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Comparative Analysis of Prediction Algorithms for Heart Diseases
Cardiovascular diseases (CVDs) are the leading source of demises universally: More individuals perish yearly from heart disease than due to any other reason. An estimated 17.9 million humans died from CVDs in 2016, constituting 31% of all global deaths. [1] Such high rates of death due to heart diseases have to cease. This idea can be accelerated by the prediction of risk of CVDs. If a person can be medicated much earlier, before they have any symptoms that can be far more beneficial in averting sickness. The paper strives to communicate this issue of heart diseases employing various prediction models and optimizing them for better outcomes. The accuracy of each algorithm guides to a relative enquiry of these prediction models, forming a solid base for further research, finer prognosis and detection of diabetes. 2021, Springer Nature Singapore Pte Ltd. -
Tool wear and tool life estimation based on linear regression learning
Tools have remained an integral part of the society without which stimulation of certain aspects of human evolution would not have been possible. In recent times the modern tools are used in the manufacturing of high precision components. We know that the accuracy and surface finish of these components can be achieved only by the usage of accurate tools. Sharp edged tools may loosen their sharpness due to repeated usage and machining parameters. Hence to address this issue we propose a system to monitor tool wear by using the captured image of cutting tool tip. We used vision system since it is the primitive method of predicting tool wear and two main machining parameters feed rate and depth of cut. The image of flank wear cutting edge at tool tip is captured by examining under profile projector. The system uses linear regression model to calculate tool wear which is mapped onto continuous 2-D coordinates with feed rate and depth of cut as axis from a captured digital image. Thus the proposed intelligent system uses profile projector and digital image processing methods to estimate tool wear continuously and predictively like humans rather than using strict rules. By estimating tool wear continuously the machine can better perform and machine components accurately by using the resultant values of feed rate and depth of cut as a threshold which are arrived as a result. 2015 IEEE. -
Research Trends on Workplace Criminal Behaviour: A Bibliometric Analysis
This study presents a comprehensive bibliometric analysis of the research landscape surrounding Workplace Criminal Behaviour (WCB), examining its evolution over time. By focusing on thematic areas, research trends, and patterns of scholarly output, the study offers a systematic overview of scientific contributions in this field. A total of 767 peer-reviewed publications were retrieved from the scientific database and analyzed using bibliometric techniques. The findings indicate that scholarly interest in WCB began to gain momentum in 1989, marking a significant turning point in the field. The analysis also highlights the most prominent institutions, journals, and influential scholars contributing to the field. Keyword mapping revealed closely related areas of inquiry, including white-collar crime, workplace theft, and corporate crime, reflecting the multidimensional nature of WCB research. This study offers a valuable resource for emerging scholars, outlining key areas of focus, frequently used methodologies, high-impact publication outlets, and potential collaborators. By mapping the intellectual structure of the field, the findings contribute to shaping future research directions and fostering more targeted and impactful scholarly efforts in workplace criminal behaviour. (2026), (South-West University "Neofit Rilski"). All rights reserved. -
A novel security framework for healthcare data through IOT sensors
The Internet of Things (IoT) has played a crucial role in the distribution of health records and poses security issues to the patient-specific health information needed for remote hospital attention. The majority of publicly accessible security mechanisms for health information do not concentrate on the flow of information from IoT different sensors installed upon the person's blood through networking devices to primary health care centers. In this paper, we investigated the potential risks of unprotected transmission data, particularly among IoT sensor systems and network gateways. The study encourages the transmission of health insurance data to hospitals remotely. The proposed health care information model would encode immediately so that the sensing element before even being transferred to cryptographic techniques. To use a laboratory configuration with two-stage cryptography at the IoT sensor and two-stage decoding at the physician's surgery receptor, the prototype system was validated. The test results for a complete safety system for IoT - based on the transmission of healthcare data seem good. The study opens up new avenues for information security on IoT devices. 2022 The Authors -
Numerical modeling of novel cage-like cross-linked membranes for enhanced proton conductivity in a high temperature-polymer electrolyte membrane fuel cell
Phosphoric acid (PA)-doped polybenzimidazole (PBI) membranes have encountered several problems associated with high cost, chemical instability, poor solubility in organic solvents, and higher doping level which results in poor mechanical properties and faster degradation of the membrane. Alternative membranes with high proton conductivity and mechanical strength for high-temperature applications are of great interest, one such membrane being cPBI-IL X. The cage-like cross-linked structure of these membranes shows a dual proton transport path due to which proton conductivity is elevated. The ionic liquid content of these membranes improves the PA absorbing capability and shortens the proton transfer path. These membranes exhibit the highest proton conductivity of 13.3 S/m and better durability compared to existing PBI Membranes. A mathematical model is developed and validated versus published experimental results to account for the proton conductivity of these membranes. The developed model is further investigated for a detailed understanding of polarization phenomena and species distribution. 2023 Wiley Periodicals LLC. -
Data journalists perception and practice of transparency and interactivity in Indian newsrooms
Data journalism research recorded exponential growth during the last decade. However, the extant literature lacks comparative perspectives from the Asian region as it has been focused on select geographies (mainly Europe and the US). In this backdrop, the present study examined data journalism practices in the Indian media industry by conducting intensive interviews with 11 data journalists to investigate their perception of transparency and interactivity which are two of the core aspects of data journalism practice. Further, a content analysis of data stories published by two Indian news organizations for two years was conducted to assess the status of transparency and interactivity options in these stories. The findings showed that Indian data journalists acknowledge the importance of transparency and interactivity, but exhibit a cautious approach in using them. There is general apathy in practicing transparency among journalists in legacy organizations, drawing a stark contrast with their counterparts in digitally-native organizations. 2022 Asian Media Information and Communication Centre. -
Indexing of exoplanets in search for potential habitability: application to Mars-like worlds
Study of exoplanets is one of the main goals of present research in planetary sciences and astrobiology. Analysis of huge planetary data from space missions such as CoRoT and Kepler is directed ultimately at finding a planet similar to Earththe Earths twin, and answering the question of potential exo-habitability. The Earth Similarity Index (ESI) is a first step in this quest, ranging from1 (Earth) to0 (totally dissimilar to Earth). It was defined for the four physical parameters of a planet: radius, density, escape velocity and surface temperature. The ESI is further sub-divided into interior ESI (geometrical mean of radius and density) and surface ESI (geometrical mean of escape velocity and surface temperature). The challenge here is to determine which exoplanet parameter(s) is important in finding this similarity; how exactly the individual parameters entering the interior ESI and surface ESI are contributing to the global ESI. Since the surface temperature entering surface ESI is a non-observable quantity, it is difficult to determine its value. Using the known data for the Solar System objects, we established the calibration relation between surface and equilibrium temperatures to devise an effective way to estimate the value of the surface temperature of exoplanets. ESI is a first step in determining potential exo-habitability that may not be very similar to a terrestrial life. A new approach, called Mars Similarity Index (MSI), is introduced to identify planets that may be habitable to the extreme forms of life. MSI is defined in the range between 1 (present Mars) and 0 (dissimilar to present Mars) and uses the same physical parameters as ESI. We are interested in Mars-like planets to search for planets that may host the extreme life forms, such as the ones living in extreme environments on Earth; for example, methane on Mars may be a product of the methane-specific extremophile life form metabolism. 2017, Springer Science+Business Media B.V. -
Optical Spectroscopy of Classical Be Stars in Old Open Clusters
We performed the optical spectroscopy of 16 classical Be stars in 11 open clusters older than 100 Myr. Ours is the first spectroscopic study of classical Be stars in open clusters older than 100 Myr. We found that the H? emission strength of most of the stars is less than 40 in agreement with previous studies. Our analysis further suggests that one of the stars, [KW97] 35-12, might be a weak H? emitter in nature, showing H? equivalent width of ?0.5 Interestingly, we also found that the newly detected classical Be star LS III +47 37b might be a component of the possible visual binary system LS III +47 37, where the other companion is also a classical Be star. Hence, the present study indicates the possible detection of a binary Be system. Moreover, it is observed that all 16 stars exhibit a lesser number of emission lines compared to classical Be stars younger than 100 Myr. Furthermore, the spectral type distribution analysis of B-type and classical Be stars for the selected clusters points out that the existence of CBe stars can depend on the spectral type distribution of B-type stars present in these clusters. 2023. National Astronomical Observatories, CAS and IOP Publishing Ltd. -
Do callous-unemotional traits and resilience work together or in conflict? Thriving after trauma in children
Cumulative trauma, which is devastating can lead to severe psychological outcomes. Exposure to various kinds of trauma, especially in childhood is even more harmful than the sheer volume of experience. Children experiencing cumulative trauma are at greater risk of developing callous unemotional traits. This study examined the mediating role of callous unemotional traits and the moderating role of resilience, in the association between cumulative trauma and behavioral problems among children. A survey of 314 vulnerable children, aged 13 to 17 years (girls = 167; boys = 147) was conducted at seven different institutes in Assam, India. The results indicated that the children experienced 16 different types of trauma during their developmental phases. The mediating model indicated a strong facilitating effect of callous-unemotional (CU) traits in the relationship between cumulative trauma and behavioral problems: externalizing (? =.01; p <.05); internalizing problems (? =.03; p <.01); personal adjustment (? =.03; p <.01); and school problems (? =.04; p <.01). Additionally, resilience did not moderate the association between trauma and behavioral problems. This study strengthens the evidence associating childhood cumulative trauma with an increased risk for the development of CU traits, supporting the notion that these traits could potentially reduce resilience. 2026 Informa UK Limited, trading as Taylor & Francis Group. -
Performance analysis of alternating minimization based low complexity detection for MIMO communication system
Several antennas are used for sending and receiving in large MIMO (Multiple-Input-Multiple-Output) devices and assist in enhanced performances of wireless communication systems. One important component of Large MIMO systems is that MIMO detectors are placed at receiver ends, whose functions are to regain symbols broadcasts from multiple antennas. In this paper, novelAMLCD (Alternating Minimizationbased Low Complexity Detections) method is proposed in which AMs (Alternating Minimizations) are applied in initial stages to detect signals. Soft value generation is used for the second stage to estimate the signals. Finally, the more optimal estimated signal value will be chosen by applying the MPSOs (Modified Particle Swarm Optimizations). The system's functions are evaluated using CPMs (Continuous Phase Modulations) and channels AWGNs (Additive White Gaussian Noises). According to the results obtained, the suggested AMLCD method with modulations of CPMs outperform known methods using QAMs (Quadrature Amplitude Modulations) under multiple antennas in terms of BERs (Bit Error Rates). The AMLCD method also reduces the time complexity and computational complexity compared to the existing methods. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
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Analysing the Influence of Activation Functions in CNN models for Effective Malware Classification
With the advancement of information technology, malware has become a persistent cyber security concern that targets computer systems, smart devices, and wide networks. Due to flaws in performance accuracy, analysis type, and malware classification methodologies that miss unsuspected malware attacks, malware classification has thus always been a significant concern and a challenging subject. Using the Malimg dataset, which has 9349 samples from 25 different families, this study classifies malware using a deep learning algorithm called a convolution neural network and evaluating the accuracy using a number of activation functions in this study. The proposed CNN model for malware classification achieves a high accuracy rate without the need for complex feature engineering. The model achieved the highest accuracy of 96.93% when using the Rectified Linear Unit (ReLU) activation functions whereas Leaky Relu gives accuracy of 96.76%, Pre relu gives 96.36%, ELU gives 95.72% and tanh gives accuracy of 95.58%. 2024 IEEE. -
An Encryption and Decryption of Block Ciphers Using Multipartite Graphs
Cryptography is the technique that secures the process of sending and receiving messages. It involves designing the text to be communicated in a complex form, so that possible intruders might find it difficult to access the information conveyed. This approach comprises a lock that encodes or encrypts the information and a key that decodes or decrypts the ciphered text. This is essential to secure the flow of confidential information. Graph theory plays a crucial role in cryptography. A graph-based technique revolves around the maintenance of security in information flow by factoring the text into graphical models of all the cipher parameters and forming a reverse mechanism to decipher the ciphered model. In this study, we introduce a novel cryptographic graphbased model, aiming to enhance the effectiveness of the existing approaches, thereby advancing secure communication in this digital era. This approach specifically deals with block ciphers structured as a multipartite graph, ensuring a secure encryption, with symmetric key cryptography. 2026 Scrivener Publishing LLC. -
Investigating the Use of Natural Language Processing in Electronic Medical Record
Natural language processing (NLP) implemented in digital scientific records (EMRs) can substantially enhance the nice and efficiency of affected person care. The purpose of NLP implemented in EMRs is to extract applicable facts from affected persons' notes written in a human language together with English. This information can then be stored in a suitable structured form for further evaluation and records mining. NLP has been carried out in the clinical field for the reason that Fifties as a green approach for retrieving textual content-based data and reading interactions among affected persons and healthcare professionals. With the arrival of electronic facts, NLP has come to be extra extensively applied for the diffusion of purposes, inclusive of automatic coding, scientific choice aid, and medical doctor order access. This summary makes a of exploring the usage of NLP in EMRs. The scope of this research consists of an evaluate of present NLP technologies and their software in EMRs. It additionally outlines a number of the present-day demanding situations inside the use of NLP for clinical information and shows capability answers. Finally, the potential applications of NLP-driven EMRs are discussed, inclusive of making use of in-health practitioner order entry, scientific choice assistance, and population health control. 2024 IEEE. -
Implementation Strategies for Green Computing
In this chapter, we look at how renewable energy sources can be integrated into the planning, design, and construction of long-term sustainability in green buildings. When it comes to establishing a framework for environmentally friendly building, there are two primary schools of thought. One is related to the use of conventional architecture and low-energy construction material. The fundamental focus of green building design is on using renewable energy solutions for the purpose of managing energy protection. When referring to a green building, either sustainable construction or green construction may be used instead. To guarantee a structure will last for its intended purpose and the environment will not be harmed in the process, sustainable construction practices should be included from the start. Additionally, the economics of renewable energy are presented in this chapter with eco-friendly construction practices that make use of renewable energy sources. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa. -
Amalgamation of IoT, Blockchain, Artificial Intelligence for Metaverse
The metaverse is a set of technologies that uses a computer to create a virtual world of reality and human connections. Some of the most significant enablers of this evolution are the Internet of Things (IoT), blockchain, and artificial intelligence (AI). These technologies not only provide a better experience but also introduce a new way of how security, efficiency, and inter-activeness should be done within the Metaverse. This chapter discusses the intersection between IoT, blockchain, and AI and its relevance in the framework of Metaverse. It discusses how IoT devices generate environments, the blockchain maintains high levels of security and provides digital ownership, and AI facilitates interactions. The rise of these technologies guarantees that the use of the virtual worlds will be consistent and also enhance the user experience, yet the blend of these technologies brings a number of difficulties like interoperability problems, data privacy problems, and also the concern of combining such a lot of various systems. The Metaverse has been explored in these challenges to achieve its full potential. The objective of this chapter is to paint a picture of how IoT, blockchain, and AI should be utilized to improve the Metaverse. This chapter presents an analysis of technical and ethical issues, offers potential solutions to the current problems, and outlines the possible directions of further development. Our study thus points out that the application of these technologies together offers an enormous opportunity to propel the development of the Metaverse in its quest to deliver virtual spaces that are secure, intelligent, and interactive. The final part of the chapter outlines the long-term effect on the society as well as the future prospects for development and the potential ethical challenges in this popping field of study. 2025 Scrivener Publishing LLC. -
Food calorie estimation using convolutional neural network
The modern world healthy body depends on the number of calories consumed, hence monitoring calorie intake is necessary to maintain good health. At the point when your Body Mass Index is somewhere in between from 25 to 29. It implies that you are conveying overabundance weight. Assuming your BMI is more than 30, it implies you have obesity. To get in shape or keep up the solid weight individuals needs to monitor the calorie they take. The existing system calorie estimation is to be happened manually. The proposed model is to provide unique solution for measuring calorie by using deep learning algorithm. The food calorie calculation is very important in medical field. Because this food calorie is provide good health condition. This measurement is taken from food image in different objects that is fruits and vegetables. This measurement is taken with the help of neural network. The tensor flow is one of the best methods to classify the machine learning method. This method is implementing to calculate the food calorie with the help of Convolutional Neural Network. The input of this calculated model is taken an image of food. The food calorie value is calculated the proposed CNN model with the help of food object detection. The primary parameter of the result is taken by volume error estimation and secondary parameter is calorie error estimation. The volume error estimation is gradually reduced by 20%. That indicates the proposed CNN model is providing higher accuracy level compare to existing model. 2021 IEEE. -
The world of communication & computing platform in research perspective: Opportunities and challenges
Computing paradigms are introduced for solving complex problems by analyzing, designing and implementing by complex systems. Computing can be defined as the effective use of computer or computer technology to solve tasks that are goal oriented. Computing is used in development of producing scientific studies, building intelligent systems, channeling different media for communication. Over the last few years, internet became so popular which lead to the increase in computer processing capacity, data storage and communication with one another. Computing has evolved from one technology to another in its field and formed a robust framework over the years. In this paper a survey on different computing paradigms like evergreen computing is cloud computing, to deal with basic scheduling is grid computing, for multi task handing is parallel computing, to handle smart phone data's that is mobile computing, cluster computing, and distributed computing is carried out. These technologies improved the way computing functions and made it easier to the computer world. The applications and research issues of the most of the computing paradigms are discussed in this article. The recent research issues in computing platform are scheduling and security. The scheduling is dealing with data processing from one computing platform to other computing device. Security is one of the important research issues. 2021 IEEE. -
Artificial Intelligence Personalization: Opportunities, Risks, and the Need for Ethical Data Practices
The benefits of AI personalization are numerous; however, the question still remains, what are the side effects of this feature? Will it help make shopping and enjoying content more enjoyable and efficient or will it destroy the trust of the users by creating privacy concerns. These are the issues this article has attempted to discuss and investigate. Artificial Intelligence (AI) personalization is a very helpful and useful feature, but there must remain a proper balance between personalization and data collection in order to ensure client comfort. A transparent and honest collection of data is to be expected for almost all the companies and this data is to be used responsibly. Activities like profiling must be regulated and controlled and should not be left unregulated. Despite laws being updated to be more considerate towards the privacy of users, the development of better enforcement is imperative. Regardless of the presence of appropriate laws, it is important that each individual practice the respective ethics while present in the digital universe. This article is discussed about AI personalization issues and its research challenges. 2025 IEEE.