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    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.
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    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.