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The future of urban life: The technological and humanistic dimensions of cognitive cities
A smart city implies realising sustainable city growth enabled by technology-based intelligent solutions to give its citizens a good quality of life. Information and communication technologies play a crucial role as the nerve centre of the smart city for collecting and analysing data from various sources, like mobile, social media, and sensors. The Internet of things (IoT) and big data (BD) also play a critical role in smart city infrastructures, changing how we analyse patterns and trends in human behaviour. Smart cities generate massive amounts of data and therefore need many flexible ways to process data and implement solutions. Recently, cognitive analytics have attracted the attention of researchers and practitioners worldwide as a technology-based innovative solution. It is a novel approach to information discovery and decision-making which uses multiple intelligent technologies such as statistical machine learning, deep learning, distributed artificial intelligence, natural language processing and visual pattern recognition to understand data and generate insights. A cognitive smart city refers to the convergence of emerging IoT and smart city technologies to realise cyber-physical social systems, their generated big data from sensing to communication and computing, and artificial intelligence techniques for all aspects of collaborative computing in sensors, actuators and human-machine interfaces. The field of humanities typically approaches the concept of cognitive cities from a cultural, philosophical, and humanistic perspective. Humanities scholars examine how cities shape our thoughts, beliefs, values, and experiences and how they impact our collective memory and identity. They consider the role of cities as sites of cultural production and consumption and explore the social and political implications of urbanisation and technological advancement. This paper aims to highlight the connection between technology and the humanities in the context of cognitive cities. The paper will explore the technological aspects of cognitive cities and their cultural, humanistic, and philosophical implications. 2023 Author(s). -
Effectiveness of performance appraisal systems in relation to teacher dedication in public and private secondary schools in zimbabwe
Performance appraisal systems need to be effective in improving or sustaining employee performance; otherwise, they are a sheer waste of time and money spent on their development and implementation. This study was an evaluation of the effectiveness of the current teacher performance appraisal system, in relation to teacher dedication to work, newlineas practised in Zimbabwean Secondary Schools. Since the introduction of the current teacher appraisal scheme in Zimbabwe in 2011, no research was carried out to determine whether it serves the purposes for which it was designed. Evaluating the effectiveness of the system encompasses a wide scope, including the perceptions of those appraised. The question that comes to the fore is,and#8214; What are teachers perceptions of the effectiveness of the current system of teacher appraisal as practised in public and private secondary schools in Zimbabwe?and#8214; Both quantitative newlineand qualitative methods of research were used to address the question. The study sought to establish the strength of the relationship that exist between the current teacher performance appraisal system and day to day newlineduties of the teacher, the extent to which it leads to improvements in the teaching and students learning process. It also seeks to establish how it addresses teacher development needs and whether the mechanisms and procedures for the management and implementation of the appraisal system in the schools are adequate. The current Performance Appraisal System, Result-Based Management and is output oriented. The main objective of this study was to assess the effectiveness of the current performance appraisal system on the performance of teachers in public and private secondary schools in Zimbabwe, by studying its implementation in five of the ten provinces. The overall purpose of the newlinestudy is to contribute to current policy and practice debate on how to improve and strengthen teacher performance appraisal and management system in Zimbabwe. -
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IoT-based traffic prediction and traffic signal control system for smart city
Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of 5 phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
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.