Advanced Machine Learning Model for Optimizing Pricing Strategies for Logistic Firms
Cost optimization; Logistics; Machine learning; Operational efficiency; Profitability; Relocation
Cost optimization in logistics is a very crucial aspect for businesses to remain profitable and competitive by identifying and eliminating unnecessary costs. Most of the researchers concentrated primarily on demand modeling, vehicle routing challenges, and warehouse cost optimization, hence the existing models underperform. This study introduces a novel prediction model that optimizes costs by considering critical factors such as labor charges, material costs, transportation expenses, task types, and branch location. The current model is worked on a primary dataset of 2468 rows and 28 columns which was obtained from an established relocation company in India with all the confidentiality followed. To improve model performance, the required features were adjusted by rigorous feature engineering and data pretreatment techniques such as box-cox scaling, Winsorization, robust scaling, and one-hot encoding. Three ensemble learning techniques were tested: AdaBoost, XGBoost, and gradient boosting. The gradient boosting model correctly captured the complicated nonlinear connections between cost components and income, enabling for cost optimization decisions across a wide range of operational conditions. The proposed model has shown excellent results with the values achieving an MSE of 15% which demonstrates the effectiveness in cost optimization. However, the presence of residuals and potential outliers suggests that more model refinement and process improvements are required. The studys findings offer a data-driven framework for logistics and relocation companies to reduce costs, boost profitability, and gain a competitive advantage in the marketplace. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Rithul, V.; George, Jossy; Nair, Akhil M.; Alapatt, Bosco Paul; Baby, Riya
Learning and Analytics in Intelligent Systems;Volume;43;pp.77-88
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-97-9855-1_6 https://www.scopus.com/pages/publications/105006993965?origin=resultslist
Rithul V., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; George J., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; Nair A.M., Luxsh Technologies Pvt Ltd., Middlesex, United Kingdom; Alapatt B.P., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; Baby R., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India
Advanced Machine Learning Techniques for Detecting Irregularities in Skin Lesion Borders: Enhancing Early Skin Cancer Detection
arc-based features; dermatogram analysis; ensemble learning; feature extraction; Skin cancer detection
Dermatograms are pivotal in the early detection of skin cancer, a disease with significant mortality rates. This paper introduces a novel feature extraction method that captures irregularities in the boundaries of abnormal skin regions. Each raw dermatogram is converted into a binary mask image using an effective segmentation algorithm. The boundary of the lesion region is extracted from the mask. The boundary, together with the centroid of the lesion mask, is used to define a set of directional vectors. An Arc is defined using these directional vectors, and a new Arc feature is calculated based on the number of times the lesion boundary crosses the arc. The proposed Arc feature is evaluated using three standard skin lesion datasets: ISBI 2016, HAM10000, and PH2. Additionally, color features and Local Binary Pattern (LBP) features are implemented for comparison. Classical machine learning algorithms are employed to evaluate these features. Results indicate that for the ISBI 2016 and HAM10000 datasets, the Arc feature set demonstrates superior classification accuracy. In contrast, the PH2 dataset benefits more from the LBP feature. Comparative analysis with recent studies highlights the dependency of accuracy on datasets and classifiers, underscoring the necessity for models incorporating feature fusion and ensemble classifiers. The proposed method outperforms traditional color and texture features and shows competitive results against deep learning models, particularly in scenarios with limited computational resources. These findings suggest that the Arc feature is a promising approach for improving skin cancer detection, although further investigation is needed to fine-Tune performance, optimize classifier selection, and explore feature fusion strategies. 2024 World Scientific Publishing Company.
Raju G.; Nikesh P.; Resmi K.R.; Swain D.; Acharya B.; Gerogiannis V.C.; Kanavos A.
International Journal on Artificial Intelligence Tools
Raju G., Department of Computer Science and Engineering, Christ University, Bengaluru, Karnataka, India; Nikesh P., Department of Computer Science and Engineering, Government Engineering College, Wayanad, Kerala, India; Resmi K.R., Department of Computer Science and Engineering, Christ University, Bengaluru, Karnataka, India; Swain D., Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India; Acharya B., Department of Computer Engineering-AI, Marwadi University, Gujarat, Rajkot, India; Gerogiannis V.C., Department of Digital Systems, University of Thessaly, Larissa, Greece; Kanavos A., Department of Informatics, Ionian University, Corfu, Greece
Advanced Machine Vision Paradigms for Medical Image Analysis
Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs. 2020 Elsevier Inc. All rights reserved.
Gandhi T.; Bhattacharyya S.; De S.; Konar D.; Dey S.
Advanced Machine Vision Paradigms for Medical Image Analysis, pp. 1-289.
Gandhi T., Department of Electrical Engineering, Indian Institute of Technology, Delhi, New Delhi, India; Bhattacharyya S., Christ University, Department of Computer Science and Engineering, Karnataka, Bangalore, India; De S., Cooch Behar Government Engineering College, Department of Computer Science and Engineering, West Bengal, Cooch Behar, India; Konar D., Sikkim Manipal Institute of Technology, Department of Computer Science and Engineering, Sikkim Manipal University, Sikkim, Majitar, India; Dey S., Sukanta Mahavidyalaya, Department of Computer Science, West Bengal, Dhupguri, Jalpaiguri, India
Advanced Malware Analysis and Detection Using Deep Neural Networks
Artificial Intelligence; Cyber-attack; Deep Learning; Machine Learning; Malware Detection; Neural Network; Random Forest
Malware is malicious software that is used to cause harm to the computer systems, networks or users across several operating systems, such as Windows, macOS, iOS, Android and Linux. The identification and categorisation of malware is a difficult subject with no one-size-fits-all solution due to the constant evolution of the malware and lack of standardised detection frameworks. The use of deep learning models in cybersecurity encourages the growth of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) techniques. The goal of this research is to automate the way of analysing malware using a simple framework without the need of complex software. This article focuses on the application of Neural Networks, a deep learning model in detecting and analysing the behavior of malware. The model performed better when compared to other techniques by achieving an accuracy of 99.43%, precision of 99.05% and a F1 score of 0.99, which was trained on a large dataset containing 1,38,048 samples. 2025 IEEE.
Dominic Andrew, P.; Jose, Anna Mariya; Jayapandian, N.; Angel, Chinnapalli Neha; Brar, Krishjeet
Conference Proceedings - 2025 IEEE 4th International Conference on Data, Decision and Systems, ICDDS 2025;pp.7-12
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICDDS67737.2025.11344689 https://www.scopus.com/pages/publications/105033355386?origin=resultslist
Dominic Andrew P., Christ University, Department of Cse, Bangalore, India; Jose A.M., Christ University, Department of Cse, Bangalore, India; Jayapandian N., Christ University, Department of Cse, Bangalore, India; Angel C.N., Christ University, Department of Cse, Bangalore, India; Brar K., Christ University, Department of Cse, Bangalore, India
Advanced Materials for Next-Generation Energy Storage Devices: A Focus on Efficiency and Cost Reduction
Advanced materials; cost reduction; efficiency optimization; energy storage; nanomaterials
The increasing demand for efficient and cost-effective energy storage systems has pushed extensive research into improved materials for next-generation energy storage devices. This study discusses the crucial significance of material advances in boosting the performance and reducing the costs of storage technologies such as batteries and supercapacitors. Conventional energy storage systems face limits in energy density, charge or discharge rates, and scalability, which impede their broad implementation. Advanced materials, including nanomaterials, solid-state electrolytes, and innovative electrode compounds, offer solutions to these difficulties by enhancing energy efficiency, power output, and overall longevity. Additionally, the use of plentiful and low-cost materials, such as sodium-ion and aluminium-based compounds, presents prospects for significant cost savings. This research analyzes current trends, issues in material manufacturing, and future perspectives for energy storage systems, concentrating on balancing efficiency improvements with cost-effectiveness to enable the rising integration of renewable energy sources. The development of these materials is important to creating sustainable, scalable, and economical energy storage systems for the future. The Authors, published by EDP Sciences.
Chandrakala P.; Liz A.S.; Mane V.; Jeshurun S.B.; Sharma G.; Pardeshi P.R.; Senthil kumar R.
https://doi.org/10.1051/e3sconf/202459101001 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211801054&doi=10.1051%2fe3sconf%2f202459101001&partnerID=40&md5=6a6055616267c09ddd036f1ce952ef45
Chandrakala P., Department of EEE, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, 127, India; Liz A.S., Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Tamil nadu, Chennai, 600073, India; Mane V., Vishwakarma Institute of Technology, Pune, India; Jeshurun S.B., School of Business and Management, CHRIST University, Bangaluru, India; Sharma G., Institute of Business Management, GLA University, Mathura, India; Pardeshi P.R., Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Techology, Pimrpi, Pune, India; Senthil kumar R., AAA College of Engineering and Technology, Sivakasi, India
Advanced Materials from Biomass and Its Role in Carbon-Di-Oxide Capture
Biomass; Carbon sequestration; CO2; Nanotechnology; Pyrolysis
This chapter explores utilizing agricultural waste for developing advanced materials for CO2 capture, overcoming drawbacks of conventional adsorbents. It compares biomass-based activated carbons CO2 adsorption capabilities to commercial adsorbents, highlighting promising performance. Strategies for enhancing selectivity and efficiency through functional group hybridization are discussed, alongside investigations into operational parameters effects on material properties and CO2 uptake. Additionally, the chapter reviews biomass-derived carbon materials role in CO2 capture, detailing conversion techniques like pyrolysis and hydrothermal carbonization. Various modification methods, including activation and N-doping, are examined for enhancing CO2 capture. Discussion extends to diverse advanced materials derived from biomass, including biochar and activated carbon. The chapter underscores the circular-economy impact of utilizing biomass-derived porous carbons in CO2 capture processes, particularly in biogas upgrading to biomethane. Overall, it offers insights into addressing CO2 capture challenges, proposing future research directions in this field. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Materials Horizons: From Nature to Nanomaterials, Vol-Part F3306, pp. 237-247.
Nair J.B., Serah Zines LLP, C/O Seed Foundation IISER Pune, Maharashtra, Pune, 411008, India; Joseph A.M., Department of Chemistry, Newman College Thodupuzha, Kerala, Thodupuzha, 685585, India; Joseph M.M., Department of Life Sciences, School of Science, CHRIST University, Karnataka, Bengaluru, 560029, India
Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to Dimensionality Reduction Using NLP
BERT Embeddings; Lexicon-Based Sentiment Analysis; Natural Language Processing (NLP); Sentiment Analysis in Many Different Languages
Social media platforms serve as vital connections for communication, generating massive quantities of data that represent an array of perspectives. Efficient sentiment analysis is necessary for understanding public opinion, particularly in domains such as product reviews and socio-political discussion. This paper develops a novel sentiment analysis model that is customized for social media data by integrating machine learning algorithms, language processing techniques with part-of-speech tagging, and dimensionality reduction methods. The model will improve sentiment analysis performance by tackling challenges like noise and data domain variations. To further improve sentiment representation, it includes convolutional neural networks (CNNs), BERT embeddings, N-grams, and sentiment lexicons. The model's effectiveness is determined on a variety of datasets, which enhances sentiment analysis in social media discussion. This paper goes beyond sentiment analysis in code-mixed, multilingual text and highlights the importance of careful data before treatment and an extensive variety of ML algorithms. This study attempts to explain the nuances of sentiment analysis and its use in social media discussions through methodical research. 2024 IEEE.
Kumar V.; Chauhan P.S.; Lella J.A.; Momidi M.; Gupta P.; Upreti K.
2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 441-446.
Kumar V., SEAS, GWU, Washington, DC, United States; Chauhan P.S., Global Payments, Atlanta, United States; Lella J.A., Electrical & Comm. Engg., National Institute of Technology, Calicut, India; Momidi M., Dell Technologies, Bangalore, India; Gupta P., Computer Science Engineering, ADGIPS, Delhi, India; Upreti K., Christ University, Dept. of Computer Science, Delhi NCR Campus, India
Advanced Simulation and Sensor-Based Approaches for Evaluating Cyclone Readiness of Civil Structures
Cyclones pose significant threats to civil infrastructure, demanding advanced strategies for prediction, assessment, and resilience. This chapter explores cutting-edge simulation and sensor-based technologies that enhance the cyclone readiness of buildings and critical structures. Techniques such as Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), Structural Health Monitoring (SHM), and digital twin modelling provide deeper insights into structural behaviour under extreme winds. The integration of AI and machine learning further improves forecasting accuracy, damage detection, and real-time decision-making. Global case studies demonstrate successful applications in cyclone-prone regions. As climate change intensifies storm severity, adopting these innovative tools is essential for safeguarding communities, reducing economic losses, and supporting sustainable, resilient infrastructure development worldwide. 2026 by IGI Global Scientific Publishing. All rights reserved.
Singha, Ranjit; Singha, Surjit; Jose, Shruti; Muthu Ruben, V.; Haokip, Alphonsa Diana
Predicting, Preventing, and Mitigating Natural Disasters through Advanced Technologies;pp.139-174
https://doi.org/10.4018/979-8-3373-6415-ch005 https://www.scopus.com/pages/publications/105032359864?origin=resultslist
Singha R., Christ University, India; Singha S., Kristu Jayanti University, India; Jose S., Christ University, India; Muthu Ruben V., Christ University, India; Haokip A.D., Don Bosco College, Maram, India
Advanced Technological Improvements in Making of Smart Production Using AI and ML
Applications; Artificial Intelligence; Industry 4.0; Machine Learning; Smart Production; Sustainability; Systematic Literature Review
The necessity for adaptation and creativity in the manufacturing sector demonstrates the importance of sustainable manufacturing by the merging of advanced technologies. To encourage sustainability, a global view on the integration of smart manufacturing procedures is important. Artificial intelligence (or AI) has appeared as a crucial factor in achieving environmentally conscious manufacturing, with methods like the use of machine learning (ML) getting popularity. This study carefully studies the scientific papers related to the usage of AI and ML in business. The emergence of Industry 4.0 as a whole has positioned machine learning (ML) and artificial intelligence (AI) as drivers for the smart industry change. The study categorizes material based on release year, writers, scientific field, country, institution, and terms, applying the Web of Biology and SCOPUS databases. Utilize UCINET alongside NVivo 12 software, thereby the analysis covers empirical studies on machine learning (ML) and artificial intelligence (AI) via 1999 until the present, showing their growth before and after the start of Industry 4.0. Notably, the USA displays a substantial addition to this area, with a noticeable surge in desire following the rise of Industry 4.0. 2024 IEEE.
Praveenraj D.D.W.; Vijayaraj T.; Kumar K.S.; Sakthivel N.K.; Multani F.; Nagaveni P.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 178-183.
https://doi.org/10.1109/ICACITE60783.2024.10617114 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201804747&doi=10.1109%2fICACITE60783.2024.10617114&partnerID=40&md5=5bda97679cfbd0b5cc549dba6fe853f8
Praveenraj D.D.W., Christ University, School of Business and Management, TN, Bangalore, India; Vijayaraj T., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Mechanical Engineering, TN, India; Kumar K.S., Department of Electronics and Communication Engineering, Chennai, Tamil Nadu, Avadi, India; Sakthivel N.K., Nehru Institute of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nadu, T.M. Palayam, India; Multani F., Ies College of Technology, Department of Computer Science& Engineering, M.P, Bhopal, India; Nagaveni P., Karpagam Academy of Higher Education, Department of Electrical and Electronics Engineering, TN, India
Advancement and Challenges of Biosensing Using Field Effect Transistors
biosensors; Debye length; field effect transistors; functionalization; screening; sensitivity; specificity
Field-effect transistors (FETs) have become eminent electronic devices for biosensing applications owing to their high sensitivity, faster response and availability of advanced fabrication techniques for their production. The device physics of this sensor is now well understood due to the emergence of several numerical modelling and simulation papers over the years. The pace of advancement along with the knowhow of theoretical concepts proved to be highly effective in detecting deadly pathogens, especially the SARS-CoV-2 spike protein of the coronavirus with the onset of the (coronavirus disease of 2019) COVID-19 pandemic. However, the advancement in the sensing system is also accompanied by various hurdles that degrade the performance. In this review, we have explored all these challenges and how these are tackled with innovative approaches, techniques and device modifications that have also raised the detection sensitivity and specificity. The functional materials of the device are also structurally modified towards improving the surface area and minimizing power dissipation for developing miniaturized microarrays applicable in ultra large scale integration (ULSI) technology. Several theoretical models and simulations have also been carried out in this domain which have given a deeper insight on the electron transport mechanism in these devices and provided the direction for optimizing performance. 2022 by the authors.
All Open Access; Gold Open Access; Green Open Access
ISSN: 20796374; PubMed ID: 36005043; CODEN: BISSE
https://doi.org/10.3390/bios12080647 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137127533&doi=10.3390%2fbios12080647&partnerID=40&md5=5e69c15cc53200417800b8335ce06662
Thriveni G., Department of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Mysore Road, Kumbalgodu, Bengaluru, 560074, India; Ghosh K., Centre for Nanoelectronics and VLSI Design, Vellore Institute of Technology, Vandalur Kelambakkam Road, Chennai, 600127, India, Vellore Institute of Technology, School of Electronics Engineering, Vandalur Kelambakkam Road, Chennai, 600119, India
Advancements and challenges in deep learning for breast cancer screening: A review
Breast cancer screening; Deep learning (DL); Feature selection; Self-supervised learning; UNet architecture
Breast cancer continues to be the prevalent cancer on a global scale, playing a major role in the worldwide cancer statistics, the critical role of early detection in reducing death rates is underscored. In the context of breast cancer, screening, deep learning (DL) emerged as a game-changer, providing notable improvements over existing techniques. This review explores the use of DL in analysing images from various sources such as X-rays, ultrasound, magnetic resonance imaging, and biopsies. Additionally, it highlights DL's potential to pre-screen for cancer by integrating diverse data, including demographic information, biological markers, and meta-analytical risk assessments. The analysis reveals that deep learning frameworks, especially those optimized with feature selection techniques, attain the minimal false-negative rates, effectively distinguishing between patients with and without cancer. Notably, DL models demonstrate lower prediction uncertainty compared to traditional machine learning, as shown by reduced standard deviations in performance metrics. Additionally, the paper proposes a cascade network model that achieves 98.61% classification accuracy and a 98.41% F1 score by segmenting tumours with a UNet architecture and classifying them with a ResNet backbone. Despite these advancements, challenges such as limited annotated data and adaptability to new data domains persist. In response to these issues, the proposed Self AdaptNet leverages innovative self-supervised learning and adversarial techniques to improve the resilience as well as adaptability of BC detection models.AI technology, particularly DL-based systems, has the capacity to completely transform breast cancer screening by improving screening accuracy and reducing observer variability. However, clinical adoption requires standardized guidelines, trustworthy AI practices, and collaboration among researchers, clinicians, and regulatory bodies. 2026 Author(s).
Debnath, Rimpi; Paul, Jeno Lovesum Selvaraj; Thiyagarajan, Vigneshwaran
AIP Conference Proceedings;Volume;3345;Issue;1;Article No.;20267;
https://doi.org/10.1063/5.0298759 https://www.scopus.com/pages/publications/105028311957?origin=resultslist
Debnath R., Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India; Paul J.L.S., Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India; Thiyagarajan V., Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India
Advancements in Astronaut Health Monitoring Technologies
Biomarker Analysis; Cardiovascular performance; Cosmic radiation; Microgravity; Radiation Exposure Simulation
Astronautics also outlines unique biological and cognitive obstacles that demand advancements in health monitoring and techniques for risk prevention for space travellers. This study investigates the consequences of microgravity, space radiation and persistent confinement on astronaut well-being, focusing on cardiovascular, musculoskeletal, neurological and immune system vulnerability. Cardiovascular ailment, a major concern, is monitored using clinical prediction models (CPMs) that combine traditional risk factors, biomarkers and machine learning techniques. Additionally, AI-powered methods consisting of GPT-based models and time series transformers are required for real-time health monitoring and analytical assessment. Test-based outcomes illustrate that models such as logistic regression, random forest and support vector machines attain high designation accuracy in defining astronauts health hazards from non-astronaut data. Furthermore, wearable medical trackers and space-sourced clinical techniques are detected as an alternative solution for both space missions and terrestrial well-being. The study also highlights the need for perpetual advancements in zero gravity to protect astronauts well-being and enhance medicinal solutions for upcoming space travels. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Shinde, Gunjan; Anand, Garima; Singhal, Prateek
Lecture Notes in Networks and Systems;Volume;1684 LNNS;pp.98-111
Springer Science and Business Media Deutschland GmbH
https://doi.org/10.1007/978-3-032-08246-6_9 https://www.scopus.com/pages/publications/105028352853?origin=resultslist
Shinde G., Department of Computer Science, School of Sciences, Christ (Deemed to be University), Ghaziabad, Delhi-NCR, India; Anand G., Department of Computer Science, School of Sciences, Christ (Deemed to be University), Ghaziabad, Delhi-NCR, India; Singhal P., Department of Computer Science, School of Sciences, Christ (Deemed to be University), Ghaziabad, Delhi-NCR, India
Advancements in Automated Spine Disorder Detection Using CT Scans: A Decade of Progress (20142024)
CT scans; Deep learning; Medical imaging; Neural networks; Spine segmentation
Automated spine disorder detection has transformed a lot in the last decade, from classic segmentation techniques to advanced deep learning models. Remarkable developments can be noticed in this field, especially in developing hybrid architectures combining CNNs with LSTM networks to increase diagnostic accuracy. Recent implementations reach an accuracy of up to 97.46% and a precision of 99.72%, highlighting the achievement of impressive performance metrics by modern systems in detecting spinal deformity. Integrating U-net architectures for detecting accurate cervical spine fracture and developing two-tier detection pipelines which efficiently balance specificity and sensitivity are significant innovations. Early approaches concentrated on detecting basic anatomical features, and the latest methods comprise advanced deep learning models for comprehensive analysis. From traditional segmentation tasks to managing complicated challenges and iterative random walks, the field of automated spine disorder detection has improved significantly. However, issues regarding data standardization and model generalization persist, despite this growth. Future research should focus on the development of more robust, system-independent frameworks that are capable of handling various imaging conditions and patient populations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Manoj, Rakshitha A.; Warrier, Gayathry S.; Kokilavani, T.; Mahalakshmi, J.; Balakrishnan, C.; Stephen, R.
Lecture Notes in Networks and Systems;Volume;1671 LNNS;pp.443-454
https://doi.org/10.1007/978-981-95-3489-0_33 https://www.scopus.com/pages/publications/105035619108?origin=resultslist
Manoj R.A., CHRIST University, Karnataka, Bengaluru, India; Warrier G.S., CHRIST University, Karnataka, Bengaluru, India; Kokilavani T., CHRIST University, Karnataka, Bengaluru, India; Mahalakshmi J., CHRIST University, Karnataka, Bengaluru, India; Balakrishnan C., CHRIST University, Karnataka, Bengaluru, India; Stephen R., CHRIST University, Karnataka, Bengaluru, India
Advancements in battery and energy storage materials: Paving the way for sustainable energy solutions
Advanced materials are key to battery and energy storage technology improvements, which are a cornerstone of sustainable energy for the future and are the topic of this chapter. It explores advances in solid- state electrolytes, lithium- sulfuric and sodium- ion batteries, nanomaterials and organic compounds, which all have the potential to enhance energy density, cycle life and environmental sustainability. These materials hold great promise, as they may overcome current limitations in battery performance, safety, and cost, the authors say. The chapter also explores the economic and environmental implications of these innovations, spotlighting their role in the global transition to renewable energy. Given ongoing research efforts and favourable policies, next- generation energy storage systems will play an essential role in advancing clean energy technologies in areas from electric vehicles to electric grid storage. 2025, IGI Global Scientific Publishing. All rights reserved.
Josephine, Joy; Rebeka, R. Sreethi; Nadampalli, Babitha; Haokip, Alphonsa Diana; Singha, Surjit; Singha, Ranjit; Jose, Shruti
Innovations in Next-Generation Energy Storage Solutions;pp.39-74
https://doi.org/10.4018/979-8-3693-9316-1.ch003 https://www.scopus.com/pages/publications/105008488120?origin=resultslist
Josephine J., Good Shepherd English School, Karaikal, India; Rebeka R.S., Mount Carmel College, India; Nadampalli B., Christ University, India; Haokip A.D., Don Bosco College, Maram, India; Singha S., Kristu Jayanti College, India; Singha R., Christ University, India; Jose S., Christ University, India
Singha, Surjit; Singha, Ranjit; Jose, Shruti; Josephine, Joy; Sreethi Rebeka, R.; Nadampalli, Babitha; Haokip, Alphonsa Diana
https://doi.org/10.4018/979-8-3693-9316-1.ch003 https://www.scopus.com/pages/publications/105008743833?origin=resultslist
Singha S., Kristu Jayanti College, India; Singha R., Christ University, India; Jose S., Christ University, India; Josephine J., Good Shepherd English School, Karaikal, India; Sreethi Rebeka R., Mount Carmel College, India; Nadampalli B., Christ University, India; Haokip A.D., Don Bosco College, Maram, India
Advancements in Computational Modeling for Enhancing Financial Risk Management: Applications, Challenges, and Future Directions
Agent-based modelling; Anomaly detection; Computational modelling; Credit risk; Credit risk assessment; Ensemble methods; Ethical issues; Financial system; Fraud detection; Liquidity risk; Machine learning; Market risk; Monte carlo simulation; Natural language processing; Operational risk; Optimization; Risk control; Risk management; Risk management process; Stochastic modelling; Stochastic modelling and supervised learning
The advancement in risk management with deeper insights and more accurate predictions amidst complex data landscapes is attributed to computational modeling. It offers sophisticated tools to analyze, forecast, and mitigate risks in the dynamic financial market. This research article discusses integrating machine learning, network analysis, and other techniques to enhance risk identification, scenario analysis, and decision support in financial institutions. This article also addresses the importance of data quality, model validation, and transparency in ensuring the reliability and effectiveness of computational models. The application of machine learning techniques in credit risk assessment, market risk analysis, stress testing, scenario analysis, sensitivity analysis, portfolio management, and optimization is discussed. The study has demonstrated the conceptual model where identifying the type of risks is the first step, followed by sourcing the data internally and externally, considering the accuracyand reflection of current market conditions. Choosing the right computational techniques occupies an important stage due to the availability of both traditional and modern techniques. Traditional techniques are equally important to modern techniques, but this comes with challenges. Further risk management processes can be initiated to address the identified risks proactively and reduce potential financial losses. Finally, the study outlines future trends and technological advancements that promise to shape the future of computational modeling in financial risk management. 2025, Bentham Books imprint.
Arjun, B.S.; Ravinagarajan, Janani; Pachiyappan, Sathish; Thomas, Binoy
Computational Modelling Approaches to FinTech Innovation;pp.191-215
https://doi.org/10.2174/9798898810818125010013 https://www.scopus.com/pages/publications/105025272544?origin=resultslist
Arjun B.S., School of Business and Management, Christ University, Bangalore, India; Ravinagarajan J., Department of Commerce and Management, Shiv Nadar University, Chennai, India; Pachiyappan S., School of Business and Management, Christ University, Bangalore, India; Thomas B., Department of Business Management, Sahrdaya Institute of Management Studies, Kerala, Thrissur, India
Advancements in Cyber Security and Information Systems in Healthcare from 2004 to 2022: A Bibliometric Analysis
Bibliometric Analysis; Citation Analysis; Cyber Security; Healthcare; Information Systems
The main goals of the multifaceted healthcare system were to prevent, identify, and treat illnesses or conditions that affect human health. As the usage of IT in healthcare increased, the complexities in managing the IT infrastructure also increase, emphasizing the need of robust cyber security systems. The study aims to emphasize the advancements made in cyber security and information systems in healthcare, based on bibliometric analysis. 5,487 document's metadata was obtained from Scopus and data was analyzed using Vos Viewer. Ranking of articles was done with average yearly citations of the publications. Bibliometric analysis was performed based on 'bibliographic coupling of countries', 'co-occurrence of all keywords', 'author-based co-authorship', and 'term co-occurrence based on text data'. It was found that United States had the maximum publications (1337). 'Department of Information Systems and Cyber Security, The University of Texas at San Antonio, United States' is the most influential organization with 159 publications. IEEE Access is the most preferred platform for publication related to cyber security and information systems in healthcare (231 publications). 167 publications have received more than 100 citations. Choo K. K.R. is the most influential author with 185 publications. 2023 IEEE.
Proceedings of 2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023
Nair S.V.R., School of Business and Management, Christ University, Bangalore, India; Saha S.K., School of Business and Management, Christ University, Bangalore, India
Advancements in Deep Learning Techniques for Potato Leaf Disease Identification Using SAM-CNNet Classification
convolutional neural network; data normalization; GoogLeNet; potato leaf diseases; Spatial Attention Mechanism
Potato leaf diseases like Late Blight and Early Blight significantly challenge potato cultivation, impacting crop yield and quality worldwide. Potatoes are a staple for over a billion people and crucial for food security, especially in developing countries. The economic impact is substantial, with Late Blight alone causing annual damages over $6 billion globally. Effective detection and management are essential to mitigate these effects on agricultural productivity and economic stability. This paper presents a novel approach to potato leaf disease detection using advanced deep learning and optimization techniques. Key components include data normalization to eliminate noise, feature extraction using GoogLeNet, and hyperparameter tuning through the Elk Herd Optimizer (EHO). Additionally, a Spatial Attention Mechanism and Convolutional Neural Network (SAM-CNNet) are employed for robust classification. The method is validated using the Plant Village dataset, yielding an accuracy of 98.58%, with precision of 97.68%, recall of 98.42%, and F1-Score of 98.21%, demonstrating exceptional performance and reliability. This study highlights the proposed approach's efficacy in accurately identifying and classifying potato leaf diseases, offering a promising solution for precision agriculture and crop management. Copyright: 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license.
Patil S.; Satya A.D.V.; Bajjuri U.R.; Damarapati P.K.; Manur M.; Thirumalraj A.; Vatambeti R.
Ingenierie des Systemes d'Information, Vol-29, No. 5, pp. 2021-2030.
International Information and Engineering Technology Association
All Open Access; Hybrid Gold Open Access
https://doi.org/10.18280/isi.290533 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207450988&doi=10.18280%2fisi.290533&partnerID=40&md5=1a9b37d4fe16dfe4c3d8ee4fe647d79a
Patil S., Department of Information Technology, MLR Institute of Technology, Hyderabad, 500043, India; Satya A.D.V., Department of Chemistry, New Horizon College of Engineering, Bengaluru, 560103, India; Bajjuri U.R., Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, 521230, India; Damarapati P.K., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, 522302, India; Manur M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Thirumalraj A., Department of Computer Science and Engineering, K. Ramakrishna College of Technology, Trichy, 621112, India; Vatambeti R., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India
Advancements in e-Governance Initiatives: Digitalizing Healthcare in India
Contact tracing applications; e-Governance; EHR; ICT digital health
In order to improve the quality of service delivery to the public, to encourage interactive communications between government and citizens or government and business, and to address development challenges in any given society, information and electronic governance is the sophisticated fusion of a wide range of information and communication technologies with non-technological measures and resources. Digital technology advancements over the past ten years have made it possible to quickly advance data gathering, analysis, display, and application for bettering health outcomes. Digital health is the study and practice of all facets of using digital technologies to improve ones health, from conception through implementation. Digital health strategies seek to improve the data that is already accessible and encourage its usage in decision-making. Digital patient records that are updated in real-time are known as electronic health records (EHRs). An electronic health record (EHR) is a detailed account of someones general health. Electronic health records (EHRs) make it easier to make better healthcare decisions, track a patients clinical development, and deliver evidence-based care. This concept paper is based on secondary data that was collected from a variety of national and international periodicals, official records, and public and private websites. This paper presents a review of advancements for scaling digital health within Indias overall preparedness for pandemics and the use of contact tracing applications in measuring response efforts to counter the impact of the pandemic. The paper provides information about the government of Indias EHR implementation and initiatives taken toward the establishment of a system of e-governance. The document also covers the advantages of keeping EHR for improved outreach and health care. Further, this paper discusses in depth the effectiveness of using contact tracing applications in enhancing digital health. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
Lecture Notes in Networks and Systems, Vol-755 LNNS, pp. 287-296.
Das S., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bengaluru, India; Garg V., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bengaluru, India; Joseph J.J., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bengaluru, India