Step-Wise Working Status for Next-Generation Smart Cities: Integrating Fog and Edge Computing Solutions L Sherin Beevi, V. Nehru, P. M. Joe Prathap, W. Vinil Dani Proceedings of International Conference on Visual Analytics and Data Visualization Icvadv 2025, 2025 In the fast changing environment of smart cities, combining fog and edge computing with powerful IoT devices like Hikvision cameras is revolutionising urban administration and public safety. Hikvision, a video surveillance systems pioneer, offers a variety of IP and analogue cameras, thermal imaging, and intelligent software platforms used for traffic management, public safety, environmental monitoring, and infrastructure security. These technologies, which combine high-definition video quality, advanced image processing, and cognitive analytics, enable real-time monitoring, event detection, and quick responses. Traffic cameras collect real-time data for vehicle counts and congestion management, while environmental sensors monitor air, water, and noise levels to aid in health warnings and pollution control. Smart meters and wearable health gadgets monitor energy, water, and health indicators to optimise resource management. Parking sensors and waste management sensors boost parking and rubbish collection efficiency. The integration of fog and edge computing processes data locally, lowering latency and bandwidth utilisation, while fog nodes aggregate data for thorough analysis. Cloud storage enables long-term data analytics, which improves decision-making for city planners and emergency responders. Hikvision's software, which includes HikCentral and iVMS-4200, combines video surveillance, access control, and intelligent traffic management to create a comprehensive urban security framework. These systems are crucial to public safety because they provide real-time monitoring, facial recognition, and motion detection. For city planners, such innovations provide essential insights for urban growth and sustainability programs. The integration of Hikvision video surveillance with fog and edge computing is a game-changing approach to smart city management, improving urban safety and efficiency while improving inhabitants' quality of life.
Denoising and segmentation of brain image by proficient blended threshold and conserve edge scrutinize technique Nehru Veerabatheran, Prabhu Venkatesan, Rakesh Kumar Mahendran Computational Intelligence, 2024 Abstract Unusual collection of mass tissue in human body is commonly refereed as tumor. The tumor when it is found in brain. These tumor cells have tendency to multiply and grow in rapid speed. The growth of tumors is generally uncontrollable in nature. Tumor in brain develops along the skull and it evolves to contact with the functioning of the brain. Brain tumor (BT) can be detected at the earlier stages with the help of MRI or CT scan techniques. These scanning techniques are proved to be efficient in detecting the tumor irrespective of its size. Brain tumor being a life‐threatening disease has to be diagnosed at the earliest before it turns to be malignant. The current research work focuses in proposing an efficient image processing techniques by processing the MRI images of human brain which is affected by brain tumor. This process is carried out in two stages as image denoising and image segmentation which are helpful in detecting and localizing the tumor affected region in human brain. Initially, the MRI image is read and preprocessed by converting the input image into a grayscale image and noise removed by involving the proposed method. Later, the proposed image is segmented using sobel edge detection method and the image is enhanced using the image enhancement techniques. It is achieved by using the proficient blended thresholding (PBT) Segmentation method. The performance of the proposed methods is evaluated using PSNR (peak signal noise ratio) and RMSE (root mean square error). The proposed conserve edge scrutinize (CES) filter achieved highest PSNR value. Then segmentation is evaluated by five metrices: Sensitivity, Specificity, Dice, Jaccard, and Accuracy.
Segmentation of brain tumor subregions with depthwise separable dense U-NET (DSDU-NET) V. Nehru, V. Prabhu International Journal of Imaging Systems and Technology, 2023 Brain tumors are one of the most dangerous medical conditions. The dispersed extremities and nonuniform structure of the tumors are the basis of why techniques of traditional segmentation have grown to be inefficient. Magnetic resonance imaging (MRI) is one of the most widely used scanning procedures for tumors. However, to ameliorate the survival rate, the detection of tumors alone does not suffice, and there are other effective procedures. One of the most pivotal procedures for diagnosing the condition is the process of the brain tumor segmentation is a laborious and cumbersome process. As a result, a deep learning (DL) based solution is used to extract tumor subregions like enhancing tumor (ET), tumor core (TC), and whole tumor (WT). The proposed model involves novel DSDU‐Net: Depth‐wise separable dense U‐NET (DSDU‐NET) that precise outputs are acquired by retaining the low‐level features. In the preprocessing stage, a methodology called multi‐scale patch extraction is used to segregate tumor regions, and a grouping of Gaussian filter, unsharp masking, and histogram equalization is carried out on the data set to get significantly better performance. The proposed DSDU‐NET network performed better in terms of performance, specificity, sensitivity, dice similarity index, and Hausdorff distance for segmented image sub‐regions when validated on BraTS 2018 and 2019 data sources when particularly in comparison to other models.
Empowering the Tribal people with the use of big data processing expert system in animal Husbandry and Poultry Farming application R. Saravanan, V. Nehru, S. Muthuselvi 2023 IEEE International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2023, 2023 The population of the tribal people has been decreased day by day in India due to the lack of awareness in health related issues and there is a series challenges in their sustainable livelihood. The Particularly Vulnerable People from tribal groups (PVTGs) engaged in animal husbandry and poultry farming as their primary source of income, which improves their standard of living. Maintain and safeguard poultry and animals from diseases is a cumbersome process. Providing enough medical facility is still a challenging task due to the geographical location and unavailability of the infrastructure and human resources. The proposed framework uses Apache Kafka-Apache Storm-NoSQL Mongo DB architecture to process enormous volume of sensor data in real time and it receives the sensor data and uses it to create the various disease identification models. The processed data are stored in Mongo DB as a historical data. The system provides a Web-based monitoring system for continuos monitoring the health conditions of cattles and poultry through the Smart Health Care Centre. Smartness in operation is performed through System on Chip (SoC) IoT system, the proposed big data expert system model transcends from the traditional functionalities of disease identification by the real time field visit analysis by the medical professionals. The proposed system is more suitable for the remote hill area. Smart Health Care system improves the disease identification accuracy and provides a powerful Big Data architecture for data analytics and data storage. The big data expert system frame work is underwent successful functional testing of "SoC-IoT smart devices" connected with the network and the performance of the network in terms of CPU, memory usage and the network delay is analyzed. Further the frame work uses the big data processing with the machine learning approach "Hybrid diseases identification Model" with the combination of DBSCAN for outlier detection together with Random Forest classification, which improves the disease identification accuracy of the various disease attacked the cattles and poultry.
Predictive analysis on brain tissue segmentation and knowledge extraction with RS-FCM algorithm International Journal of Scientific and Technology Research, 2020
Automatic detection and classification techniques of acute myelogenous leukemia (Aml) using svm and generic algorithm V.Nehru, Dr.N.D.Bobby, K.S.Rani, professor Giridhar Reddy, Dr.M.Anto Bennet International Journal of Recent Technology and Engineering, 2019 Majority of youngsters’ having connected online through internet either through computers or by smart phones. After the entry of Jio in the field of internet, the competition began and the cost of internet service became much cheaper andnoweveryone can afford the cost. Latest “Times of India” statistics shows around 59% of internet users are college students/young men. The trend of going to the physical stores to buy the product is in the decline stage where as the trend of surfing product specification as well as its cost and alternates through online marketing sites is increasing among youngsters. Since, it is more convenient for them to shop anywhere and anytime. Shopping can be done 24 x 7 and before buying; review of product performance through social media and compare its price through varies alternate sites. There is no compulsion to buy the product while surfingoreven if visited the siteforany number of times. The payment can be made through online transition and products will be delivered to doorstep.Hence shopping through online become a joyful experience and preferred by youngsters.
An adaptive unsupervised segmentation algorithm based on color-texture (CTEX) integration International Journal of Control Theory and Applications, 2016
Integrating MAPD in mobility architecture using MIPv6 R. Venkadesh, V. Nehru, K. Rajan 2013 4th International Conference on Computing Communications and Networking Technologies Icccnt 2013, 2013 This paper we propose if access routers in future all wireless networks are restricted to perform network layer functions only, we investigate the design of intelligent routers, called Mobility Anchor Points in Dynamic(MAPD), to implement peruser regional management in IP wireless networks. We investigate a proxy-based Service Management with Integrated Mobility Architecture (SIMA) under which a client-side proxy is created on a per-user basis to serve as a gateway between a MN and all services engaged by the MN. we analyze per-user regional registration schemes extending from Mobile IP Regional Registration and Hierarchical Mobile IPv6 for integrated mobility and service management with the goal to minimize the web traffic, network signaling and packet delivery cost in future all wireless networks. We show that there is an optimal "service area" under which the overall cost due to query processing, cache consistency management and mobility management is minimized.
