMMM.2025.144615 https://www.scopus.com/pages/publications/86000134816?origin=resultslist
Balaram A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Banglore, India; Raju N., Department of Computer Science and Engineering, Coimbatore Institute of Technology, Tamil Nadu, Coimbatore, India
A Fundamental Study on Electric Vehicle Model for Longitudinal Control
Closed loop; Electric vehicle; Longitudinal control; Modeling; Open loop; Simulation
Stricter emission norms need to drift toward being environment friendly have shifted the concentration in the automobile sector toward electric vehicles. This research article highlights the fundamental modeling steps required for an electric vehicle control system design following a simulation approach using MATLAB/Simulink software. From an electric vehicle design perspective, this approach offers an excellent solution to give insights into EV research involving multidisciplinary engineering aspects. The study presents longitudinal control technique, relevant observations and results to bring out the differences in open-loop and closed-loop case studies. It also intends to provide better understanding toward the need for a feedback, realization of an expected path profile for students and researchers in this field of interest. The steps involved in transforming the mathematical model into a simulation model and analysis of the simulation results are detailed in this article. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Smart Innovation, Systems and Technologies, Vol-229, pp. 367-375.
Indu K., Christ (Deemed to be University), Bengaluru, Karnataka, India; Aswatha Kumar M., Christ (Deemed to be University), Bengaluru, Karnataka, India
A Fusion Based Approach for Blood Vessel Segmentation from Fundus Images by Separating Brighter Optic Disc
blood vessel segmentation; fundus images; image fusion; optic disc
Abstract: In ophthalmology, blood vessel segmentation from fundus images plays a significant role in automated retinal disease screening systems. Several research papers on blood vessel segmentation suggest enhancing fundus images before segmentation significantly to improve performance. The brightness of the optic disc region in a fundus image negatively influences the enhancement of relatively darker vessel pixels. Segregation of brighter optic disc from fundus images before its enhancement is the fundamental idea behind developing the proposed framework. Initially, the optic disc is extracted from the input fundus image to form two images, one containing optical disc and the other, fundus image without optical disk. In the second stage, both the images are enhanced independently, followed by blood vessel segmentation. Finally, the segmented blood vessels from the images are fused to obtain a single image. Experiments conducted with fundus images from DRIVE, STARE, and CHASE_DB1 databases show improvement in the identification of blood vessel pixels. 2021, Pleiades Publishing, Ltd.
Pattern Recognition and Image Analysis, Vol-31, No. 4, pp. 811-820.
Wahid F.F., Department of Information Technology, Kannur University, Kannur, 670567, Kerala, India; Sugandhi K., Department of Information Technology, Kannur University, Kannur, 670567, Kerala, India; Raju G., Department of Computer Science and Engineering, Christ (Deemed to Be University), Kengeri Campus, Bengaluru, 560074, India
A Fuzzy AHP Approach to Evaluation of Value Addition in the Indian Medical Equipment Supply Chain
AHP; Fuzzy; Supply chain; Value addition
The research delineates the risks within the supply chain that impede firms from achieving optimal value-added ratios. A comprehensive inventory of medical equipment crucial to healthcare is identified, and manufacturing and order fulfilment durations are ascertained through non-participatory observation. Employing these durations, the value-added ratio is computed for each piece of medical equipment to assess the firms performance. The study divulges that the average value-added ratio for hospital laboratory equipment is alarmingly low, signifying the prevalence of non-value-added activities resulting in augmented costs and protracted turnaround times. Furthermore, hospital diagnostic and surgical equipment, although exhibiting higher value-added ratios in comparison with laboratory equipment, still evince an average value-added ratio below fifty per cent, underscoring that over half of the activities along this supply chain are non-value-added. These findings accentuate the necessity of addressing these issues to bolster the value addition within the Indian medical equipment supply chain. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Dass, P. Leslie; Nair, Sreerengan V. R.; Kurien, Georgy P.; Chandar, S. Kumar
Lecture Notes in Networks and Systems;Volume;1275;pp.43-60
https://doi.org/10.1007/978-981-96-2694-6_4 https://www.scopus.com/pages/publications/105007226294?origin=resultslist
Dass P.L., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Nair S.V.R., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Kurien G.P., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Chandar S.K., School of Business and Management, Christ University, Karnataka, Bengaluru, India
A fuzzy approach to project team selection
Fuzzy set; Project team selection; Soft set
Project team selection is a complex process in software engineering. The study uses a multiple criteria decision making (MCDM) approach for the selection of a project team under fuzzy environment. In this paper a FRI, FSS approaches are developed to the selection of project team. 2019, International Journal of Scientific and Technology Research. All rights reserved.
Kalayathankal S.J.; Abraham J.T.; Kureethara J.V.
International Journal of Scientific and Technology Research, Vol-8, No. 9, pp. 1794-1798.
International Journal of Scientific and Technology Research
Kalayathankal S.J., Department of Mathematics, K. E. College, Mannanam, Kottayam, 686561, India; Abraham J.T., Department of Computer Science, Bharatha Matha College, Thrikkakara, Cochin, 682021, Kerala, India; Kureethara J.V., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India
A fuzzy computing software quality model
Expectation of the quality of a software varies from user to user. A fuzzy approach to measure the quality of a software is very appropriate so that it can deal with non-crisp aspects of the various parameters. In the proposed model, ordered intuitionistic fuzzy soft sets (OIFSS) and relative similarity measures of OIFSS are considered in the backdrop of fuzzy multiple criteria decision making (FMCDM) approach. 2019 Author(s).
https://doi.org/10.1063/1.5092932 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063270474&doi=10.1063%2f1.5092932&partnerID=40&md5=75a02680e49862ffe8c5c480b81a82c1
Kalayathankal S.J., R and D Centre, Bharathiar University, Coimbatore, Tamil Nadu, 641046, India, Kuriakose Elias College, Mannanam, Kerala, 686561, India; Abraham J.T., Bharatha Matha College, Cochin, Kerala, 682021, India; Kureethara J.V., CHRIST (Deemed to Be Univeristy), Bengaluru, Karnataka, 560029, India
A Fuzzy Logic Approach for Prioritizing Customer Retention Strategies in OTT Video Platforms
AHP; Customer retention strategies; Fuzzy AHP; OTT video platforms; Strategy prioritization
The emergence of over-the-top (OTT) video platforms has significantly transformed the way people consume content and the entire media landscape. The significant growth of OTT video platforms in recent years, amidst fierce competition and changing consumer preferences, has posed a challenge for the platforms in retaining customers. Elevated churn rates in the OTT video platforms have prompted them to focus on customer retention. OTT platforms implemented different strategies to retain customers. The customer retention strategies are identified through a literature review and unstructured interviews with industry experts. This paper presents a novel approach to prioritize customer retention strategies using a fuzzy analytic hierarchy process (fuzzy AHP). The fuzzy AHP analysis results show that content strategy is the most significant, followed by pricing, customer experience, and platform extension. This paper provides actionable insights for OTT platform managers, helping them enhance user satisfaction and retain customers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Babu, Bincy Ann; Elangovan, N.; Anand, Kerena; Kalapurackal, Jacob Joseph
Lecture Notes in Networks and Systems;Volume;1445 LNNS;pp.47-61
https://doi.org/10.1007/978-981-96-7140-3_4 https://www.scopus.com/pages/publications/105028292338?origin=resultslist
Babu B.A., School of Business and Management, Christ University, Bengaluru, India; Elangovan N., School of Business and Management, Christ University, Bengaluru, India; Anand K., School of Business and Management, Christ University, Bengaluru, India; Kalapurackal J.J., School of Business and Management, Christ University, Bengaluru, India
Covid-19 prediction; Fuzzy soft MCDM; PRONE index; Self-organization; Swarm intelligence and Covid
The entire world experienced a rampant outbreak of Covid-19 beginning in December 2019. The spread of this disease was so rapid and aggressive that many developed countries struggled to control it. However, some countries such as China and Australia have done a commendable job of controlling this virus. Various studies have been done in parallel to analyze strategies to curb the spread of the virus. In many locations, people displayed swarm intelligence. The collective behavior of people was mixed. Some people followed the instructions of the health authorities. In addition to the instructions, people in some localities developed self-organization to resist the spreading of the virus. This research work mainly focuses on the prediction of coronavirus spread in different districts of Kerala by use of a fuzzy approach as the fuzzy approach is considered the best tool that would not show imprecise data in any situation. The PRONE (Predicted Risk of New Event) indexing algorithm was used for finding the intensity of the spread in five districts of Kerala (Trivandrum, Ernakulam, Kozhikode, Kannur, and Kasargod) and was evaluated under the input parameters of immunity of person, food habits, financial factors, and age with the total number of infected people as the output variable. An eight-step algorithm is provided to determine the PRONE index. Kasargod is more vulnerable to the virus. The final results show that this proposed model better predicts virus spread. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.
Kalayathankal S.J.; Sukumaran A.; Kureethara J.V.
Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications, pp. 331-341.
Kalayathankal S.J., Jyothi Engineering College, Cheruthuruthy, Thrissur, India; Sukumaran A., Jyothi Engineering College, Cheruthuruthy, Thrissur, India; Kureethara J.V., Christ University, Bengaluru, India
A Gated Recurrent Unit Based Continual Normalization Model for Arrythmia Classification Using ECG Signals
Cardiac arrhythmias; Continual normalization; Electrocardiogram; Gated Recurrent Unit; MIT-BIH
In this world, around 31% of the deaths are commonly caused because of cardiovascular diseases. Around 80% of sudden deaths occur due to cardiac arrhythmias and heart diseases. The mortality rate has increased for cardiac disease and therefore early heart disease detection is significant to preclude patients from dying. At the initial phase, the heart disease is detected by analyzing abnormal heartbeats. The existing models failed to select the features before performing the extraction of features. The developed model examined MIT-BIB database to surpass the overfitting issue. Therefore, in the present research work, the Gated Recurrent Unit (GRU) based Continual Normalization (CN) classifier is used to speed up the training to a higher learning rate to enable simpler learning for the standard deviation of the neurons' output. The extracted features were used to classify Electrocardiogram (ECG) signals into 5 important classes named as N, S, V, F & Q which denote the kinds of arrhythmia. The findings revealed that the proposed GRU based Continual Normalization technique obtained an accuracy of 99.41% which is better when compared with the existing researches. 2023 IEEE.
Manjunath C.; Hemalatha K.L.; Sandya V.; Sunil G.; Shakir A.M.
IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
Manjunath C., CHRIST(Deemed to Be University), Department of Cse, Bengaluru, India; Hemalatha K.L., Sri Krishna Institute of Technology, Department of Information Science and Engineering, Bengaluru, India; Sandya V., Cmr Technical Campus, Department of CSE(Data Science), Hyderabad, India; Sunil G., School of Computer Science & Artificial Intelligence, Sr University, Warangal, India; Shakir A.M., The Islamic University, Najaf, Iraq
A Gauss Hypergeometric-Type Model for Heavy-Tailed Survival Times in Biomedical Research
Bayesian estimation; Gauss hypergeometric function; heavy-tailed data; loglogistic distribution; maximum likelihood estimation; survival analysis
In this study, we introduced and analyzed the SlashLogLogistic (SlaLL) distribution, a novel statistical model developed by applying the slash methodology to loglogistic and beta distributions. The SlaLL distribution is particularly suited for modeling datasets characterized by heavy tails and extreme values, frequently encountered in survival time analyses. We derived the mathematical representation of the distribution involving Gauss hypergeometric and beta functions, explicitly established the probability density function, cumulative distribution function, hazard rate function, and reliability function, and provided clear definitions of its moments. Through comprehensive simulation studies, the accuracy and robustness of maximum likelihood and Bayesian methods for parameter estimation were validated. Comparative empirical analyses demonstrated the SlaLL distributions superior fitting performance over well-known slash-based models, emphasizing its practical utility in accurately capturing the complexities of real-world survival time data. 2025 by the authors.
Gillariose, Jiju; Abdelwahab, Mahmoud M.; Joseph, Joshin; Hasaballah, Mustafa M.
Symmetry;Volume;17;Issue;11;Article No.;1795;
Multidisciplinary Digital Publishing Institute (MDPI)
https://doi.org/10.3390/sym17111795 https://www.scopus.com/pages/publications/105023208925?origin=resultslist
Gillariose J., Department of Statistics and Data Science, Christ University, Hosur Road, Karnataka, Bangalore, 560029, India; Abdelwahab M.M., Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Joseph J., School of Commerce and Professional Studies, Marian College Kuttikkanam, Kerala, Peermade, 685531, India; Hasaballah M.M., Department of Basic Sciences, Marg Higher Institute of Engineering and Modern Technology, Cairo, 11721, Egypt
A generalized software reliability prediction model for module based software incorporating testing effort with cost model
Maximum likelihood estimation (MLE); Non-homogeneous Poisson process (NHPP); Optimal release time (ORT); Software reliability growth model (SRGM); Testing effort function (TEF); Warranty cost model
As software innovation has advanced, it has been noted that the testing effort function (TEF) is one of the key factors influencing the improvement of software reliability. This paper presents a simplified model which incorporates the testing effort for the reliability growth of a software. A closed form solution has been derived for the reliability of the software. This study examines the impact of testing efforts on a software reliability model based on NHPP. The sensitivity analysis has been made available to investigate how the created model's system parameters affect the cost function, mean value function, and softwares reliability. The parameters of the model have been estimated using the non-linear least square estimation (MLE) method in MATLAB software. Additionally, a warranty cost model is constructed to assess the optimal release policy for the software. The general form of the reliability expression involves elliptic integrals, which can be computed easily through a software like Mathematica. We have derived analytical solutions for reliability pertaining to several particular cases. Optimal release time for the software product has been calculated for some particular cost-sets. Goodness of fit curves have been plotted to compare the proposed model with some well-known existing SRGMs. Numerical illustrations are provided to bolster the analytical outcomes. The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.
Life Cycle Reliability and Safety Engineering, Vol-13, No. 4, pp. 419-436.
Yadav A.K., Department of Applied Mathematics and Scientific Computing, Indian Institute of Technology, Roorkee, 247001, India; Srivastava S., Christ University, Karnataka, Bengaluru, 560029, India; Pant M., Department of Applied Mathematics and Scientific Computing, Indian Institute of Technology, Roorkee, 247001, India, Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology, Roorkee, 247667, India
A generic cyber immune framework for anomaly detection using artificial immune systems
Artificial immune system framework; Cyber immune system; Intrusion detection system; Natural killer cells; Response time
Intrusion detection systems play a significant role in computer security. Artificial immune systems are the prime contender in developing an anomaly-based intrusion detection system due to their simplicity. The fundamental goal of this paper is to create a generic framework for an artificial immune system which is fast and accurate in detecting anomalies using artificial immune system concepts. Natural killer cells in the immune system and their quick response to foreign pathogens inspired the adaptation of those cells into an artificial immune system based framework. A natural killer cell-based framework is proposed to improve the accuracy and speed of anomaly detection. The structure of the proposed framework includes major histocompatibility complex class 1 representation, affinity calculation, cell generation, and cell proliferation. This framework addresses the overlapping and hole problem while creating natural killer cells to increase the system's performance. The negative selection algorithm and the positive selection algorithm generate the cells that enhance the anomaly detection technique and give high precision. The parameter response time introduced in this paper is crucial for an intrusion system to be used in real-time. 2022 Elsevier B.V.
Bejoy B.J.; Raju G.; Swain D.; Acharya B.; Hu Y.-C.
https://doi.org/10.1016/j.asoc.2022.109680 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140476942&doi=10.1016%2fj.asoc.2022.109680&partnerID=40&md5=d63e9960f036f6fcef72bd6586ddbbe3
Bejoy B.J., Department of CSE, CHRIST University, Bangalore, India; Raju G., Department of CSE, CHRIST University, Bangalore, India; Swain D., Department of CSE, Pandit Deendayal Energy University, Gandhinagar, India; Acharya B., Department of Computer Engineering-AI, Marwadi University, Gujarat, Rajkot, India; Hu Y.-C., Department of Computer Science and Information Management, Providence University, Taiwan
A generic framework for forecasting lake surface area dynamics using level set segmentation and double exponential smoothing
Change detection framework; Double exponential smoothing; Landsat multi-temporal data; Water body change monitoring
Water has been a crucial element for the sustenance of civilization throughout history and civilizations have sprung up around a body of water in one form or another. It becomes imperative to address the pressing issue of water shortage and the shrinking size of urban water bodies, which is particularly relevant in Indian cities like Bangalore. The effective management and preservation of these invaluable resources depend on the development of accurate and automated tools to monitor them. The proposed framework introduces a novel approach, combining a level set-based segmentation algorithm with double exponential smoothing to monitor water bodies using multispectral satellite images. In-depth review of nine lakes within Bangalore was carried out using a Landsat time series data set spanning 1987 to 2020. The resulting forecasting model, employing a univariate smoothing methodology, showcased exceptional performance metrics. Notably, it yielded an average error of 0.072 and exhibited a robust correlation coefficient of 0.94 when cross-referenced with proven results. The proposed framework holds great potential for practical implementation in the domain of long-term water body analysis, effectively catering to the requirements of administrative and decision-making entities. Moreover, the adaptability of this framework for the incorporation of additional external factors, as well as its potential to analyze seasonal dynamics, offers exciting avenues for further exploration. The dataset of delineated lake images prepared in this study presents an opportunity for the advancement of image-to-image regression networks, enabling the prediction of both area and shape variations for lakes, thereby enhancing predictive accuracy and insights. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
Bijeesh, T.V.; Narasimhamurthy, K.N.; Sathish, P.K.; Bejoy, B.J.; Michael Moses, J.
Sustainable Water Resources Management;Volume;11;Issue;4;Article No.;84;
https://doi.org/10.1007/s40899-025-01277-6 https://www.scopus.com/pages/publications/105014125472?origin=resultslist
Bijeesh T.V., Department of CSE, CHRIST University, Kengeri Campus, Karnataka, Bangalore, 560060, India; Narasimhamurthy K.N., Department of Civil Engineering, CHRIST University, Kengeri Campus, Karnataka, Bangalore, 560060, India; Sathish P.K., Department of CSE, CHRIST University, Kengeri Campus, Karnataka, Bangalore, 560060, India; Bejoy B.J., Department of CSE, CHRIST University, Kengeri Campus, Karnataka, Bangalore, 560060, India; Michael Moses J., Department of CSE, CHRIST University, Kengeri Campus, Karnataka, Bangalore, 560060, India
A genome wide association study of fast beta EEG in families of European ancestry
Electrophysiology; Endophenotype; Genome-Wide Association Study (GWAS); Resting EEG
Background Differences in fast beta (2028Hz) electroencephalogram (EEG) oscillatory activity distinguish some individuals with psychiatric and substance use disorders, suggesting that it may be a useful endophenotype for studying the genetics of disorders characterized by neural hyper-excitability. Despite the high heritability estimates provided by twin and family studies, there have been relatively few genetic studies of beta EEG, and to date only one genetic association finding has replicated (i.e., GABRA2). Method In a sample of 1564 individuals from 117 families of European Ancestry (EA) drawn from the Collaborative Study on the Genetics of Alcoholism (COGA), we performed a Genome-Wide Association Study (GWAS) on resting-state fronto-central fast beta EEG power, adjusting regression models for family relatedness, age, sex, and ancestry. To further characterize genetic findings, we examined the functional and behavioral significance of GWAS findings. Results Three intronic variants located within DSE (dermatan sulfate epimerase) on 6q22 were associated with fast beta EEG at a genome wide significant level (p<5נ10?8). The most significant SNP was rs2252790 (p<2.6נ10?8; MAF=0.36; ?=0.135). rs2252790 is an eQTL for ROS1 expressed most robustly in the temporal cortex (p=1.2נ10?6) and for DSE/TSPYL4 expressed most robustly in the hippocampus (p=7.3נ10?4; ?=0.29). Previous studies have indicated that DSE is involved in a network of genes integral to membrane organization; gene-based tests indicated that several variants within this network (i.e., DSE, ZEB2, RND3, MCTP1, and CTBP2) were also associated with beta EEG (empirical p<0.05), and of these genes, ZEB2 and CTBP2 were associated with DSM-V Alcohol Use Disorder (AUD; empirical p<0.05). Discussion In this sample of EA families enriched for AUDs, fast beta EEG is associated with variants within DSE on 6q22; the most significant SNP influences the mRNA expression of DSE and ROS1 in hippocampus and temporal cortex, brain regions important for beta EEG activity. Gene-based tests suggest evidence of association with related genes, ZEB2, RND3, MCTP1, CTBP2, and beta EEG. Converging data from GWAS, gene expression, and gene-networks presented in this study provide support for the role of genetic variants within DSE and related genes in neural hyperexcitability, and has highlighted two potential candidate genes for AUD and/or related neurological conditions: ZEB2 and CTBP2. However, results must be replicated in large, independent samples. 2016 Elsevier B.V.
Meyers J.L.; Zhang J.; Manz N.; Rangaswamy M.; Kamarajan C.; Wetherill L.; Chorlian D.B.; Kang S.J.; Bauer L.; Hesselbrock V.; Kramer J.; Kuperman S.; Nurnberger J.I., Jr.; Tischfield J.; Wang J.C.; Edenberg H.J.; Goate A.; Foroud T.; Porjesz B.
International Journal of Psychophysiology, Vol-115, pp. 74-85.
ISSN: 1678760; PubMed ID: 28040410; CODEN: IJPSE
https://doi.org/10.1016/j.ijpsycho.2016.12.008 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010953537&doi=10.1016%2fj.ijpsycho.2016.12.008&partnerID=40&md5=d461f8607df9968ee93ec10d0fdd4749
Meyers J.L., Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY, United States; Zhang J., Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY, United States; Manz N., Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY, United States, Department of Physics, College of Wooster, Wooster, OH, United States; Rangaswamy M., Department of Psychology, Christ University, Bangalore, India; Kamarajan C., Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY, United States; Wetherill L., Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States; Chorlian D.B., Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY, United States; Kang S.J., Albany Stratton VA Medical Center, Albany, NY, United States; Bauer L., University of Connecticut School of Medicine, Farmington, CT, United States; Hesselbrock V., University of Connecticut School of Medicine, Farmington, CT, United States; Kramer J., Department of Psychiatry, University of Iowa, Iowa City, IA, United States; Kuperman S., Department of Psychiatry, University of Iowa, Iowa City, IA, United States; Nurnberger J.I., Jr., Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States; Tischfield J., Rutgers University, Piscataway, NJ, United States; Wang J.C., Icahn School of Medicine at Mt. Sinai, New York, NY, United States; Edenberg H.J., Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States; Goate A., Icahn School of Medicine at Mt. Sinai, New York, NY, United States; Foroud T., Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States; Porjesz B., Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY, United States
A Glimpse into the Future: AI, Digital Humans, and the Metaverse Opportunities and Challenges for Life Sciences in Immersive Ecologies
AI; Augmented reality; Digital humans; Digital twins; Extended reality; Metaverse; Mixed reality; Virtual reality
The Metaverse is poised to have a significant impact in life sciences, especially in the healthcare sector. In the near future, genomic data along with AI and extended reality may be used to enhance digital humans to create digital twins to be used for virtual world interactions, or manipulated to obtain insights for real-world healthcare decision-making. In addition, extended reality may enable more robust population-based research and faster drug discovery, and permit the creation of virtual spaces and immersive environments for patients and physicians alike. In this chapter, we examine aspects of extended reality and AI that will play important roles in various areas of life sciences and discuss the future of life sciences in the Metaverse. 2023 John Wiley & Sons Ltd.
AI in Clinical Medicine: A Practical Guide for Healthcare Professionals, pp. 521-527.
Surveswaran S., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, India; Deshpande L., XR Labs, Tata Consultancy Services, Mumbai, India
A global perspective on psychologists' and their organizations' response to a world crisis; [Una perspectiva global sobre la respuesta de los psicogos y sus organizaciones a una crisis mundial]
COVID-19; Governance; International psychology; Organizational response; Public policy
Around the world, individual psychologists have stepped up to deliver essential services to address the social and emotional sequelae of the COVID-19 pandemic. Many psychological organizations have also responded to this public health crisis, though their efforts may be less widely recognized. Psychological organizations engaged in preventive and mitigation efforts targeted, among others, the general public, local communities, and high-risk groups such as health care providers. They disseminated mental health information to the general public, trained laypersons to provide psychological first aid, and used research to design and evaluate public health responses to the pandemic. In some countries, psychological organizations contributed to the design and implementation of public health policies and practices. The nature of these involvements changed throughout the pandemic and evolved from reactive to proactive, from local to international. Several qualities appear key to the value, impact, and success of these efforts. These include organizational agility and adaptability, the ability to overcome their political inertia and manage conflict, recognizing the need to address cultural differences, and allocating limited resources to high-risk and resource-depleted constituencies where it was needed most. 2021, Sociedad Interamericana de Psicologia. All rights reserved.
Gutirez G.; Barbarin O.; KlicperovBaker M.; Padakannaya P.; Thompson A.; Crowe S.; Khoury B.
Interamerican Journal of Psychology, Vol-55, No. 2
https://doi.org/10.30849/ripijp.v55i2.1713 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118228839&doi=10.30849%2fripijp.v55i2.1713&partnerID=40&md5=93b778d2b5eea3ff4e012f5012a6bcf7
Gutirez G., Universidad Nacional de Colombia, Bogot Colombia; Barbarin O., University of Maryland, College Park, United States; KlicperovBaker M., Czech Academy of Sciences, Prague, Czech Republic; Padakannaya P., Christ University, Bengaluru, India; Thompson A., University of the Bahamas, Nassau, Bahamas; Crowe S., La Trobe University, Melbourne, Australia; Khoury B., American University of Beirut, Beirut, Lebanon
A GPS-Gradient Mapped Database-Based Fuzzy Energy Management System for a SeriesParallel Hybrid Electric Vehicle
The Energy Management System developed for the hybrid electric vehicle operates using a database with GPS co-ordinates and corresponding altitudes mapped, thereby giving a predictive control to optimize the operation of the seriesparallel hybrid system. The system aims at extracting the maximum potential of the seriesparallel hybrid power train architecture. The mapping of the latitude and longitude obtained from a global positioning system (GPS) to the altitude measured to create a database which generates a predefined driving cycle prior to the actual motion of the vehicle. The created database is then used in a MATLAB/Simulink model to simulate the operation of the seriesparallel hybrid system and implement the Energy Management System. The validated data is then tested in a Raspberry Pi (RPi)-based prototype. The Energy Management System regulates the vehicle dynamics based on the input drive cycle. The fuzzy logic-based control mechanism is implemented in the RPi to optimize the load sharing between the IC engine and the brushless DC motor. 2020, Springer Nature Singapore Pte Ltd.
Thomas J.; Thomas A.; Biju A.; Mathew A.; Jose C.P.; Haneesh K.M.
Lecture Notes in Electrical Engineering, Vol-665, pp. 515-527.
Thomas J., Faculty of Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Thomas A., Faculty of Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Biju A., Faculty of Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Mathew A., Faculty of Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Jose C.P., Faculty of Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Haneesh K.M., Faculty of Engineering, CHRIST (Deemed to Be University), Bengaluru, India