MCA

This research explores the integration of geospatial, IoT, and cybersecurity technologies to secure urban small water bodies. The study presents a comprehensive framework enabling real-time monitoring, data protection, and sustainable management of critical water resources. Using IoT sensors and geospatial mapping, the research emphasizes proactive security through encryption, authentication, and intrusion detection systems, ensuring data integrity and informed decision-making.

This research aims to develop a real-time water quality mapping and reporting system utilizing IoT sensors and GIS technology, with an emphasis on enhanced cybersecurity. By deploying IoT sensors across various water sources, continuous data collection on water quality parameters such as pH, turbidity, temperature, and pollutant levels is achieved. This data is integrated into a Geographic Information System (GIS) platform, enabling dynamic mapping, visualization, and analysis of water quality in real-time. The study underscores the critical role of cybersecurity in protecting the integrity and confidentiality of the collected data. Advanced security measures, including encryption, secure authentication protocols, and intrusion detection systems, are implemented to safeguard the system against cyber threats. The research highlights the significance of secure, real-time data-driven approaches in the sustainable management and monitoring of water quality.

This research article investigates the utilization of geospatial data reporting to inform higher-level authorities about low water levels in interconnected reservoirs within a secure environment. The aim is to combine real-time reservoir monitoring, geospatial information, and robust security measures to enhance the reporting of reservoir level changes. By incorporating advanced technologies such as IoT devices and geographic information systems (GIS), the research aims to improve the accuracy and timeliness of reporting to higher-level authorities. Emphasis is placed on integrating geospatial data into the reporting process and ensuring the security of sensitive reservoir information through secure communication channels, encryption, and access control measures. The proposed approach has the potential to significantly enhance decision-making and emergency response in reservoir management, providing higher-level authorities with real-time, geospatially enriched data for informed decision-making.

Live streaming has emerged as a powerful medium for content creation, social interaction, and digital broadcasting. However, its rapid growth has introduced severe cybersecurity threats, particularly deepfake technology and identity theft. Deepfakes, powered by artificial intelligence, enable malicious actors to manipulate orimpersonate digital anchors in real time, leading to misinformation, reputational damage, and fraud. Identity theft in live streaming further exacerbates these risks, as unauthorized access to personal data and account takeovers compromises the security of content creators and audiences alike. This paper explores the multifaceted cybersecurity challenges faced by live streamers, emphasizing the growing sophistication of deepfake attacks and identity fraud. The study also examines vulnerabilities in live streaming platforms, including weak authentication protocols, lack of real-time deepfake detection, and the risks associated with biometric data breaches. Existing countermeasures, such as AI-driven deepfake detection, blockchain-based identity verification, and multi-factor authentication, are analyzed for their effectiveness in mitigating these risks. Additionally, this paper highlights regulatory efforts and ethical considerations surrounding the use of AI in content creation. The findings suggest that a multi-layered cybersecurity framework is essential for protecting digital anchors from

In the digital age, governance systems across the globe are increasingly adopting technological innovations to enhance the efficiency, transparency, and responsiveness of public administration. This paper explores the role of digital governance in shaping and implementing economic policies, particularly in emerging and developing economies. Through the integration of digital tools such as e-governance platforms, big data analytics, and automated monitoring systems, governments are now better positioned to design, enforce, and evaluate economic initiatives with improved accuracy and speed. The study critically examines how digital governance enhances fiscal management, promotes inclusive policy delivery, and reduces corruption by minimizing bureaucratic layers. Drawing on secondary data, case studies, and empirical findings, the paper highlights both the opportunities and challenges associated with the digital transformation of public sector governance. The research concludes that while digital governance significantly improves the implementation of economic policies, its success is heavily dependent on institutional readiness, technological infrastructure, and public trust.

This paper presents an algorithm to remove random impulse noise in a digital grayscale image based on fuzzy logic approach. Numbers of filters are used to remove noise using fuzzy rules. Filter window used the local statistic to determine local weighted mean. Pixel that are detected noisy are filtered, other pixels are remain unchanged. Experimental results show that this method has better performance than other state of the art filters. The effectiveness of this method is 44.95 in terms of peak-signal-to-noiseratio.

Interpretation of the ECG waves plays a vital role in analysis of cardiovascular diseases. Therefore, many semi and fully-automatic approaches using advanced machine learning techniques for the ECG waves detection are being exhaustively investigated by the researchers. Regardless of advanced machine learning or deep learning techniques, present methods lacks generalization, robustness, reliability and real-time implementation. Moreover, none of the existing study had presented the information related to lead variability which is imperative aspects to handle for accurate detection of ECG waves. Hence, in this paper, we are presenting the new approach of ECG wave segmentation called semantic segmentation, a well-known concept in image segmentation. The proposed approach mainly includes; 1) hybrid channel-mix convolutional and bidirectional LSTM which able to extract temporal dependencies as well as short and long-time dependencies in forward and backward time stamps for semantic segmentation. 2) Experiments with channel-mix convolution to handle lead to lead variability, 3) Experiments with noisy dataset to increase the robustness, reliability and generalization. The proposed hybrid model is implemented on the standard publicly available QT database. The remarkable results with high accuracy ranging from 96 to 98.56 % with average and weighted accuracies of 96.72 % and 95.54 % respectively are obtained for segmentation of ECG waves from continuous raw ECG signal. There are 12.28 %, 8.08 % and 5.77 % increments in weighted average accuracy using proposed hybrid model than LSTM, BiLSTM and double BiLSTM respectively.

Experience plays a vital role in the decision-making (DM) process. In this paper simulation, modeling, and analysis of past experience over DM has been done using the Iowa gambling task (IGT). The Human DM process is very complex and difficult to model through computational methods because it is a subjective type of process and varies person-to-person. Therefore, this study is an attempt to simulate a DM model similar to the human DM process. For this collection of real data was done and was provided as input to the developed eight Reinforcement Learning (RL) models. The result shows that the performance of the model based on Prospect Utility (PU) learned with Decay Reinforcement Rule (DRI) and Trial Dependency Choice (TDC) is better as compared to other models. It is observed from the analysis of data and also validated that simulation and models output that the experienced group performs better than inexperienced.

A brain connectivity network (BCN) is an advanced approach for examining brain functionality in various conditions. However, the predictability of the BCN is affected by the connectivity measure used for the network construction. Various connectivity measures available in the literature differ according to the domain of their working data. The application of random connectivity measures might result in an inefficient BCN that ultimately hamper its predictability. Therefore, selecting an appropriate functional connectivity metric is crucial in clinical as well as cognitive neuroscience. In parallel to this, an effective network identifier plays a vital role in distinguishing different brain states. Hence, the objective of this paper is twofold , which includes identifying suitable connectivity measures and proposing an efficient network identifier. For this, the weighted BCN (WBCN) is constructed using multiple connectivity measures like correlation coefficient (r), coherence (COH), phase-locking value (PLV), and mutual information (MI) from electroencephalogram (EEG) signals. The most recent technique for feature extraction, i.e., weighted ordinal connections, has been applied to EEG-based BCN. EEG signals data has been taken from the schizophrenia disease database. Further, several classification algorithms such as k-nearest neighbours (KNN), support vector machine (SVM) with linear, radial basis function and polynomial kernels, random forest (RF), and 1D convolutional neural network (CNN1D) are used to classify the brain states based on extracted features. In classification, 90% accuracy is achieved by the CNN1D classifier with WBCN based on the coherence connectivity measure. The study also provides the structural analysis of the BCN.

Traditionally edges have been loosely defined as pixel intensity discontinuities with in an image. First order linear filters and second order linear filters are most widely used algorithms for detection of edges but all these algorithms are sensitive to noise. Edges can be finding using fuzzy logic algorithm with different levels of processing. Edge detection is one of the basic requirements for biometric based human identification system. In this paper we are trying to detect the edges of images by using fuzzy logic in MATLAB environment without determining threshold value. Fuzzy inference system is successfully developed which corresponds to nine input pixels of matrix values with one output and can detect edges of the images. Experimental results are obtained and shown.