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Restructuring of Layout Designs and Operational Processes in Production Lines of Manufacturing Companies Globally to Compete Post Pandemic Conditions
Manufacturers globally faced various challenges in terms of sustainability and continuous production in the past COVID conditions and it showed the importance of redesigning the existing processes. All new manufacturing processes should be designed by considering the pandemic situation in the future. The present study is focused on restructuring the layout of an existing production unit in order to cope with any such eventualities. Various models that are suitable for adoption in post-pandemic, have been proposed and their efficiencies are compared in this paper. The authors also investigate how such changes will impact the efficiency of the existing line. Key parameters considered in this paper are the total production hours, line efficiency, balancing delay, and production rate. 2023 ACM. -
Advancements in Cyber Security and Information Systems in Healthcare from 2004 to 2022: A Bibliometric Analysis
The main goals of the multifaceted healthcare system were to prevent, identify, and treat illnesses or conditions that affect human health. As the usage of IT in healthcare increased, the complexities in managing the IT infrastructure also increase, emphasizing the need of robust cyber security systems. The study aims to emphasize the advancements made in cyber security and information systems in healthcare, based on bibliometric analysis. 5,487 document's metadata was obtained from Scopus and data was analyzed using Vos Viewer. Ranking of articles was done with average yearly citations of the publications. Bibliometric analysis was performed based on 'bibliographic coupling of countries', 'co-occurrence of all keywords', 'author-based co-authorship', and 'term co-occurrence based on text data'. It was found that United States had the maximum publications (1337). 'Department of Information Systems and Cyber Security, The University of Texas at San Antonio, United States' is the most influential organization with 159 publications. IEEE Access is the most preferred platform for publication related to cyber security and information systems in healthcare (231 publications). 167 publications have received more than 100 citations. Choo K. K.R. is the most influential author with 185 publications. 2023 IEEE. -
Exploration and Analysis of Seizure Spikes Through Spectral Domain Transformation
Seizure detection is the most crucial area of investigation when it comes to understanding brain disorders. This proposed research study embarked on an automated model for epileptic seizure diagnosis by means of different kinds of Spectral transformation using EEG inputs from seizure sufferers and healthy subjects. This automated model accommodates non-invasive brain electrical activity monitoring. This method aims to facilitate the analysis and identification of epileptic seizure states since, monitoring and diagnosing such brain electrical activity is a complex task due to its numerous divisions and underlying features. The primary objective of this research study is to distinguish between EEG-based seizures and healthy individuals. To achieve this goal, a combination of spectral transformation and EEG analysis techniques is utilized. These techniques include examining the frequency spectrum, magnitude spectrum, correlation, and T-Distributed Stochastic Neighboring Embedding (T-SNE) analysis. This analysis yields valuable insights from EEG data, refining the input data and making it more suitable for prediction and identification. The models performance is evaluated using two distinct datasets: real-time EEG data from individuals experiencing epileptic seizures and EEG data from healthy subjects. These datasets are sourced from the Bangalore EEG Epilepsy Dataset (BEED), India and the BONN epilepsy dataset from the UCI repository. In a comparative study of spectral transformation methods, including Complex Fast Fourier Transform (CFFT) and Real-Valued Fast Fourier Transform (RFFT), it is discovered that reducing the data dimension by using feature extraction is not the optimal approach. This simplification leads to the loss of valuable information. Therefore, preserving the full spectrum of EEG characteristics is crucial for gaining valuable insights into brain neuronal functions, ultimately enabling more accurate seizure prediction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Impact ofFeature Selection Techniques forEEG-Based Seizure Classification
A neurological condition called epilepsy can result in a variety of seizures. Seizures differ from person to person. It is frequently diagnosed with fMRI, magnetic resonance imaging and electroencephalography (EEG). Visually evaluating the EEG activity requires a lot of time and effort, which is the usual way of analysis. As a result, an automated diagnosis approach based on machine learning was created. To effectively categorize epileptic seizure episodes using binary classification from brain-based EEG recordings, this study develops feature selection techniques using a machine learning (ML)-based random forest classification model. Ten (10) feature selection algorithms were utilized in this proposed work. The suggested method reduces the number of features by selecting only the relevant features needed to classify seizures. So to evaluate the effectiveness of the proposed model, random forest classifier is utilized. The Bonn Epilepsy dataset derived from UCI repository of Bonn University, Germany, the CHB-MIT dataset collected from the Childrens Hospital Boston and a real-time EEG dataset collected from EEG clinic Bangalore is accustomed to the proposed approach in order to determine the best feature selection method. In this case, the relief feature selection approach outperforms others, achieving the most remarkable accuracy of 90% for UCI data and 100% for both the CHB-MIT and real-time EEG datasets with a fast computing rate. According to the results, the reduction in the number of feature characteristics significantly impacts the classifiers performance metrics, which helps to effectively categorize epileptic seizures from the brain-based EEG signals into binary classification. 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. -
Privacy-preserving federated learning in healthcare: Fundamentals, state of the art and prospective research directions
Recent collaborations in medical diagnostic systems are based on data private collaborative learning using Federated Learning (FL). In this approach, multiple organizations train a machine-learning model at the same time eventually leading to global model generation. This paper reviews the fundamentals of FL and its evolution path in Healthcare. The objective of this review is to scope a wide variety of healthcare applications in FL. Exactly what research direction is moving in interesting for research communities to guide their future course. This review uniquely focuses on examining numerous FL-based healthcare implementations, detailing their core methodologies and performance metrics, which, to our knowledge, have not been previously available. Privacy-preserving collaborative distributed learning through federated learning in healthcare enhances research collaborations, thereby resulting in better-performing models. This comprehensive review will act as a valuable reference for researchers exploring new FL applications in the healthcare domain. 2024 IEEE. -
Residual-Based Statistical Process Control Charts in the Presence of Multicollinearity: an EWMA Framework with (RK) Estimator
Reliability monitoring of financial health requires strong control mechanisms, and the residual chart is an invaluable instrument to perform it. One of the key problems statisticians face while modeling is the problem of multicollinearity which arises when there is a strong correlation between independent variables leading to imprecise coefficient estimates and poor outcomes. To solve this problem and to make sure that the control chart works even with correlated data, we integrated a Weighted Moving Average Exponential smoothing chart within the modeling technique. The theoretical approach assures long-term variability and consistency of the residual control chart. These control charts are used to understand the process and the performances in various sectors. The charts can be used as analytical instruments to help recognize patterns, variations, or anomalies in economic indicators specifically in budget deficit data and facilitate rapid identification of any changes or inconsistencies in the fiscal deficit by policymakers. Further advances in statistical process control are rendered feasible by this study, which deepens the understanding and awareness of the potential uses and implications of the Weighted Moving Average Exponential smoothing chart for fiscal deficit data in the Economic realm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Tracking Sigmoid Regression with Multicollinearity in Phase I: An Approach Incorporating Control Charts
Regression and quality control are two crucial techniques that data analysis employs in improving the decision-making process. We use the sigmoid function to model the connection between independent factors and the dependent variable in sigmoid regression. When there is a significant correlation among the independent variables in a regression model, multicollinearity a statistical phenomenon exists. Multicollinearity presents problems with higher uncertainty when estimating individual coefficients possibly making it harder to identify each variable's distinct contribution to the model. By suggesting a control chart specifically designed for the sigmoid regression model, this research presents a strategy to address the impact of influential observations using regression control charts, by making use of principal component regression class estimators. Principal component regression merges from the principal component analysis and linear regression methodologies, aiming to alleviate multicollinearity issues and enhances the stability of regression models. The performance of the model is evaluated using Pearson's residuals, Deviance residuals, and residuals. This strategy is proven to be useful in real world situations demonstrated through an application in the field of sleep wellness disorder. In conclusion, this study introduces a unique control chart to manage multicollinearity in sigmoid regression, providing a new perspective on the topic to spot differences in the underlying process by highlighting trends in the residuals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Machine Learning Approaches for Suicidal Ideation Detection on Social Media
Social media suicidal ideation has become a serious public health issue that requires creative solutions for early diagnosis and management. An extensive investigation of machine-learning techniques for the automated detection of suicidal thoughts in internet postings is presented in this research. We start off by talking about the concerning increase in information on social media about mental health issues and the pressing need to create efficient monitoring mechanisms. The research explores the several methods used to identify the subtleties of suicidal thought conveyed in text, photographs, and audio-visual information. These methods include sentiment analysis, natural language processing, and deep learning models. We look at the problems with unbalanced data, privacy issues, and the moral ramifications of keeping an eye on user-generated material. We also go over the research's practical ramifications, such as the creation of instruments for real-time monitoring and crisis response techniques. Through comprehensive experiments and benchmarking, we demonstrate the potential of machine learning in providing timely support for those in need, thereby reducing the impact of suicidal ideation on society. 2023 IEEE. -
An Early-Stage Diabetes Symptoms Detection Prototype using Ensemble Learning
Diabetes is one of the most increasing health issues that the whole world is facing. Recent research has shown that diabetes is spreading quickly in India. Having more than 77 million sufferers, India is actually regarded as the diabetes capital of the world. The lifestyle and eating patterns of people who move from rural to urban settings alter, which raises the prevalence of diabetes. Diabetes has been linked to consequences like vision loss, renal failure, nerve damage, cardiovascular disease, foot ulcers, and digestive issues. Diabetes can harm the blood arteries and neurons in a variety of organs. FPG (Flaccid Plasma Glucose) is a popular test that is done to find out whether a person is a diabetic patient or not. However, not all people consistently take medication and neither monitor their blood sugar levels on a regular basis. Early detection of this disease is also an important thing that people usually don't do. Technology these days has emerged a lot in the healthcare zone. Many prototypes have already been made for the detection of diabetes. The prototype discussed in this paper is an ensemble learning approach for the detection of diabetes in a very early stage. Ensemble learning which includes the use of multiple model prediction has been used to make the outcome stronger and more trustworthy. The overall accuracy achieved by the model is 96.54%. XGBoost also records the minimal execution time of 2.77 seconds only. 2023 IEEE. -
Optimizing Portfolio for Highly Funded Industries Within Budget Constraints for the Period of 20232024
This research paper aims to analyze and optimize portfolios for the top funded industries based on the budget23. The study uses a data-driven approach to identify the best investment opportunities within these industries. The methodology involves collecting financial data, conducting market analysis, and using optimization techniques to create an optimal portfolio. The results of the study show that the top funded industries have a high potential for growth, and the optimized portfolios can maximize returns while minimizing risk. The findings can provide valuable insights for investors and fund managers who are seeking to make informed investment decisions in these industries. The study also highlights the importance of considering the budget constraints while optimizing portfolios. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Quantum Information Processing for Legal Applications through Bloch Sphere of Law
The objective of the research work is to propose a quantum information processing model (QIP) for legal applications including litigation and investigation phases. The quantum information processing and quantum computing concepts can be visualized within a Bloch Sphere of Law (BSL) as legal Bloch vectors (LBV) as quantum computing entities. This quantum approach is needed since the complexity of legalities and the legal objects involved in the final judgement are to be reversible with a lot of uncertainties. The reasoning and prosecution through various trials and investigations are to be considered as mathematical matrix or unitary operations in this muti dimensional legal space. The mapping of legal information into technical and then vectorial representations are deployed through a glossary of legal terms in this quantum paradigm. As a forerunning study and application in the quantum paradigm, mathematical and computational models have been proposed in the work with a case study of a recent civil case. 2022 IEEE. -
A Reliable Method of Predicting Water Quality Using Supervised Machine Learning Model
Water contributes to around 70% of the world's exterior and is perhaps the primary source essential to supporting life. The rapid growth of urban and industrial geographies has prompted a disintegration of the quality of water at a concerning pace, bringing about nerve-racking sicknesses. Water quality has been expectedly assessed through costly and tedious lab and measurable examinations, which render the contemporary thought of continuous observing disputable. The disturbing results of helpless water quality require an elective strategy, which is speedier and more economical. With this inspiration, this exploration investigates a progression of administered AI calculations to appraise the Water Quality Index (WQI), which acts as a unique attribute to express the generic nature of water. The proposed system utilizes multiple info boundaries, specifically, temperature, pH, dissolved O2 concentration, and all-out broken down molecules. Of the multitude of utilized regression calculations and slope boosting, the water quality index can be expected most productively, with an MSE of 0.27. The propositioned study accomplishes acceptable precision by utilizing a minimum number of features to improve the chances of it getting implemented progressively in water quality recognition frameworks. 2022 IEEE. -
IOT-Enabled Supply Chain Management for Increased Efficiency
Deep learning methods have demonstrated potential Supply chain is a set or group of people as well as companies responsible for producing goods and getting it to their consumers. The producers of the raw materials are the first links in the chain, and the vehicle that delivers the finished goods to the client is the last. Lower costs and higher productivity are the benefits of an efficient supply network, which emphasizes the importance of management of supply chain. The internet of things, or IoT, is a network of mechanical and digital technology that can communicate with one another and send data without the need for human contact. Smart items were included into the conventional supply chain system to increase intelligence, automation potential, and intelligent decision-making. The existing supply chain system is offering previously unforeseen chances to increase efficiency and reduce cost. The aim and motive of our research is to analyze the methods of supply chain management where the main elements of IoT in management of supply chain will be highlighted. 2024 IEEE. -
An Analysis of Word Sense Disambiguation (WSD)
Word sense disambiguation (WSD) is the method of using computer algorithms to determine the sense of arguments in the background. As a result of its difficult nature, WSD has measured an AI-complete problem, i.e., a problem whose key is as minimum as difficult as those posed by artificial intelligence. This article describes the task and introduces motives to resolve the ambiguity of words discussed throughout the text. This article summarizes supervised, unsupervised, and knowledge-based solutions. Senseval/semeval campaigns are described in relation to the assessment of WSDs, with the aim of an unbiased assessment of schemes working on numerous disambiguation errands. Finally, future directions, requests, open difficulties, and open problems are discoursed. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Novel Steganographic Approach for Image Encryption Using Watermarking
Steganography is a technique for obfuscating secret information by enclosing it in a regular, non-secret file or communication; the information is subsequently extracted at the intended location. Steganography can be used in addition to encryption to further conceal or safeguard data. Watermarking is one such technique practiced in the area of steganography. Watermarking can be practiced via multiple algorithmic techniques like Discrete Wavelength Transform (DWT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), Discrete Fourier Transform (DFT). In this study, a combination of such approaches along with AES encrypted watermarked images has been implemented. Validation of these techniques has been achieved by evaluating the Peak Signal to Noise Ratio (PSNR). 2023 IEEE. -
Exploring the Influence of Service Learning on the Socio-Educational Commitment and Self- Efficacy of Graduate Educators in the Artificial Intelligence (AI) Domain.
This study, conducted by a distinguished university, aims to contribute significantly to the professional development of educators dedicated to creating a fair, sustainable, and socially conscious world. The research focuses on a pedagogical approach using Service Learning to foster civic and social skills in higher education students. The main goal is to examine how graduate students, actively participating in Service-Learning initiatives, develop socio-educational commitment and self-efficacy compared to traditional university volunteering. The study, involving 1562 aspiring educators, employs a quantitative correlational methodology. The hypothesis suggests that Service-Learning leads to more positive outcomes in socio-educational commitment, pedagogical self-efficacy, and crafting instructional materials. The findings, statistically significant (p < 0.01), highlight the increased development of these metrics among participants in Service-Learning programs. 2024 IEEE. -
Application of CNN and Recurrent Neural Network Method for Osteosarcoma Bone Cancer Detection
The outlook for people with metastatic osteosarcoma at an advanced stage is poor. Osteosarcoma is the most frequent form of bone cancer in children and young adults. There is an urgent need for both advances in treatment tactics and the identification of novel therapeutic targets for osteosarcoma since the disease typically develops resistance to existing treatments. Cancer stem cells, also known as tumor stem cells, have been linked to the development and spread of cancer at multiple points in the disease's progression. Cancer stem cells are linked to treatment resistance and carcinogenesis, and recent studies have demonstrated that osteosarcoma shares these properties. The proposed methodology rests on the three pillars of preprocessing, feature extraction, and model training. During preprocessing, that the proposed approach eliminated isolated highlights to help us zero in on the trustworthy region. They use the wavelet transform and the gray level co-occurrence matrix to extract features. A CNN-RNN technique is used to evaluate the models. In terms of output quality, the proposed technique is superior to both CNN and RNN. 2023 IEEE. -
Fire Resistance of Concrete with Partial Replacement of Ceramic Waste and Carbon Fiber as Additives
One of the primary hazards that causes catastrophic damage to properties and peoples lives is fire. Although ceramic garbage is deposited on the land, it is a non-biodegradable waste that pollutes the environment. This study is based on the use of industrial waste products such as ceramic sanitary waste to improve the mechanical qualities of concrete that have been exposed to elevated temperatures. An experimental investigation was carried out on cubes, cylinders, and beams to assess compressive strength, split tensile strength, and flexural strength with fractional replacement of fine aggregates with 10, 20, and 30% of ceramic waste and 0, 1, and 2% of carbon fibers as additives at normal and elevated temperature as per ASTM code recommendations and the results shown as a significant improvement. The strength of M30 grade concrete with partial replacement of fine aggregate with ceramic waste up to 30% and carbon additives up to 2% shows an improvement of compressive strength by 17.56% than conventional concrete. It is also observed that normal M30 grade concrete loses its strength by 49.6% when it is exposed to 600C and with fractional replacement of fine aggregate by ceramic waste by up to 30% and carbon additives by up to 2% shows the loss of strength is decreased up to 22.67%. It shows that it is the probable substitute solution for the secure discarding of Ceramic waste. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
The Impactful Role of ML Algo in the Field of Enactment Nostrum: An Intensive/Deep Review
Machine translation (MT) research has explored a variety of models, including statistical machine translation (SMT), rule-based machine translation (RBMT), and hybrid approaches. Hybrid MT systems aim to improve translation quality by using the strengths of different models. However, the complex set of functions associated with MT systems is still difficult to understand and optimize. This instant study propose an approach based on ML with respect to hybrid MT that addresses these issues by automatically interpreting and weighting features using ML tools. This research framework includes a classification approach to classify and compare translations from multiple black-box A system that uses ML classifiers trained on feature vectors derived from natural language processing tools. This study presents a method to train and use an SVM-based classifier to generate hybrid interpretations. The test results for English-Chinese pairs show the potential of this research approach to improve translation quality. The proposed framework is a simple and efficient way to combine different MT systems, improving translation results without manual intervention. 2024 IEEE.