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Human activity recognition using wearable sensors
The advancement of the internet coined a new era for inventions. Internet of Things (IoT) is one such example. IoT is being applied in all sectors such as healthcare, automobile, retail industry etc. Out of these, Human Activity Recognition (HAR) has taken much attention in IoT applications. The prediction of human activity efficiently adds multiple advantages in many fields. This research paper proposes a HAR system using the wearable sensor. The performance of this system is analyzed using four publicly available datasets that are collected in a real-time environment. Five machine learning algorithms namely Decision tree (DT), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (kNN), and Support Vector Machine (SVM) are compared in terms of recognition of human activities. Out of this SVM responded well on all four datasets with the accuracy of 77%, 99%, 98%, and 99% respectively. With the support of four datasets, the obtained results proved that the performance of the proposed method is better for human activity recognition. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Hyperledger Fabric as a Secure Blockchain Solution for Healthcare 4.0 Framework
The healthcare sector deals with extremely sensitive information that must be administered in a safe and confidential way. The objective of the proposed framework is to utilize Blockchain Technology (BT) for tracking medical prescriptions and the implementation is carried out using the Hyperledger Fabric platform, an enterprise-grade open-source distributed ledger technology platform designed for Bigdata applications. Multiple entities, including patients, e-pharmacies, pharmacies, doctors and hospitals can establish connections by introducing several nodes in the Fabric chain. A web-centered application is provided for doctors, connecting them with participating pharmacies, hospitals and e-pharmacies through which, they can share patient prescription. Pharmacies and e-pharmacies have access to this data and can notify patients about the availability of prescribed medicines. Additionally, reminders for refills, such as heart medication, can be sent for patients requiring long-term medication. Patients can also try with nearby pharmacies and the availability of their prescribed medicines. The inclusion of a wallet feature in the application enables patients to use mobile tokens for making purchases. Patient data is treated with the utmost confidentiality, kept private, and accessed only upon request and with the consent of the concerned parties. This privacy is ensured through the use of zero-knowledge proof. Patients retain access to their complete medical history, facilitating interactions with doctors without the need for repetitive information sharing. 2023 IEEE. -
Smart Embedded Framework of Real-Time Pollution Monitoring and Alert System
The sustainability and progress of humanity depend on a clean, pollution-free environment, which is essential for good health and hygiene. Huge indoor auditorium does not have proper ventilation for air flow so when the auditorium is crowded the carbon di-oxide is emitted and it stays there for many days this may be a chance to spreading of COVID-19 and other infectious diseases. Without proper ventilation virus may present in the indoor auditorium. In the proposed system, emissions are detected by air, noise, and dust sensors. If the signal limit is exceeded, a warning is given to the authorities via an Android application and WiFi, and data is stored in cloud networks. In this active system, CO2 sensor, noise sensor, dust sensor, Microcontroller and an exhaust fan are used. This ESP-32 based system is developed in Arduino Integrated Development Environment (Aurdino IDE) to monitor air, dust and noise pollution in an indoor auditorium to prevent unwanted health problems related to noise and dust. More importantly, using IoT Android Application is developed in Embedded C, which continuously records the variation in levels of 3 parameters mentioned above in cloud and display in Android screen. Also, it sends an alert message to the users if the level of parameters exceeds the minimum and maximum threshold values with more accuracy and sensitivity. Accuracy and sensitivity of this products are noted which is very high for various input values. 2022 IEEE. -
IoT Based Enhanced Safety Monitoring System for Underground Coal Mines Using LoRa Technology
Extracting coal from Underground mine is a hazardous and tough job that needs continuous monitoring of environmental conditions to protect workers health and safety. Though some research works have explored wireless monitoring devices for underground mining, such as ZigBee and Wi-Fi technologies, they come with inherent restraints for instance restricted coverage, susceptibility to interference, reliability issues, security concerns, and high-power consumption. An Enhanced Safety Monitoring System for coal extraction from Underground Mines, employing LoRa communication technology for the effectual transmission of collected data to overcome existing challenges is discussed in this paper. The proposed system consists of two subsystems, one for monitoring the status of miners and another for comprehensive monitoring. LoRaWAN (Long Range Wide Area Network) is a multipoint protocol and this media access control (MAC) enables low-power devices to establish communication with Internet of Things (IoT) applications over extended wireless connections for long-range networks. LoRaWAN operates on lower radio frequencies, thereby providing a longer range of communication. This technology is known for its efficiency in optimizing LPWAN, offering extended range, extended battery life, robustness, and cost-effectiveness, making it highly suitable for industrial mining applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Neuroleadership strategies: Elevating motivation and engagement among employees
In the ever-evolving landscape of the modern era, organizations face the ongoing challenge of maintaining motivated and engaged employees. Despite the substantial body of research on this topic, many organizations still struggle to effectively promote engagement and motivation among their employees. This research aims to investigate the application of neuroleadership strategies in addressing this issue. The SCARF model, based on neuroscience principles, provides a valuable framework for understanding neuroleadership strategies which address social and emotional triggers that impact engagement and motivation. It can be effectively used to drive motivation and engagement in the workplace by addressing the fundamental social and emotional needs of employees. This study employs a quantitative approach which assesses the 321 employees from different organizations in India. The results of the study would provide leaders with practical insights to boost motivation and engagement in organizations and thereby improve the effectiveness of the organization. 2024, IGI Global. All rights reserved. -
Transforming workplace stress: The importance of neuroleadership for building resilient work environment
Workplace stress is a common issue that can significantly impact both employees and employers. This study explores the dynamic intersection of workplace stress and the emerging field of neuroleadership, offering insights into fostering resilient work environments. Drawing on the principles of neuroleadership, the chapter highlights how an understanding of neuroscience can inform leadership practices and contribute to creating resilient workplaces. This chapter discusses the neurological basis of stress and the role of leaders in mitigating its effects. It explores emotional intelligence in leadership and the impact of organisational culture on stress resilience. The chapter suggests practical interventions like mindfulness practices and supportive work environment initiatives grounded in neuroscience to cultivate a culture of wellbeing. By adopting resilient leadership strategies and understanding the neuroscience of stress, organisations can create environments that promote employee wellbeing and navigate the challenges of the modern workplace. 2024, IGI Global. All rights reserved. -
IoT Based Risk Monitoring System
The Internet of things (IoT) aims at connecting different objects, things using internet. The IoT is expanding rapidly and this motivates to apply for the food preservation domain such as preserve the standard of the veggies and fruits. In this paper we have worked on a cold storage system to analyze the environmental conditions under which the food item is being stored. The proposed system senses the temperature, moisture, gas parameters of surrounding environment as these parameters affect nutritional values of food items. An Arduino-based system is created and put into operation; it serves as both a central hub and a network layer for the refrigerated holding tank. It is also linked to the cloud, where an open-source application server supports digital storage functions. By establishing a connection to the database (DB) via its IP address, the measured variables are delivered to the base station (BS) from the cloud and stored there. Then, a cooperative sensing model that uses many observed information as input and one merged informational item or action to be performed as output is tried. As a result, numerous inputs, such as temperature and humidity, were combined and averaged to provide a tightly integrated result. Last, the system integrated an android mobile application which is used to facilitate user interaction and connect through IoT based system that is station or gateway and the internet. GPS is Used to track the remote cold storage and transport container live locations. 2022 IEEE. -
Enhancing Kubernetes Auto-Scaling: Leveraging Metrics for Improved Workload Performance
Kubernetes is an open-source production-grade container orchestration platform, that can enable high availability and scalability for various types of workloads. Maximizing the performance and reducing the cost are two major challenges modern applications encounter. To achieve this, resource management and proactively deploying resources to meet specific application requirements becomes utmost important. Adopting Kubernetes auto-scaler to fit one's needs are important to maximize the performance. This study aims to perform a comprehensive analysis of Kubernetes auto-scaling policies. This paper also lists out the various parameters considered for auto-scaling, and prediction methods used to efficiently handle resource requirements of applications. The research findings reveal a scarcity in the existing work regarding the variety of workload based auto-scaling and custom metrics. This paper provides a concise overview of a forthcoming research endeavor that explores the utilization of custom metrics in conjunction with auto-scaling. 2023 IEEE. -
Reinforcement Learning based Autoscaling for Kafka-centric Microservices in Kubernetes
Microservices and Kafka have become a perfect match for enabling the Event-driven Architecture and this encourages microservices integration with various opensource platforms in the world of Cloud Native applications. Kubernetes is an opensource container orchestration platform, that can enable high availability, and scalability for Kafkacentric microservices. Kubernetes supports diverse autoscaling mechanisms like Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA) and Cluster Autoscaler (CA). Among others, HPA automatically scales the number of pods based on the default Resource Metrics, which includes CPU and memory usage. With Prometheus integration, custom metrics for an application can be monitored. In a Kafkacentric microservices, processing time and speed depends on the number of messages published. There is a need for auto scaling policy which can be based on the number of messages processed. This paper proposes a new autoscaling policy, which scales Kafka-centric microservices deployed in an eventdriven deployment architecture, using a Reinforcement Learning model. 2022 IEEE. -
Detection of cyber crime based on facial pattern enhancement using machine learning and image processing techniques
Cybercrime has several antecedents, including the rapid expansion of the internet and the wide variety of users around the world. It is now possible to use this data for a variety of purposes, whether for profit, non-profit, or purely for the benefit of the individual. As a result, tracing and detecting online acts of terrorism requires the development of a sound technique. Detection and prevention of cybercrime has been the subject of numerous studies and investigations throughout the years. An effective criminal detection system based on face recognition has been developed to prevent this from happening. Principle component analysis (PCA) and linear discriminant analysis (LDA) algorithms can be used to identify criminals based on facial recognition data. Quality, illumination, and vision are all factors that affect the efficiency of the system. The goal of this chapter is to improve accuracy in the facial recognition process for criminal identification over currently used conventional methods. Using proposed hybrid model, we can get the accuracy of 99.9.5%. 2022, IGI Global. All rights reserved. -
Enhancements to greedy web proxy caching algorithms using data mining method and weight assignment policy /
International Journal of Innovative Computing, Information And Control, Vol.14, Issue 4, pp.1311-1326, ISSN No. 1349-4198. -
Enhancing greedy web proxy caching using weighted random indexing based data mining classifier /
Egyptian Informatics Journal, Vol.20, Issue 2, pp. 117-130, ISSN No. 1110-8665. -
Enhancements to Content Caching Using Weighted Greedy Caching Algorithm in Information Centric Networking
Information-Centric Networks (ICN) or Future Internet is the revolutionary concept for the existing infrastructure of the internet that changes the paradigm from host-centric networks to data-centric networks. Caching in Information-Centric Networks (ICN) has become one of the most critical research areas in today's world, especially for the leading in content delivery over Internet companies like Netflix, Facebook, Google, etc. This paper is intended to propose a novel Caching strategy called Weighted Greedy Dual Size Frequency for caching in Information-Centric networks. In this paper, the WGDSF considers multiple critical factors for maintaining the Web Content efficiently in ICN Caching Router. Simulation is done for the various performance metrics like Cache Hit ratio, Link load, Path Stretch, and Latency for WGDSF cache replacement algorithm, and results shown that WGDSF outperforms well compared with LRU, LFU, and RAND Caching Strategies. 2020 The Authors. Published by Elsevier B.V. -
Enhancements to randomized web proxy caching algorithms using data mining classifier model
Web proxy caching system is an intermediary between the users and servers that tries to alleviate the loads on the servers by caching selective web pages, behaves as the proxy for the server, and services the requests that are made to the servers by the users. In this paper, the performance of a proxy system is measured by the number of hits at the proxy. The higher number of hits at the proxy server reflects the effectiveness of the proxy system. The number of hits is determined by the replacement policies chosen by the proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The outcomes of the paper are proactive strategies that augment the traditional replacement policies with data mining techniques. In this work, the performance of the randomized replacement policies such as LRU-C, LRU-S, HARM, and RRGVF are adapted by the data mining classifier based on the weight assignment policy. Experiments were conducted on various data sets. Hit ratio and byte hit ratio were chosen as parameters for performance. Springer Nature Singapore Pte Ltd. 2019. -
An iot based wearable device for healthcare monitoring
Nowadays IoT (Internet of Things) devices are popularly used to monitor humans remotely in the healthcare sector. There are many IoT devices that are being introduced to collect data from human beings in a different scenario. These devices are embedded with sensors and controllers in them to collect data. These devices help to support many applications like a simple counting step to an advanced rehabilitation for athletes. In this research work, a mini wearable device is designed with multiple sensors and a controller. The sensors sense the environment and the controller collects data from all the sensors and sends them to the cloud in order to do the analysis related to the application. The implemented wearable device is a pair of footwear, that consists of five force sensors, one gyroscope, and one accelerometer in each leg. This prototype is built using a Wi-Fi enabled controller to send the data remotely to the cloud. The collected data can be downloaded as xlsx file from the cloud and can be used for different analyses related to the applications. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Spoken Language Identification using Deep Learning
A crucial problem in natural language processing is language identification, which has applications in speech recognition, translation services, and multilingual content. The five main Indian languages that are the subject of this study are Hindi, Bengali, Tamil, English, and Gujarati. A Deep Neural Network is introduced in the paper which is specifically made to use Mel-Frequency Cepstral Coefficients (MFCCs) for sophisticated language categorization. The suggested architecture of the model, which includes batch normalisation and tightly linked layers, helps it to be adept at identifying complex linguistic patterns. Comparing the research to the source work [18], promising improvements are shown, highlighting the potential of the model in language detection. 2024 IEEE. -
Wheat Yield Prediction using Temporal Fusion Transformers
In precision framing, Machine Learning models are an essential decision-making tool for crop yield prediction. They aid farmers with decisions like which crop to grow and when to grow certain crops during the sowing season. Many Machine Learning algorithms have been used to support agriculture yield prediction research, but it is observed that Deep Learning models outperform the benchmark Machine Learning algorithms with a significant difference in accuracy. However, though these Deep Learning models perform better, they are not preferred or widely used in place of Machine Learning models. This is because Deep Learning methods are black box methods and are not interpretable, i.e., they fail to explain the magnitude of the impact of the features on the output, and this is unsuitable for our use case.In this paper, we propose using Temporal Fusion Transformer (TFT), a novel approach published by Google researchers for wheat yield prediction viewed as a Time Series Forecasting problem statement. TFT is the state-of-the-art attention-based Deep Learning architecture, which combines high-performance forecasting with interpretable insights and feature importance. We have used TFT to perform wheat yield prediction and compare its performance with various Machine Learning and Deep Learning algorithms. 2023 IEEE. -
ANN Based MPPT Using Boost Converter for Solar Water Pumping Using DC Motor
The solar DC pump system is simple to set up and run completely on its own without the need for human intervention. Solar DC pumps require fewer solar panels to operate than AC pumps. Solar PV Arrays, a solar DC regulator, and a DC pump make up the Solar DC Pump system. The nonlinear I-V characteristics of solar cells, PV modules have average efficiency compare to other forms of energy, and output power is affected by solar isolation and ambient temperature. The prominent factor to remember is that there will be a significant power loss owing to a failure to correspond between the source and the load. In order to get the most power to load from the PV panel, MPPT is implemented in the converter circuit using PWM and a microcontroller. In order to give the most power to load from the source, the solar power system should be designed to its full potential. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Pollution forecast of united states using holt-winter exponential method
The United States is the world's most developed country and one of the top ten most air polluted countries in the world. Though the population is not very dense as in India or China, people face immense health problems. The US government is taking a lot of initiatives than any other government globally. However, it still faces issues. This paper mainly focuses on developing a forecasting model of the top four pollutants like SO2, NO2, CO, O3 that will help the country take necessary actions for the near future. This paper involves the secondary data of the daily pollution collected and merged for all states from 2007 to 2017. The forecast will throw the better output at the pollutants for the next four years, until 2021. The findings revealed that despite the increased GDP, the country had controlled the pollution level. NO2 has decreased to a better level. O3 and CO2 are also decreasing but has slight fluctuations. It will take some time to stabilize. SO2 had an increased level till 2017 and has started reducing afterwards. 2021 Ecological Society of India. All rights reserved. -
Impact of Lysinibacillus macroides, a potential plant growth promoting rhizobacteria on growth, yield and nutritional value of tomato plant (Solanum lycopersicum L. F1 hybrid Sachriya)
Plant growth promoting bacteria enhance the growth in plants by solubilizing insoluble minerals, producing phytohormones and by secreting enzymes that resist pathogen attack. The present study was aimed at identifying the potential of Lysinibacillus macroides isolated from pea plant possessing rich microbial rhizobiome diversity in promoting the growth of tomato plant (Solanum lycopersicum L.). Potential of L. macroides in the promotion of S. lycopersicum L. growth by increased shoot length, terminal leaf length and breadth was assessed. Anatomical sectioning of stem and root revealed no varied cellular pattern indicating that the supplemented bioculture is not toxic to S. lycopersicum. Plantlets treated with L. macroides along with organic compost showed an increased total phenol content (17.580.4 mg/gm) compared to control samples (12.440.41 mg/g). Carbohydrate content was noticed to be around 1.3 folds higher in the L. macroides plus compost mixture supplemented slots compared to control sample. Significant increase in shoot length was evident in the L. macroides plus compost supplied slots (23.42.7 cm). Plant growth promoting properties might be due to the nitrogen fixing activity of the bacteria which enrich the soil composition along with the nutrients supplied by the organic compost. Rich microbial rhizobiome diversity in pea plant and the usage of L. macroides from a non-conventional source improves the diversity of the available PGPR for agricultural practices. Further research is needed to detect the mechanism of growth promotion and to explore the plant microbe interaction pathway. Jyolsna et al. (2021).