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Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems
Recently, intelligent transportations system (ITS) has gained significant internet due to the higher needs for road safety and competence in interconnected road network. As a vital portion of the ITS, traffic flow prediction (TFP) is offer support in several dimensions like routing, traffic congestion, and so on. To accomplish effective TFP outcomes, several predictive approaches have been devised namely statistics, machine learning (ML), and deep learning (DL). This study designs an optimal stacked sparse autoencoder based traffic flow prediction (OSSAE-TFP) model for ITS. The goal of the OSSAE-TFP technique is to determine the level of traffic flow in ITS. In addition, the presented OSSAE-TFP technique involves the traffic and weather data for TFP. Moreover, the SSAE based prediction model is designed for forecasting the traffic flow and the optimal hyperparameters of the SSAE model can be adjusted by the use of water wave optimization (WWO) technique. To showcase the enhanced predictive outcome of the OSSAE-TFP technique, a wide range of simulations was carried out on benchmark datasets and the results portrayed the supremacy of the OSSAE-TFP technique over the recent state of art methods. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model
At these times, internet of things (IoT) technologies have become ubiquitous in the healthcare sector. Because of the increasing needs of IoT, massive quantity of patient data is being gathered and is utilized for diagnostic purposes. The recent developments of artificial intelligence (AI) and deep learning (DL) models are commonly employed to accurately identify the diseases in real-time scenarios. Despite the benefits, security, energy constraining, insufficient training data are the major issues which need to be resolved in the IoT enabled medical field. To accomplish the security, blockchain technology is recently developed which is a decentralized architecture that is widely utilized. With this motivation, this paper introduces a new blockchain with DL enabled secure medical data transmission and diagnosis (BDL-SMDTD) model. The goal of the BDL-SMDTD model is to securely transmit the medical images and diagnose the disease with maximum detection rate. The BDL-SMDTD model incorporates different stages of operations such as image acquisition, encryption, blockchain, and diagnostic process. Primarily, moth flame optimization (MFO) with elliptic curve cryptography (ECC), called MFO-ECC technique is used for the image encryption process where the optimal keys of ECC are generated using MFO algorithm. Besides, blockchain technology is utilized to store the encrypted images. Then, the diagnostic process involves histogram-based segmentation, Inception with ResNet-v2-based feature extraction, and support vector machine (SVM)-based classification. The experimental performance of the presented BDL-SMDTD technique has been validated using benchmark medical images and the resultant values highlighted the improved performance of the BDL-SMDTD technique. The proposed BDL-SMDTD model accomplished maximum classification performance with sensitivity of 96.94%, specificity of 98.36%, and accuracy of 95.29%, whereas the feature extraction is performed based on ResNet-v2 World Scientific Publishing Company. -
Steering through the pandemic: narrative analysis of school leader experiences in India
The COVID ?19 pandemic has disrupted the regular functioning of schools. Transitioning to online learning posed significant challenges to all stakeholders in the educational system. The continued changes and challenges due to the pandemic require school leaders to make intuitive decisions. School leaders vision and leadership styles can considerably impact successfully managing crises and challenges. The current study looks at the lived experiences of eight school leaders working in India. The data collected using an interview guide was subjected to narrative thematic analysis. The interviews were designed primarily in an open-ended manner to captivate the story of their experiences. The results yielded an understanding of how school leaders navigated through multiple challenges such as transitioning online, attending to student needs, financial challenges adopting crisis and collaborative leadership. The results highlight various personal feelings and experiences that helped the school leaders to hold up during the crisis. School leaders lack training in crisis management, and their mental health needs are neglected. The paper calls for professional support for school leaders in managing professional and personal challenges. The article gives direction for school professionals on focus areas and requirements in Indian schools. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Steering through the pandemic: narrative analysis of school leader experiences in India
The COVID ?19 pandemic has disrupted the regular functioning of schools. Transitioning to online learning posed significant challenges to all stakeholders in the educational system. The continued changes and challenges due to the pandemic require school leaders to make intuitive decisions. School leaders vision and leadership styles can considerably impact successfully managing crises and challenges. The current study looks at the lived experiences of eight school leaders working in India. The data collected using an interview guide was subjected to narrative thematic analysis. The interviews were designed primarily in an open-ended manner to captivate the story of their experiences. The results yielded an understanding of how school leaders navigated through multiple challenges such as transitioning online, attending to student needs, financial challenges adopting crisis and collaborative leadership. The results highlight various personal feelings and experiences that helped the school leaders to hold up during the crisis. School leaders lack training in crisis management, and their mental health needs are neglected. The paper calls for professional support for school leaders in managing professional and personal challenges. The article gives direction for school professionals on focus areas and requirements in Indian schools. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Steering through the pandemic: narrative analysis of school leader experiences in India
The COVID ?19 pandemic has disrupted the regular functioning of schools. Transitioning to online learning posed significant challenges to all stakeholders in the educational system. The continued changes and challenges due to the pandemic require school leaders to make intuitive decisions. School leaders vision and leadership styles can considerably impact successfully managing crises and challenges. The current study looks at the lived experiences of eight school leaders working in India. The data collected using an interview guide was subjected to narrative thematic analysis. The interviews were designed primarily in an open-ended manner to captivate the story of their experiences. The results yielded an understanding of how school leaders navigated through multiple challenges such as transitioning online, attending to student needs, financial challenges adopting crisis and collaborative leadership. The results highlight various personal feelings and experiences that helped the school leaders to hold up during the crisis. School leaders lack training in crisis management, and their mental health needs are neglected. The paper calls for professional support for school leaders in managing professional and personal challenges. The article gives direction for school professionals on focus areas and requirements in Indian schools. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Accident prevention system using real time embedded technology
Two different aspects are presented in the proposed system: a transmitter and a recipient. The velocity boundary is controlled immediately after entering the emitter area by receiving a signal from the RF transmitter. A few meters even before the area, the significantly impacted might be put for this purpose. The surveillance program contains an alcoholic detector, an eye detector, and a smoke detector. GPS and GSM for the detection of incidents on mobile phones. The electromechanical device monitors the information as a consequence of the impact by transmitting it to the microprocessor ATmega330Q. GPS of your smart telephone will then communicate with both the satellite to acquire latitude and longitudinal data as well as the incident names will be transmitted to the families, fire departments, etc. which are already defined. 2021, SciTechnol, All Rights Reserved. -
Factors Influencing Data Utilization and Performance of Health Management Information Systems: A Case Study
The Healthcare Management Information System (HCMIS) is a comprehensive collection of data systematically gathered at healthcare institutions to fulfill the requirements for statistical information on medical services. This research aimed to assess the use of HCMIS information and identify the elements that impact the efficiency of the medical system at the district and primary medical institution levels in Tanzania as a case study. This research was conducted in 11 districts in Tanzania and included 115 healthcare institutions. It was cross-sectional research. The data were gathered via a semi-structured survey given to healthcare professionals at the institution and district stages. The information was then recorded utilizing an observational checklist. The researchers used an analytical technique for thematic content to combine and validate the replies and findings and gather essential data. 93 healthcare institution personnel and 13 district authorities were surveyed. Approximately 61% of the facility participants said they utilized the HCMIS information, but only 39% of the district participants acknowledged consistently analyzing HCMIS information. Out of the participants from nine districts, 68% said that they regularly get feedback on the quality of their work from authority figures monthly and quarterly. The patient workload was often shown to significantly impact the efficiency of staff members in data collection and administration. Insufficient analysis and subpar use of information were prevalent in most districts and healthcare institutions in Tanzania. Inadequate human and financial resources, absence of rewards and monitoring, and lack of standard processes for data handling significantly hindered the HCMIS efficiency in Tanzania. The Research Publication, www.trp.org.in. -
Unmasking the Masked: A Classical Machine Learning Pipeline for Detecting Forged Receipts
The abundance of digital and paper document forgery requires strong automated detection tools against financial fraud. This research provides a classical machine learning method for forged receipt detection using multimodal features from image and text modalities. The approach entailed designing a feature set to obtain textural and statistical attributes from receipt images via Local Binary Patterns (LBP) and Canny edge detection, along with structural features obtained from the associated text files. Another demanding issue in this area is the excessive class imbalance between genuine and forged documents. To overcome this issue, Synthetic Minority Over-sampling Technique (SMOTE) is used to create a balanced training dataset. The models are assessed using the macro F1-score, precision, recall, PR AUC and ROC AUC to address class imbalance. The enhanced detection of the minority class is achieved using SMOTE, while hyperparameter tuning leads to the improvements in performance. The final Tuned Support Vector Machine model achieves a macro F1-score of 0.5429, and it has the highest recall on forged receipts, demonstrating that it detects more histories of tampered documents effectively. This research sets a good baseline for receipt forgery detection and emphasizes that class imbalance solving is a key towards creating a working system. 2025 IEEE. -
THE CONFLICT CATALYST: IHL'S ROLE WHEN CLIMATE CHANGE TRIGGERS DISPLACEMENT
The escalating climate crisis is profoundly reshaping global human mobility, forcing millions to abandon their homes due to both sudden-onset disasters and insidious slow-onset environmental degradation. This paper examines how international humanitarian law (IHL) and climate-induced displacement are related, particularly when armed conflict intensifies or intersects with the consequences of climate change. Despite the informal usage of the term climate refugee, there is a significant protection gap because the 1951 Refugee Convention does not give it a legal designation. While IHL is primarily designed to regulate armed conflict and protect its victims, this research argues that its principles and provisions become indirectly, yet crucially, relevant when climate change acts as a threat multiplier, intensifying existing conflicts or creating new fragilities that lead to displacement. Through a qualitative legal analysis complemented by three diverse case studies, the Sunderbans (India and Bangladesh), the Lake Chad Basin, and Somalia/Horn of Africa. The paper aims to critically analyse the applicability and limitations of International Humanitarian Law in addressing climate-induced displacement, particularly in contexts where climate change acts as a threat multiplier for armed conflict. Through a case-based legal analysis, the article seeks to demonstrate how existing legal frameworks fall short of providing adequate protection for climate-displaced persons and to situate IHL within a broader matrix of human rights, migration, and climate governance regimes. 2025 Brawijaya Law Journal. -
Pertaining analysis of fracture risk in Osteoporotic patients using Machine Learning Techniques
Bone fractures in the spine or hip are the most severe complications of Osteoporosis. Older subjects with Osteoporosis are vulnerable to falls. This paper aims to review the breakthrough in machine learning methods over the past four years in assessing fracture risk in osteoporotic patients. Machine learning is applied in the healthcare and medical field. Machine learning professionals can accurately predict disease onset by analyzing a large amount of data. Osteoporosis is one of the healthcare domains in which new Machine learning and Artificial Intelligence techniques can be implemented. The objective of this research is to give an overview of the recent advancements in machine learning methods in finding out the risk factors for fractures or predicting the onset of disease. A systematic search was conducted in PubMed to get research papers published on Machine learning methods to detect, classify, or predict osteoporosis-related fracture risk. The articles belonging to Fracture prediction and risks (n=14), Osteoporosis classification(n=3), Diagnosis of fracture(n=3), and Predicting length of stay (n=1) were identified. The quality of the articles is assessed. Most articles described the efforts to create the model and showcased excellent results in predicting the risks. Significant limitations were in the form of inadequate data splitting and data validations. More validation studies are needed in various large groups to improve the model. Most of the participants in significant studies were in their initial stage of the disease, and the reproducibility analysis was done with major disease issues. 2023 IEEE. -
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Corporate Insolvency Resolution Process Under Insolvency and Bankruptcy : A Critical Study
A robust legal system is essential for corporations to carry on business smoothly. Previously in India, winding up and corporate rescue were dealt in multiple legislations for different entities. Provisions relating to winding up of companies were found in The Indian Companies Act 1913, Indian Companies Act, 1956 and further in the Companies Act, 2013. These provisions did not ensure expeditious winding up procedures and the same affected the interest of stakeholders. newlineThe increase in NPA compelled the need to bring an efficient framework to protect the rights of creditors and debtors. As a solution to this IBC was enacted in 2016, to facilitate timely resolution of insolvency and bankruptcy. This research aims to critically analyze the provisions of corporate insolvency resolution process, to examine whether IBC is facilitating newlinerehabilitation of insolvent corporations and protecting the interest of creditors, so as to balance their interests. It further aims to outline a draft policy for a better insolvency resolution process in India. Primary data for the study was collected through a structured interview of stakeholders and conclusion was drawn through a qualitative thematic analysis using NVivo software. The findings showed that, through CIRP there is debt recovery for financial creditors, but it is not expeditious. There are multiple reasons for the delay. The operational creditors are not able to newlinerecover from the process and as a result, many of them are turning insolvent. The rights of the corporate debtor are protected under the Code but they are not adequately protected under the Code as there is no value maximization under the Code. There are many delays in the process, resulting in more companies going into liquidation. IBC is a debtor friendly legislation. Both resolution and liquidation benefit the corporate debtor as it helps the company to resolve its newlineinsolvency. -
The future of urban transport integrating VANETs and blockchain for seamless mobility
The combination of VLC and blockchain technologies has immense potential to solve several problems that arise in urban infrastructures of smart cities. This chapter examines real- life instances and scenarios of the said integration beginning from accident prevention, traffic management to coordination of autonomous vehicles and smart parking services. VANET consists of V2V and V2I communication which promotes averting collisions, hazards and even allows an adaptive optimization of traffic in real- time environments. In addition, blockchain assists this by providing data preservation as well as guaranteeing security and accountability across all aspects of the construction through decentralized individual approaches, smart contracts, and incentivized mechanisms. Among the outlined features the one that follows from the federation of V2V devices is the accident prevention through secured emergency response systems which can be a part of blockchain ecosystem hence providing reliability in dispatch coordination. 2025, IGI Global Scientific Publishing. All rights reserved. -
Enhanced Digital Image Watermarking Using 3-Level Discrete Wavelet Transform (DWT)
This study compares the algorithm's performance to that of the DWT level 1 and level 2 techniques while proposing a digital picture watermarking technology using a 3-step Discrete Wavelet Transform (DWT). The suggested method uses alpha blending to overlay a multibit watermark into the frequency subband of the lower cover image. The watermark's appearance is controlled by the blending scale. For uniformity, watermark extraction uses the same scale factor. The 3-stage DWT approach is superior because the algorithm performs well for various scaling factors that are obtained in relation to statistical characteristics connected to the Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). 2025 IEEE. -
Cyber resilience through adaptive federated learning
This chapter explores the soon-to-be-reached intersection of cyber resilience and adaptive federated learning (FL), presenting a thorough examination of how FL, and especially through its adaptability mechanisms, can significantly enhance an organizations ability to predict, withstand, recover from, and learn from cyber-attacks. It explores the fundamental concepts of cyber resilience and FL, outlines several adaptive methodologies utilized in FL (e.g., adaptive client selection, aggregation, and optimization), and examines their direct contribution to the formation of resilient and privacy-aware cybersecurity systems. Real-world applications in critical infrastructures, accompanied by an honest review of current limitations and research avenues towards the future, present the revolutionary potential embedded within this synergy-based approach in an increasingly sophisticated and interlinked digital era. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors. -
Biomass Derived Fluorescent Nanocarbon Sensor for Effective Sensing of Toxic Cadmium Metal Ions
Cadmium ion (Cd2+) is common in our surroundings and may readily bioaccumulate into the organism following passage through the respiratory and digestive systems. Chronic exposure to Cd2+ can lead to considerable bioaccumulation in an organism because of its longer biological high life (1030 years), which permanently harms the health of humans and animals. Considering this hazardous effect of toxic Cd2+ metal ions, there is a need to develop a toxic-free and simple sensor synthesized from easily available and biocompatible biomass or natural precursor. Herein we report the effective synthesis and development of a fluorescence sensor from Indigofera tinctoria (L.), a well-known medicinal plant via one step green, hydrothermal synthesis method. The remarkable fluorescence and larger stokes shift make it ideal for fluorescence sensing strategy. This sensor detects potentially toxic Cd2+ assisting fluorescence sensing strategy in the metal ion concentration range from 1 nM to 1 M. The SternVolmer plot exhibits a remarkable linear detection range exhibiting limit of detection (LOD) as 14.74 nM. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Intelligent Safety Life Jacket Using Wsn Technology
The body loses heat in hypothermia because it cannot maintain its internal temperature owing to a freezing environment. As a result, the body temperature will decrease rapidly. The person will lose consciousness or faint when the body temperature falls below 35C. This study targets detecting climbers' hypothermia and transmitting their health status to the climber's group. It is difficult for mountain climbers to check their health and hypothermia symptoms for themselves and their climbing companions. To address this issue, we created a life jacket with an integrated hardware kit with a Peltier, temperature, and pulse sensor. LoRa Network is used to communicate with the climber's group. Alert messages are delivered to mountaineers via the Android app and suitable protocols, which helps save the climbers if any discrepancies occur. 2023 IEEE. -
Propensity Score Matching and a Difference in Difference Approach to Assess ESGs Influence on Indian Acquirer Performance
This research involves an in-depth analysis of the intricate relationship between Environmental, Social, and Governance scores and the financial and operational performance of Indian acquirers. The research methodology employed herein entails a meticulously crafted design, incorporating a blend of the Propensity Score Matching and Difference-in-Differences model. This strategic amalgamation serves to rigorously assess the impact of ESG factors on the performance outcomes of Indian acquirers involved in M&As. The empirical findings of this study reveal a robust and statistically significant correlation between M&A endeavours and ESG considerations. Notably, the research discerns that M&A activities tend to exert an adverse influence on ESG performance metrics within the Indian corporate landscape. This nuanced insight underscores the multifaceted interplay between strategic corporate actions and the broader sustainability and governance landscape, thereby offering valuable implications for scholars and practitioners in finance and corporate strategy. 2024, University of Wollongong. All rights reserved. -
Child safety gadget with home security
The Child Safety Gadget Home Security System introduces the initial five iterations of a technologically advanced system that addresses child safety and home security concerns by integrating innovative subsystems. We started with a primary password and incremented towards advanced functionality like a camera for visual verification, a lock mechanism for entry security, and finally, app integration. Finally, we extend the system with additional capabilities to fully implement security solutions based on features like an OLED display and sensors. The concealed pseudo-passcode alert system, which wrists parents up in emergencies to respond quickly, is central to our system. Our framework prioritizes cost-effectiveness, simplicity of scalability, and use effectiveness to provide an affordable solution that can protect children's safety while increasing the efficiency of intelligent sensor networks in home environments. 2026 selection and editorial matter, Dr. Poonam Nandal, Dr. Mamta Dahiya, Dr. Meeta Singh, Dr. Arvind Dagur, Dr. Brijesh Kumar. All rights reserved.