Browse Items (2150 total)
Sort by:
-
Machine Learning's Transformative Role in Human Activity Recognition Analysis
Human action recognition (HAR) is a burgeoning field of computer vision that seeks to automatically understand and classify the intricate movements performed by humans. From the graceful leaps of a ballerina to the decisive strides of a surgeon, HAR aims to decipher the language of motion, unlocking a plethora of potential applications. This abstract delves into the core of HAR, highlighting its key challenges and promising avenues for advancement. We begin by outlining the various modalities used for action recognition, such as RGB videos, depth sensors, and skeletal data, each offering unique perspectives on the human form. Next, we delve into the diverse set of algorithms employed for HAR, ranging from traditional machine learning techniques to the burgeoning realm of deep learning. We explore the strengths and limitations of each approach, emphasizing the crucial role of feature extraction and model selection in achieving accurate recognition. Challenges in Human Action Recognition (HAR), such as intra-class variations, inter-class similarities, and environmental factors. Ongoing efforts include robust feature development and contextual integration. The paper envisions HAR's future impact on healthcare, robotics, video surveillance, and augmented reality, presenting an invitation to explore the transformative world of human action recognition and its potential to enhance our interaction with technology. 2024 IEEE. -
Machine Learningcloud-Based Approach to Identify and Classify Disease
The term "Internet of Things"(IoT) describes the process of creating and modeling web-related physical objects across computing systems. IoT-based healthcare applications have offered multiple real-time products and benefits in recent years. For millions of people, these programmers provide hospitalization can get regular medical records and healthy lives. The introduction of IoT devices in the health sector has several technological developments. This study uses the IoT to construct a disease diagnostic system. Wearable sensors in this system initially monitor the patient's sympathy impulses. The impulses are then sent by a network environment to a server. In addition, a new hybrid approach to evaluation decision-making was presented as part of this research. This technique starts with the development of a set of features of the patient's pulses. Based on a learning approach qualifications are neglected. A fuzzy neural model was used as a diagnostic tool. A specific diagnosis of a particular ailment, such as the diagnosis of a patient's normal and abnormal pulse or the assessment of insulin issues, would be modeled to assess this technology. 2022 IEEE. -
Malicious Traffic Classification in WSN using Deep Learning Approaches
Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. 2023 IEEE. -
Malicious URL Detection Using Machine Learning Techniques
Cyber security is a very important requirement for users. With the rise in Internet usage in recent years, cyber security has become a serious concern for computer systems. When a user accesses a malicious Web site, it initiates a malicious behavior that has been pre-programmed. As a result, there are numerous methods for locating potentially hazardous URLs on the Internet. Traditionally, detection was based heavily on the usage of blacklists. Blacklists, on the other hand, are not exhaustive and cannot detect newly created harmful URLs. Recently, machine learning methods have received a lot of importance as a way to improve the majority of malicious URL detectors. The main goal of this research is to compile a list of significant features that can be utilized to detect and classify the majority of malicious URLs. To increase the effectiveness of classifiers for detecting malicious URLs, this study recommends utilizing host-based and lexical aspects of the URLs. Malicious and benign URLs were classified using machine learning classifiers such as AdaBoost and Random Forest algorithms. The experiment shows that Random Forest performs really well when checked using voting classifier on AdaBoost and Random Forest Algorithms. The Random Forest achieves about 99% accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Malpractice Detection in Examination Hall using Deep Learning
Various institutions administer tests at designated examination locations, chosen third-party and approved centers, and have established standards for installing CCTV cameras and conducting frisking under the supervision of designated personnel. Some institutions are using online proctoring, which enables students to take exams from any location. In all of the aforementioned scenarios, human monitoring is conducted, and maintaining a high level of vigilance may be challenging due to administrative oversight or intentional allowance of malpractice for personal gain. The malpractice detection may be attributed to acts like as plagiarism, unauthorized sharing of papers, and non-verbal communication. The study is conducted by capturing the dataset in the classroom of Christ University. The proposed approach is based on the YOLO framework. The movies are processed in real time to identify hand rotation, paper extraction, and classify the motion. The accuracy for the Head_right class is significantly higher than that of the Head_left class. The system is implemented using the programming language Python and has the potential for future expansion to provide real-time monitoring. 2024 IEEE. -
Managing with Machines: A Comprehensive Assessment on the Use of Artificial Intelligence in Organizational Perspectives
This complete study, delves into the multifaceted impacts of artificial Intelligence (AI) inside organizational settings, highlighting its ability and demanding situations. The investigation spans numerous aspects along with AI-driven customer relationship management (CRM), employee productivity, and overall performance enhancement thru AI. By analyzing distinct AI applications and methodologies across different organizational functions, this studies presents insights into how AI can transform industries, decorate CRM, improve employee productiveness, and foster sustainable development. Despite the promising programs, the study also addresses the pitfalls and enormous hesitancy in AI adoption due to disasters in some high-profile AI projects. The paper underscores the significance of strategic AI integration, context-consciousness, and the want for organizational readiness to leverage AI's full capability whilst aligning with the Sustainable improvement goals (SDGs). 2024 IEEE. -
Manta Ray Foraging Optimizer with Deep Learning based Malicious Activity Detection for Privacy Protection in Social Networks
Malicious activity detection is a vital component of ensuring privacy protection in social media networks. As users engage in online interactions, protecting their sensitive information becomes paramount. Social networks can proactively identify and mitigate malicious behaviors, such as cyberbullying, data breaches, and phishing attacks by applying advanced AI and machine learning (ML) technologies. This detection system analyzes user behavior patterns, content, and network traffic to flag suspicious activities, thus safeguarding user privacy and fostering a safer online environment. The incorporation of robust malicious activity detection mechanisms helps maintain trust in social networks and reinforces the commitment to preserving user privacy in an increasingly interconnected digital landscape. This article introduces a novel Manta Ray Foraging Optimizer with Deep Learning based Malicious Activity Detection (MRFODLMAD) technique for privacy protection in social networks. The drive of the MRFODL-MAD technique is to detect and classify malicious activities in the social network. To accomplish this, the MRFODL-MAD technique preprocesses the input data. For malicious activity detection, the MRFODL-MAD technique employs long short term memory (LSTM) system. The MRFO algorithm has been executed to hyperparameter tuning process to improve the performance of the LSTM network. The experimental outcomes of the MRFODL-MAD algorithm can be tested on social networking database and the results inferred the improved performance of the MRFODL-MAD algorithm under various different measures. 2023 IEEE. -
Mapping extinction using GALEX and SDSS photometric observations
The primary objective of this work is to create an all sky extinction map of the Milky Way galaxy. We have cross-matched the Sloan Digital Sky Survey (SDSS data release 8) photometric observations with that of Galaxy Evolution Explorer (GALEX data release 6). This provides a wide range of wavelength coverage from Far Ultra-Violet through the optical spectrum and gives one unique SDSS source for every GALEX source. We discuss a sample of ?32000 objects in the north galactic pole (?75 latitude) from this combined database. The Castelli and Kurucz Atlas was fit to the photometric observations of each star, best fit being determined using a chi-square test. Best fit parameters provide the spectral type and extinction towards each of the objects. The shift in magnitude obtained during the best-fit can be used to determine the distance to each of the stars. With this data, a comprehensive extinction map can be made for the high-latitude objects and later extended to all-sky. 2013 AIP Publishing LLC. -
Mapping of built-up area and change detection in bengaluru using semi-automatic classification
Built-up areas are ever-increasing in nature to cater to the growing population's needs due to the migration of people to urban areas. Indian cities are under stress due to unplanned developmental activities. Land use and the land cover pattern are critical to maintaining the balance of various resources. In this study, Spatio-temporal changes have been mapped from 1989 to 2022 for the Bengaluru urban region. Geospatial techniques have been adopted to map land use, land cover changes and urban growth. Passive remote sensing data sets, which are freely available, were used in this study. QGIS and ESRI's ArcGIS software packages analysed the satellite images. Vegetation indices such as the Normalised vegetation index (NDVI), Normalised Difference Water index (NDWI), and Normalised difference Built-up index (NDBI) have been used along with supervised and unsupervised classification techniques. Images were classified into water bodies, vegetation, built-up area and others. It has been observed that there is an increase in the built-up area decrease in vegetation and water bodies. As per this study, policymakers and society need to consider the conservation of natural resources and developmental activities for sustainable development. 2023 Author(s). -
Mapping the Field of Research; Computational Intelligence and Innovation
This paper measures and maps the past studies in the field of Computational Intelligence and Innovation and further understand the application of Computational Intelligence in the field of study of innovation related to businesses. The bibliometric analysis shows the associations of various sub themes of research that was done between the period 2000 to Aug 2022. Scopus database is used to collect relevant documents of the field of study where 115 documents are sourced. The descriptive nature of the field of studies is analyzed in detail and further using VOS Viewer, the network analysis study is conducted to understand the association of authors, author country publication, themes and publication pattern, in detail. Further, an in-depth review analysis is done to understand the application of Computational Intelligence in the fields of Business Management and Social Science with aids innovation in the respective fields. Recent studies focus on machine learning, neural network, digital transformation, internet of things and other upcoming areas. The growth in these sub themes exhibit the multidisciplinary research happening in this field. This is paving way for future researchers to use the already found computing intelligence techniques to varied subject areas like medicine, management, economics etc., to foster innovation. 2022 IEEE. -
Mapping the Landscape of Business Intelligence Research: A Bibliometric Approach
The integration of Business Intelligence (BI) is an essential element in contemporary enterprises, facilitating the conversion of voluminous data into valuable insights to support informed decision-making. Consequently, a considerable body of literature has been devoted to investigating the utilization of Business Intelligence (BI) in enhancing company efficiency and competitiveness. The present investigation employs bibliometric methods as a means to examine the research pertaining to Business Intelligence (BI). This includes an examination of the main writers and universities, publication patterns, and the intellectual framework of the domain. This investigation centers on the timeframe spanning from 2000 to 2022 and scrutinizes a corpus of 3729 Scopus articles pertaining to business intelligence. The findings suggest that the domain of Business Intelligence (BI) has experienced a substantial expansion recently. The study's results reveal significant contributors, establishments, nations, and references in the discipline, along with developing research patterns and prospects for further investigation. In general, this research emphasizes the significance of bibliometric evaluation as a means of comprehending the present status of BI research and discovering approaches to enhance the utilization of BI in contemporary organizational decision-making procedures. This study has the potential to provide valuable insights into the present state of research within the field, pinpoint significant trends and themes, and highlight potential avenues for future research. 2023 IEEE. -
Marketing Research and Market-Focused Production as an Effective Business Tool in Power Sector
Businesses must devote part of their resources to conducting market and marketing research to make good decisions, which will help expand any business and utilize resources effectively. Understanding the intended clients is essential to successfully operating and expanding a firm. For marketers to comprehend consumer value about the product being supplied and therefore add value to their consumers, it is crucial to have this understanding. Organizations can better influence customers to buy niche goods or corporate services after thoroughly understanding their objectives, requirements, and values. In this situation, it is required to restructure the physical system and the related control and planning systems to provide production the tools it needs to become more competitive and customer-focused, acting as a positive and active production process instead of a reactive one. One of the finest techniques for understanding consumers is market research. It provides basic information that a company may utilize to inform its marketing strategy, facilitating and enhancing sales and marketing. This paper reviews the impact of effective market and marketing research and market-focused manufacturing in the power sector. 2023 IEEE. -
Markov analysis of unmanned cryogenic nitrogen plant with standby system
The unmanned cryogenic nitrogen plants operated by the industrial gas companies globally have their unique set of Reliability, Availability and Maintainability challenges. A generic reliability model of a typical unmanned cryogenic nitrogen plant is presented in this research work along with standby cryogenic storage System. The standby system is analysed for sensing and switching device as well as for load sharing system. The complexities of unmanned cryogenic nitrogen plant under repair with standby system are analysed using Markov method. A Markovian model has been developed for two different configurations: Configuration-1: Cryogenic nitrogen plant with gas and liquid production, nitrogen plant with only gas production and its dependence on the standby cryogenic storage system and to overall system reliability. Configuration-2: Considering cryogenic nitrogen plant is under repair and the standby system with external supply component under operation without failure and the system reliability of the configurations are solved by solving the set of differential equations and the solutions are presented in this paper. 2020 Author(s). -
MARS: Manual andAutomatic Robotic Sanitization onSocial Milieu
Sanitization is not a new term, but with the evolution of deadly COVID-19, the process came into the limelight quickly. The process was already utilized widely in hospitals, vaccination centers, food processing units, and medicine industries and suddenly became crucial in every domain related to our lives. Even though sanitization is considered the first line of defense against pandemic viruses like COVID-19, it is highly difficult to sanitize every nook and corner of bigger buildings and external structures like airports, railway stations, theaters, institutions, and hospitals. Slight carelessness to eliminate the virus from the sanitization process can reciprocate in the pandemic spread. Our proposed work deals with utilizing the accuracy and precision of robots to effectively sanitize bigger structures. The multi-faceted methodology of the work manages the comprehensive investigation of the robotic unit for the social setting. The concentrate additionally stretches out to refine the standard human behavioral reaction for modern robotic consideration in our lives. This will ease up the process and, at the same time, will reduce the chance of human error. The robotic structure is powered by a 12 V rechargeable battery, which has manual and automation cleaning modes. During manual mode, we control the robot with an Android application installed on the phone and connected with the robot through Bluetooth wireless connectivity. During automation, the mode robot moves in different directions and cleans and sanitizes the area independently. There is an ESP8266-based IoT connection unit to update the overall process for the cloud. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Martian Habitats: A Review
Establishing colonies in Lunar and Martian environments is the major task of our primary means to become a multi-planetary civilization. The Space Exploration Initiative (SEI), administered by President George H.W. Bush in 1989, was the first spark that ignited humanity's vision to establish space settlements beyond low Earth orbit (LEO) (Marc M. Cohen, 2015). At present, private space companies (like SpaceX and Blue Origin) are competing to be the first ones to colonise space. From the late 1980s to the present space race, many space habitat designs to suit human factors, ensure protection from space radiation, and be capable of regulating our day-to-day activities have been proposed for both lunar and martian settlements, respectively. In this paper, only Martian settlements are focused, and the reason for that follows next. While the moon is closer to Earth than Mars, Mars has several other advantages that make it an equal, if not a better candidate for colonisation. Some of the reasons why martian colonisation is preferred over lunar colonization include the presence of an atmosphere on Mars, its resource-rich nature, and its rotation period being closer to Earth's rotation period (Mars has 24.5 hours per day, while the moon has 28-day days) (Kamrun Narher Tithi, 2017). Another added advantage is its proximity to the main belt asteroids, which will further increase the potential for space mining in the future. So this paper will be a review of the various Martian habitat designs proposed over the last one and a half decades in terms of their designs, construction and challenges. To do so, it is assumed that every step associated with delivering the habitats to the Martian environment is achievable. These steps include the following: propulsion systems for long-term spaceflights; launch vehicles capable of lifting the habitats and fitting the habitat modules within them (Marc M. Cohen, 2015). Copyright 2023 by the International Astronautical Federation (IAF). All rights reserved. -
Masked Face Recognition and Liveness Detection Using Deep Learning Technique
Face recognition has been the most successful image processing application in recent times. Most work involving image analysis uses face recognition to automate attendance management systems. Face recognition is an identification process to verify and authenticate the person using their facial features. In this study, an intelligent attendance management system is built to automate the process of attendance. Here, while entering, a persons image will get captured. The model will detect the face; then the liveness model will verify whether there is any spoofing attack, then the masked detection model will check whether the person has worn the mask or not. In the end, face recognition will extract the facial features. If the persons features match the database, their attendance will be marked. In the face of the COVID-19 pandemic, wearing a face mask is mandatory for safety measures. The current face recognition system is not able to extract the features properly. The Multi-task Cascaded Convolutional Networks (MTCNN) model detects the face in the proposed method. Then a classification model based on the architecture of MobileNet V2 is used for liveness and mask detection. Then the FaceNet model is used for extracting the facial features. In this study, two different models for the recognition have been built, one for people with masks another one for people without masks. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Maximum Decision Support Regression-Based Advance Secure Data Encrypt Transmission for Healthcare Data Sharing in the Cloud Computing
The recent growth of cloud computing has led to most companies storing their data in the cloud and sharing it efficiently with authorized users. Health care is one of the initiatives to adopt cloud computing for services. Both patients and healthcare providers need to have access to patient health information. Healthcare data must be shared and maintained more securely. While transmitting health data from sender to receiver through intermediate nodes, intruders can create falsified data at intermediate nodes. Therefore, security is a primary concern when sharing sensitive medical data. It is thus challenging to share sensitive data in the cloud because of limitations in resource availability and concerns about data privacy. Healthcare records struggle to meet the needs of security, privacy, and other regulatory constraints. To address these difficulties, this novel proposes a machine learning-based Maximum Decision Support Regression (MDSR)-based Advanced Secure Data Encrypt Transmission (ASDET) approach for efficient data communication in cloud storage. Initially, the proposed method analyzed the node's trust, energy, delay, and mobility using Node Efficiency Hit Rate (NEHR) method. Then identify the efficient route using an Efficient Spider Optimization Scheme (ESOS) for healthcare data sharing. After that, MDSR analyzes the malicious node for efficient data transmission in the cloud. The proposed Advanced Secure Data Encrypt Transmission (ASDET) algorithm is used to encrypt the data. ASDET achieved 92% in security performance. The proposed simulation result produces better performance compared with PPDT and FAHP methods. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Measuring Consumer Perception for P2P Platform: NLP Approach
The pandemic has forced lenders and borrowers to switch to alternative borrowing., investment solutions. This research explores the Google reviews of users of four P2P lending platforms in India. To understand user sentiments and emotions about P2P lending platforms. The researchers has analysed user sentiments using Vader and Liu Hu methods and defined the polarity as positive or negative sentiment. Further., Plutchik's wheel of emotions was used to relate with the emotions expressed by the users. A purposeful random sampling method was used to select only 4 out of 21 registered P2P lending platforms based on their date of incorporation. The research also defined a framework for carrying out the sentiment analysis process for this study. The overall results showed that 75.51 % of users had positive sentiments., whereas., only 19.35% of users had negative sentiments about the P2P lending platforms. As most of the reviews posted were from the borrower's., emotion of joy was seen in all 4 platforms., followed by emotions of sadness., surprise., anger., disgust., and fear. 2022 IEEE. -
Mechanical and abrasive wear behaviour of waste silk fiber reinforced epoxy biocomposites using taguchi method
The aim of this research article is to study the static mechanical properties and abrasive wear behavior of epoxy biocomposites reinforced with different weight percentage of waste silk fibers. The effect of parameters such as velocity (A), load (B), fiber loading (C) and abrading distance (D) on abrasive wear has been considered using Taguchi's L25 orthogonal array. The objective is to examine parameters which significantly affect the abrasive wear of biocomposites. The addition of silk fiber has resulted in improved flexural properties of the epoxy matrix. The results of ANOVA indicated that the parameter which played a significant role was abrading distance followed by fiber loading, load and sliding velocity. 2019 Trans Tech Publications Ltd, Switzerland. -
Mechanical and tribological investigation on al lm4/tic composite fabricated through bottom pouring method
In the present investigation LM4 reinforced 6 wt% Titanium Carbide particles composite was developed by stir casting bottom pouring method. The cast composite specimen was obtained in a cylindrical shape of dimensions 50 mm dia and 100 mm length. The composite specimens were prepared for mechanical and tribological test as per ASTM standards. The obtained results reveal that the mechanical properties are high as compared to the as cast LM4 alloy specimens. Microstructure analysis confirms that the uniform distribution of TiC particles. Tribological test was performed using pin-on-disc machine based on Taguchi's design of experiments. L27 orthogonal array was selected by changing test parameter like applied load (10, 20, 30 N), sliding distance (600, 800, 1000 m) and sliding velocity (1.5 m/s, 2.5 m/s and 3.5 m/s). The most influencing test parameters were identified by using S/N ratio and ANOVA. The wear results reveled that wear rate increases as applied load increases, and it decreases with decrease in velocity. Also wear rate decreases as sliding distance increases and at some point, it became linear. The applied load was found to be most dominating (77.61%), sliding velocity (10.44%) and sliding distance (4.47%) are less dominating factors. Worn surface morphology was studied to understand the type of wear. 2021 elsevier ltd. all rights reserved.