Yazar "Bhushan, B." seçeneğine göre listele
Listeleniyor 1 - 7 / 7
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Automated and Optimised Machine Learning Algorithms for Healthcare Informatics(Springer Science and Business Media Deutschland GmbH, 2024) Juyal, A.; Bhushan, B.; Hameed, A.A.; Jamil, A.; Pandey, S.Healthcare is a rapidly expanding field with a substantial amount of heterogeneous data driving innumerable health-related tasks. Various healthcare service providers still rely on manual procedures, which can be time-consuming and require significant effort. To automate such manual operations, recent technological advances have emerged in the domain of Machine Learning (ML). ML falls under the subject of Artificial Intelligence (AI), and it gets combined with ‘big data’ to draw meaningful insights. The integration of ML in the healthcare sector has optimized decision-making and predictive analysis. This paper discusses the various application areas of ML in healthcare. Additionally, several ML algorithms used by other researchers in healthcare-related experiments are summarized. A brief review is provided regarding the experiments. This paper delineates the challenges associated with using ML in healthcare. Finally, the paper offers insights into future research directions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Öğe Blockchain Based Security Framework for Internet of Medical Things (IoMT) Applications(Association for Computing Machinery, 2023) Baniya, P.; Nand, P.; Bhushan, B.; Hameed, A.A.; Jamil, A.The growing demand for Internet of Medical Things (IoMT) sensors within the healthcare sector has emphasized the necessity for robust security measures. However, these devices, often constrained by limited resources, frequently require external assistance to guarantee the security of data. Blockchain technology emerges as a practical solution, offering a way to enhance system security and safeguard patients' sensitive health records against unauthorized access and data breaches. The fusion of IoMT and blockchain presents a range of benefits, such as cost reduction, improved speed, automation, and the guarantee of unmodifiable data. Nevertheless, security concerns persist, particularly in light of the ever-evolving encryption techniques. The effective implementation of IoMT hinges on the delivery of personalized, patient-centered care, which has the potential to enhance affordability, simplicity, and overall convenience, ultimately contributing to improved quality of life and extended lifespans. In this comprehensive exploration, we delve into the historical context, architecture, components, and challenges associated with IoMT. Additionally, we conduct an in-depth examination of blockchain technology, covering its essential elements and categories, as well as its seamless integration into IoMT, underscoring its critical role in reinforcing security measures within the healthcare domain. Furthermore, this paper outlines recent developments in blockchain technology within the IoMT framework, highlighting its ongoing evolution and sustained significance in the healthcare field. © 2023 ACM.Öğe Blockchain-Powered Smart E-Healthcare System: Benefits, Use Cases, and Future Research Directions(Springer International Publishing, 2023) Malik, A.; Bhushan, B.; Parihar, V.; Karim, L.; Cengiz, K.Blockchain technologies are deeply distributed and used in several dominions, including for E-healthcare. Internet of Things (IoT) strategies can arrange real-time sensual information from patients for their treatment. Composed information is aimed to combine for computation, dealing, and storing. Such centralism can be challenging, as it can be the only reason for lack of success, uncertainty, document management, interfering, and confidentiality elusion. Blockchain is able to resolve these kinds of consequent complications by giving distributed computation and proper storage for IoT data records. Consequently, the mixture of blockchain technologies in healthcare can convert into a realistic selection for the scheme of distributed Blockchain-powered smart E-healthcare systems. This paper discusses the background of blockchain technology with its features and categories. The paper explores the collaboration of blockchain with IoT for E-healthcare. Further, this paper highlights some popular consensus algorithms used in blockchain in the circumstance of E-health. Finally, this paper examines some use cases of E-healthcare that illustrate how key characteristics of the IoT and blockchain can be leveraged to maintain healthcare facilities and environments. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe Deep Learning Approaches for Cyber Threat Detection and Mitigation(Association for Computing Machinery, 2023) Juyal, A.; Bhushan, B.; Hameed, A.A.; Jamil, A.Cyberspace has inflated over the past decade, primarily driven by pervasive development and widespread usage of the internet. Prolonged cyber-attacks and security vulnerabilities have become more common as a consequence. A recent study conducted by Ponemon Institute revealed that 64% of businesses have suffered web-based attacks in 2023. Thus, cybersecurity measures have become mandatory in every sector, that leverages technology, to protect confidential information from malignant threats. The majority of the sectors use conventional security measures like anti-virus products to defend their systems against outside attacks, however, these products fail to work on unseen and polymorphic threats. With cutting-edge deep learning (DL) processes getting adopted in nearly every field, it has been widely exploited in cybersecurity to detect and classify cyber-attacks. This study discusses the application of common deep learning models in cybersecurity to detect malware, intrusion, phishes, and spam. This study provides a description of deep learning models and their mathematical backgrounds that are common in cybersecurity. This study also reviews and analyses the work of other researchers who experimented with deep learning models in cybersecurity. This study delineates the challenges faced by deep learning models in cybersecurity. Finally, concluding remarks and future research directions are summarized to foresee the potential improvement of deep learning in cybersecurity. © 2023 ACM.Öğe Implementation of Homomorphic Encryption Schemes in Fog Computing(Springer Science and Business Media Deutschland GmbH, 2024) Pandey, S.; Bhushan, B.; Hameed, A.A.; Jamil, A.; Juyal, A.In today's date, fog computing is on the rise due to its characteristics. Its ability to provide smart devices with close-by computing capabilities offers a drastic reduction in cloud traffic and more efficient data transfer. However, data security in fog computing devices is a major concern. Homomorphic Encryption (HME) Scheme is a technique that protects private data from various threats, and to improve efficiency and reduce time, it offers modification of original data after encryption without decrypting it. In this paper, we explore the various homomorphic encryption schemes that can be implemented in fog computing to protect the data and provide data security. We present a detailed architecture and various characteristics of fog computing to better visualize the use of homomorphic encryption in different areas. Further, we present the applications of homomorphic encryption in different sectors and discuss future research directions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Öğe Short Message Service Spam Detection System for Securing Mobile Text Communication Based on Machine Learning(Springer Science and Business Media Deutschland GmbH, 2024) Malik, A.; Parihar, V.; Bhushan, B.; Hameed, A.A.; Jamil, A.; Bhattacharya, P.In recent years, the popularity of Short Message Service (SMS) on mobile phones has surged due to technological advancements and the growing prevalence of content-based advertising. However, this has also led to a surge in spam SMS, which can inundate one’s handset at any time and potentially result in the theft of personal information. Researchers have explored a range of options to combat spam SMS, including content-based machine learning algorithms and stylometric approaches. While filtering spam emails has proven to be effective, detecting spam SMS presents a unique set of challenges due to the presence of idioms, abbreviations, and well-known terms and phrases that are frequently used in legitimate messages. This study aims to examine and assess different classification techniques by utilizing datasets gathered from previous research studies. The primary emphasis is on comparing conventional Machine Learning (ML) methods. This study specifically investigates the efficacy of classification algorithms, including Logistic Regression (LR), Naïve Bayes (NB), and Random Forest (RF), in accurately identifying spam SMS messages. The results of our research demonstrate that the RF classifier outperforms other traditional ML techniques in the detection of spam SMS messages. This paper thoroughly examines diverse classification techniques employed in spam detection. The knowledge derived from this study has the potential to advance the development of more effective systems for detecting spam SMS messages. Ultimately, this would enhance the security and privacy of mobile phone users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Öğe A Survey on Image-Based Cardiac Diagnosis Prediction Using Machine Learning and Deep Learning Techniques(Springer Science and Business Media Deutschland GmbH, 2024) Nag, A.; Das, B.; Sil, R.; Hameed, A.A.; Bhushan, B.; Jamil, A.Cardiac imaging is crucial in the diagnosis of cardiovascular disease. Cardiovascular disease is the umbrella term for the majority of heart ailments. The majority of the causes of mortality are associated with cardiovascular illness. The authors provide a technique for the diagnosis of cardiac disease. The main aim of this study is to determine the most effective technique for predicting cardiovascular disease, specifically focusing on the use of signs of heart disease and Electrocardiogram images. This will be achieved by leveraging the latest advancements in Deep Learning and Machine Learning methods. The authors conduct a comprehensive examination of various Machine Learning and Deep Learning Techniques. These techniques were evaluated in the context of predicting cardiovascular disease evaluating Image. The analysis shows that the Convolutional Neural Network methods are much more effective than the alternatives. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
