Browse Items (2150 total)
Sort by:
-
Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN
Brain illnesses are notoriously challenging because of their fragility, surgical complexity, and high treatment costs. Contrarily, it is not obligatory to carry out the operation, as the outcomes of the procedure may fall short of expectations. Adult-onset Alzheimer's disease, which causes memory loss and losing information to varied degrees, is one of the most common brain diseases. This will vary from person to person based on their current health situation. This highlights the need of using CT brain scans to classify the extent of memory loss and determine the patient's risk for Alzheimer's disease. The four main goals of Alzheimer's disease detection are preprocessing the data, extracting features, selecting features, and training the model with GP-ELM-RNN. The Replicator Neural Network has been utilized earlier for AD detection, however this study offers an improved version of the network, modified with ELM learning and the Garson algorithm. From this study, it is deduced that the proposed method is not only efficient, but also quite precise. In this research, GP-ELM-RNN network is built to four groups of images representing different stages of Alzheimer's disease: very mildly demented, mildly demented, averagely demented, and non-demented. The class of very mildly demented patients was found to have the highest accuracy (99.1%) and specificity (0.984%). As compared to the ELM and RNN models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE. -
Innovative Method for Detecting Liver Cancer using Auto Encoder and Single Feed Forward Neural Network
Liver cancer ranks sixth among all cancers in frequency of incidence. A CT scan is the gold standard for diagnosis. These days, CT scan images of the liver and its tumor can be segmented using deep learning and Neural Network techniques. In this proposed approach to identifying cancer cells, it's focus on four important areas: To enhance a photo by taking out imperfections and unwanted details. An ostu method is used for this purpose. Specifically, this proposed approach to use the watershed segmentation technique for image segmentation, followed by feature extraction, in an effort to isolate the offending cancer cell. After finishing the model training with AE-ELM. To do this, Extreme Learning Machine incorporates an auto encoder. To achieve effective and supervised recognition, the network's strengths of Extreme Learning Machine (ELM) are thoroughly leveraged, including its few training parameters, quick learning speed, and robust generalization ability. The auto encoder-extreme learning machine (AE-ELM) network has been shown to have a respectable recognition impact when the sigmoid activation function is used and the number of hidden layer neurons is set to 1200. According to the results of this investigation, a method based on AE-ELM can be utilized to detect the liver tumor. As compared to the CNN and ELM models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE. -
Inphase and outphase concentration modulation on the onset of magneto-convection and mass transfer in weak electrically conducting micropolar fluids
The paper analyses the effect of concentration modulation at the onset of solute magneto-convection and heat transfer in a weak electrically conducting fluid by carrying out a linear and non-linear analysis. The Venezian approach is assented encompassing the correction Solute Rayleigh number and wave numbers for meagre amplitude concentration modulation. A multiscale method is applied to convert the analytically untraceable Lorenz model to an analytically traceable Ginzburg-Landau equation which is solved to quantify mass transfer through Sherwood number. It is observed that concentration modulation results in sub-critical motion however out-of-phase concentration modulation is more stable compare to others. 2019 Author(s). -
Insights into Artificial Neural Network techniques, and its Application in Steganography
Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions. 2021 Institute of Physics Publishing. All rights reserved. -
Insights of Evolving Methods Towards Screening of AI-Enhanced Malware in IoT Environment
Internet-of-Things (IoT) has been encountering a series of potential form of threats since past half decades. Artificial Intelligence (AI), which is frequently seen to be adopted to solve various challenges in IoT operation, has now been adopted even by attackers for their malicious purposes. Of all forms of threats, AI-enhanced malwares are one of the most potential forms of threats which has its extensive effectiveness towards the complete operation of the entire IoT environment. Hence, this manuscript discusses existing detection and prevention approaches evolved in current literatures to understand various taxonomies of solution-based methodologies for circumventing such threats. The paper also contributes towards highlighting the potential open-ended issues that are yet to be addressed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Insights on the Optical and Infrared Nature of MAXI J0709-159: Implications for High-Mass X-ray Binaries
In our previous study (Bhattacharyya et al., 2022), HD 54786, the optical counterpart of the MAXI J0709-159 system, was identified to be an evolved star, departing from the main sequence, based on comparisons with non-X-ray binary systems. In this paper, using color-magnitude diagram (CMD) analysis for High-Mass X-ray Binaries (HMXBs) and statistical t-tests, we found evidence supporting HD 54786s potential membership in both Be/X-ray binaries (BeXRBs) and supergiant X-ray binaries (SgXBs) populations of HMXBs. Hence, our study points towards dual optical characteristics of HD 54786, as an X-ray binary star and also belonging to a distinct evolutionary phase from BeXRB towards SgXB. Our further analysis suggests that MAXI J0709-159, associated with HD 54786, exhibits low-level activity during the current epoch and possesses a limited amount of circumstellar material. Although similarities with the previously studied BeXRB system LSI +61? 235 (Coe et al., 1994) are noted, continued monitoring and data collection are essential to fully comprehend the complexities of MAXI J0709-159 and its evolutionary trajectory within the realm of HMXBs. 2024 Societe Royale des Sciences de Liege. All rights reserved. -
Insurance Data Analysis with COGNITO: An Auto Analysing and Storytelling Python Library
Data pre-processing has taken an enhanced role with the advent of Machine learning. It is a vital element that forms the encore of the data science and business analytics process. Data pre-processing involves generating descriptive statistical summary, data cleaning, and data manipulation based on inputs gained after the initial analysis. Of late, it has been observed that data science practitioners spend 45% to 50% of their time cleaning and processing the data. Much time can be saved if the data transformation process can be automated. The COGNITO framework helps in performing the automated feature engineering and data storytelling of the dataset based on end-user discretion. The present work discusses the process and results obtained when automated feature engineering was performed on an insurance dataset using COGNITO. 2021 IEEE. -
Integral Transforms andGeneralized Quotient Space ontheTorus
In this chapter, we discuss one of the recent generalization of Schwartz distributions that has significantly influenced the expansion of various mathematical disciplines. Here, we study the space of generalized quotient on the torus. Different integral transforms are investigated on the space of generalized quotients on the torus BS?(Td). The space BS?(Td) is made of both distributions as well as space of hyperfunctions on the torus. Further, by introducing the relation between the Fourier and other integral transforms, the conditional theorems are proved for generalized quotients on tours. Moreover, we study the convergence structure of delta-convergence on the generalized quotient space, and an inversion theorem is proved. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Integrated Automated Attendance System with RFID, Wi-Fi, and Visual Recognition Technology for Enhanced Classroom Security and Precise Monitoring
The integrated automated smart attendance system utilizes RFID, Wi-Fi, and visual recognition technologies to elevate classroom security and ensure precise monitoring of attendance records. It consolidates cutting-edge components such as RFID tags, ESP8266 Wi-Fi modules, ESP-32 CAM modules, solenoid locks, servo motors, and PIR sensors to devise a strong remedy. RFID technology enables accurate attendance tracking by assigning tags to students and faculty members. The Wi-Fi and visual recognition components enhance the system's functionalities, facilitating wireless connectivity, instantaneous data transfer, and validation of identities. Solenoid locks and servo motors ensure controlled access, responding to validated attendance records. PIR sensors detect motion, contrasting between genuine presence and proximity. The paper's methodology delineates the necessary hardware and software requirements, procedures for system initialization, testing phases, establishment of server connectivity, implementation of access control mechanisms, and formulation of end-of-session protocols. It highlights the successful integration and validation of hardware components, backend connectivity, identity confirmation, attendance recording, data encryption, and session termination procedures. The research aims to modernize attendance tracking in educational settings, improving efficiency, accuracy, and security while appreciating the need for further adaptation to suit diverse educational environments for broader adoption and sustained advancement. 2024 IEEE. -
Integrated Health Care Delivery and Telemedicine: Existing Legal Impediments in India
The technological innovation in the healthcare sector has contributed to the growth of telemedicine in India. Health services fall under State responsibility as per the Indian Constitution by virtue of Schedule 7although policy and planning framework are under the scope of Central government. Telemedicine cannot not work as an autonomous service, rather, ought to be subjected to different regulations having complex ethical, medico-legal manifestations. As far as India is concerned, Ministry of Health and Family Welfare of India (MoHFW) is the body responsible for initiating the policy of digitization of healthcare. However the point ishow far digital health services going appropriately in India. Based on NDHBs comprehensive architectural framework of Federated National Health Information System in January 2020 and as the pandemic strategy Medical Council of India and the NITI Aayog released new guidelines on telemedicine with respect to registered medical practitioners, this research needed to be checked. Thus, the examination was done in these aspects. Guidelines were revisited to see how the hospitals in Delhi and NOIDA function based on the records submitted in medical consultation given to patients using telemedicine. It is felt that telemedicine being a nebulous concept in India, it needs to be analyzed in the light of prospective opportunities it would offer. There is a need for collaborative approaches on digital health, revision in the prevailing legal and ethical frameworks, the clinical practices corresponding to standing medical guidelines. Also, it is found that there exist no uniform telemedicine practices balancing the privacy norms, medico-legal responsibility and regulatory standards. To arrive at conclusion, the best practices prevailed in other countries are examined and adopted. It is felt that the policies existing in telemedicine need to be bifurcated as digital consultation, digital photography, remote patient monitoring (RPM) separately. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Integrating AI and Cybersecurity: Advancing Autonomous Vehicle Security and Response Mechanisms
The rapid evolution of autonomous and connected vehicles has led to their integration with numerous technologies and software, rendering them vulnerable targets for cybersecurity attacks. While efforts have traditionally focused on preventing these attacks, the escalating risk underscores the importance of also vindicating their wallop. Nevertheless, this procedure is often onerous & facade scalability confronted, particularly due to connectivity issues in automobiles. This research advises a vehicle-based vibrant imposition response scheme, enabling swift responses to a variety of incidents and reducing reliance on external security centers. The classification encompasses an inclusive range of probable retorts, a procedure for evaluating retorts, & innumerable assortment approaches. Implemented on an embedded platform, the solution was evaluated using two distinct cyberattack use cases, highlighting its adaptability, responsiveness, volume for dynamic arrangement constraint alterations & nominal memory trail. Concurrently, this paper presents an innovative (AVSF) that synergistically integrates (AI) and cybersecurity techniques to fortify AV resilience against evolving threats. Additionally, the framework incorporates advanced cybersecurity measures such as encryption, authentication, and intrusion detection to mitigate vulnerabilities and safeguard critical AV systems. The fusion of AI and cybersecurity not only enhances AV security posture but also enables intelligent cyber threat monitoring and response capabilities. Extensive simulations and experimental evaluations demonstrate the efficacy of the AVSF in real-time scenarios, contributing to the development of robust security solutions for autonomous vehicle deployment and advancing safer transportation systems in the era of AI-driven mobility. 2024 IEEE. -
Integrating AI Tools into HRM to Promote Green HRM Practices
The image of Human Resource Management (HRM) is undergoing a drastic transformation. The conventional methods are evolving due to the emergence of technology, especially with the integration of Artificial Intelligence (AI) and data analytics into the HR processes. With the rapidly changing concept of the overall growth of an organization, AI is becoming a vital stimulant for sustainable growth. AI-powered tools promote data-driven decision-making for talent acquisition, performance management, workforce training and development, optimization of energy consumption and waste reduction. Green HRM aligns these efforts by integrating sustainability considerations into talent management strategies, nurturing employees eco-engagement, and promoting environmentally responsible practices within the workforce. This research paper aims to explore the synergies between AI tools and Green HRM practices, investigating how the integration of AI technologies into HR processes can contribute to the promotion of environmental sustainability. By examining real-world case studies, this study aims to investigate the potential of AI-powered solutions in shaping the future of HRM through the lens of sustainability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Integration of enterprise resource planning system as an effective technology for increasing business productivity
Enterprise Resource Planning (ERP) refers to a potential software, which organisations utilise for managing daily basis activities such as proper accounting, project management, compliance as well as procurement actions within organisational standards for achieving better business performance. This research focuses on understanding ways of ERP usage of businesses for enhancing potential procurement as well as accounting for assuring best performance achievement. Literature from different company reports and other sources has been implemented that brings out an understanding of productivity optimisation of organisations using ERP. It also focuses on illustrating different types of ERP along with assuring better data visibility aspects of the ERP usage for allowing consumers to view real time data while progressing with business relationships and enabling higher procurement standards. The research aims to investigate ways in which different types of ERP are used by organisations for assuring better accounting performance and procurement standards in their marketing environment. Hypothesis is a positive association between ERP utilisation and implementation in organisation and its accounting and procurement standards, achieving high performance in the competitive market. Methodology used in this research involves Exploratory research design with a probability sampling for bringing out best possible outcomes of the research. Sample sizes include secondary sources such as articles, journals and relevant company reports and databases for understanding ways in which ERP helps in attaining suitable accounting and procurement practices of businesses within organisational standards. Results as well as implications indicate an optimal relation of proper risk management through enhancing ERP and usage of most suitable ERP that assures best possible procurement and accounting practices for businesses to get competitive advantage in the market. 2024 Author(s). -
Intelligence-Software Cost Estimation Model for Optimizing Project Management
With the evolution of pervasive and ubiquitous application, the rise of web-based application as well as its components is quite rising as such applications are used both for development and analysis of the web component by developers. The estimation of software cost is controlled by multiple factors right from human-driven to process driven. Most importantly, some of the factors are never even can be guessed. At present, there are no records of literature to offer a robust cost estimation model to address this problem. Therefore, the proposed system introduces an intellectual model of software cost model that is mainly targets to perform optimization of entire cost estimation modeling by incorporating predictive approach. Powered by deep learning approach, the outcome of the proposed model is found to be cost effective in comparison to existing cost estimation modeling. 2019, Springer Nature Switzerland AG. -
Intelligent Agents System for Vegetable Plant Disease Detection Using MDTW-LSTM Model
When it comes to agricultural output, nation, India, ranks first in the world, and agriculture is unparalleled. The need to categorize and trade agricultural goods is paramount. Manual organization, which is tedious and laborious, is not a choice. When agricultural products are graded automatically, a lot of time is saved. The application of image processing techniques facilitates the examination and evaluation of the products. A technique for identifying diseased vegetables is the focus of this effort. Feature extraction, preprocessing, segmentation, and training the model are all heavily dependent on sequence. Among the preprocessing technologies at disposal are image segmentation and filtering. Using Kapur's thresholding based segmentation method, the image's sick areas can be located during the segmentation process. Use k-means clustering for feature extraction to identify vegetable plant diseases. The training of an MDTW-LSTM model relies heavily on feature selection. In terms of performance, the proposed method surpasses two cutting-edge algorithms: LSTM and DTW. The results showed an accuracy of 97.35 percent, indicating a remarkable improvement. 2024 IEEE. -
Intelligent approach to automate a system for simulation of nanomaterials
Nanomaterial composites are generally found to have great thermal properties and hence have witnessed an increasing demand in the recent years for manufacturing of efficient miniature electronic devices. The process of finding the right composites that exhibit the desired properties is a rather tedious task involving a lot of trial and error in the current scenario. This paper proposes a methodology to digitize and automate this entire process by administering certain efficient practices of assessing the properties of nanomaterial like Coarse Grained Molecular Dynamics thus resulting in faster simulations. 2023 Author(s). -
Intelligent Approaches of Clinical and Nonclinical Type-1 Diabetes Data Clustering and Analysis
Every year in India, there are nearly 15,600 fresh cases being reported among these age groups. In 2011, in the United States, 18,000 children under 15 were newly reported for T1DM. Over 13years, the Karnataka state government has a list of records showing that out of 100,000, 37% of boys and 40% of girls are affected by T1DM Disease. This paper investigates two methodologies to identify significant details about Type-1 diabetes. The first methodology is applicable to clinical data. The second methodology is demonstrated for the NDA T1D dataset. The dataset is utilized further to apply machine learning techniques to group similar patient traits. Exploratory data analysis on the dataset has revealed significant information answering a few research questions. This analysis can be useful for India, China, and other countries with high populations. In this paper, a unique methodology based on Artificial Intelligence Technique is proposed for both clinical and non-clinical data. The Autoimmune Disease, Diabetes Type 1-T1D, is focused. Non Clinical data based on 2021 reports are collected to identify patterns. Substantial unique issues are addressed in this work which were never reported before. The knowledge generated can be helpful for creating new clinical datasets, methodology and new insights related to Type-1 diabetes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Intelligent Course Recommendation for Higher Education based on Learner Proficiency
A course recommendation provides valuable guidance and support to learners navigating their educational and career journeys. Artificial Intelligence paves the way for recommending higher education courses. In this article, a framework is proposed that uses different features like learners' interest, their past performance and mainly their family talent history. This framework emphasizes the Intelligence Robotic Course Recommendation System. The system is very helpful for the learner who don't have that much of an explorer of the current trends happening in the world. When the learners similarity knowledge interest is known with respect to real-world needs, the perfect higher education is suggested for them. This paper shows that the framework gives better results when using with artificial intelligence algorithms. 2023 IEEE. -
Intelligent Safety Life Jacket Using Wsn Technology
The body loses heat in hypothermia because it cannot maintain its internal temperature owing to a freezing environment. As a result, the body temperature will decrease rapidly. The person will lose consciousness or faint when the body temperature falls below 35C. This study targets detecting climbers' hypothermia and transmitting their health status to the climber's group. It is difficult for mountain climbers to check their health and hypothermia symptoms for themselves and their climbing companions. To address this issue, we created a life jacket with an integrated hardware kit with a Peltier, temperature, and pulse sensor. LoRa Network is used to communicate with the climber's group. Alert messages are delivered to mountaineers via the Android app and suitable protocols, which helps save the climbers if any discrepancies occur. 2023 IEEE. -
Intelligent Smart Waste Management Using Regression Analysis: An Empirical Study
The term deep learning is seen as an important part of artificial intelligence that allows the system to understand and make decisions without special human intervention. In-depth learning uses a variety of statistical models and programs that allow different computational properties to reach the highest point. It is estimated that the market development of artificial intelligence and technology for deep learning will amount to USD 500 billion by 2026. The use of advanced technology, such as neural networks, enables better image recognition and the use of automated processes for deep operations. The main purpose of the study is to understand the critical determinants of Deep Learning in Creating a better City through Intelligent Smart Waste Management, the major determinants cover: System usability scale, Implementation of RFID sensors and Optimizing route selection. The proposed work is that implementation of advanced tools like deep learning methodologies and machine learning tools can support in managing the waste in a smart way, this will enable in creating better cities, enhance the environment and support sustainable living. Smart cities today need to use tools like deep learning and other artificial intelligence to effectively manage waste. Smart vessels are mainly controlled and implemented, which makes it easier for users to open vessels, it is also suitable for storing solid and dry waste, but provides information on the total degree of filling, can share data and information with central waste management service, you can collect waste quickly and avoid flooding. To achieve this, governments, administrators and communities are introducing sensors that transmit data and information to the waste management company in real-time and take appropriate action. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.