Fursan Thabit

The Cochin Backwater region in Southern India is one of the most dynamic estuaries, strongly influenced by seasonal river runoff and seawater intrusion. This study explores the relationship between monsoonal rains, salinity, and stable isotopic composition (δ¹⁸O and δ¹³C) to estimate the contribution of freshwater fluxes at different seasonal intervals for the Cochin Backwater (CBW) estuary. Seasonal variations in oxygen isotopes and salinity revealed distinct trends indicative of freshwater–seawater mixing dynamics. The comparison of Local and Global Meteoric Water Lines highlighted the occurrence of enriched isotope values during the Premonsoon season, showing significant evaporation effects. Carbon (C) isotopic analysis in dissolved inorganic matter (δ¹³C_DIC) at 17 stations during the Premonsoon season revealed spatially distinct carbon dynamics zones, influenced by various sources. These characteristic zones were categorized as Zone 1, dominated by seawater, exhibiting heavier δ¹³C_DIC values; Zone 2, showing significant contributions of lighter terrestrial δ¹³C; and Zone 3, reflecting inputs from regional and local paddy fields with a distinct C₃ isotopic signature (−25‰), modified by estuarine productivity. In addition, different advanced machine learning techniques were tested to improve analysis and prediction of seasonal variations in isotopic composition and salinity. Although the data were sufficiently robust for demonstrating the feasibility and advantages of ML in isotopic hydrology, further expansion of the dataset would be essential for improving the accuracy of models, especially for δ¹³C. The combination of these advanced machine learning models not only improved the predictive accuracy of seasonal freshwater fluxes but also provided a robust framework for understanding the estuarine ecosystem and could pave the way for better management and conservation strategies of the CBW estuarine system.

Cochin backwater region in Southern India is one of the most dynamic estuaries, strongly influenced by seasonal river runoff and seawater intrusion. This study explores the rela-tionship between monsoonal rains, salinity, and stable isotopic composition (δ¹⁸O and δ¹³C) to estimate the contribution of freshwater fluxes at different seasonal intervals for the Cochin Backwater (CBW) estuary. Seasonal variations in oxygen isotopes and salinity re-vealed distinct trends indicative of freshwater-seawater mixing dynamics. The compari-son of Local and Global Meteoric Water Lines highlighted enriched isotope values during the pre-monsoon season, showing significant evaporation effects. Carbon (C) isotopic analysis in dissolved inorganic matter (δ¹³CDIC) at 17 stations during the pre-monsoon season revealed spatially distinct carbon dynamics zones, influenced by various sources. These characteristic zones were Zone 1, dominated by seawater, exhibited heavier δ¹³CDIC values; Zone 2 showing significant contributions of lighter terrestrial δ¹³C; while Zone 3 reflected inputs from regional and local paddy fields with a distinct C3 isotopic signature (-25‰), modified by estuarine productivity. In addition, different advanced machine learning techniques were tested to improve analysis and prediction of seasonal variations in isotopic composition and salinity. The combination of these advanced machine learn-ing models not only improved the predictive accuracy of seasonal freshwater fluxes but also provided a robust framework for understanding the estuarine ecosystem and would pave way for better management and conservation strategies.

Detecting tiny objects in aerial imagery presents a major challenge regarding their limited resolution and size. Existing research predominantly focuses on evaluating average precision (AP) across various detection methods, often neglecting computational efficiency. Furthermore, state-of-the-art techniques can be complex and difficult to understand. This paper introduces a comprehensive benchmarking analysis specifically tailored for enhancing small object detection within the DOTA dataset, focusing on one-stage detection methods. We propose a novel data-processing approach to enhance the overall AP for all classes in the DOTA-v1.5 dataset using the YOLOv8 framework. Our approach utilizes the YOLOv8’s darknet architecture, a proven effective backbone for object detection tasks. To optimize performance, we introduce innovative pre-processing techniques, including data formatting, noise handling, and normalization, in order to improve the representation of small objects and improve their detectability. Extensive experiments on the DOTA-v1.5 dataset demonstrate the superiority of our proposed approach in terms of overall class mean average precision (mAP), achieving 66.7%. Additionally, our method establishes a new benchmark regarding computational efficiency and speed. This advancement not only enhances the performance of small object detection but also sets a foundation for future research and applications in aerial imagery analysis, paving the way for more efficient and effective detection techniques.

The Internet of Things (IoT) is a rapidly expanding network of interconnected things that use embedded sensors to gather and share data in real‐time. IoT technologies have given rise to many networking applications in our everyday life such as smart homes, smart cities, smart transport, and so forth. Smart healthcare is one such application that has been revolutionized by the IoT, introducing a new branch of IoT known as the Internet of Medical Things (IoMT). IoMT encompasses an entire ecosystem consisting of smart wearable, implantable sensing equipment's or devices, transmitters that are critical for monitoring the patients remotely and continuing the real‐time and has opened the door to new innovative smart healthcare approaches while improving patient care outcomes. IoMT wearable and embedded sensing devices are commonly utilized in smart healthcare to capture medical data and transmit the medical data in a communication network stored in the cloud. The large volume of data generated and transmitted by these IoMT devices is rising at an exponential rate, resulting in an increase in security and privacy vulnerabilities of healthcare data. To ensure the Confidentiality and integrity of the IoMT devices and the sensitive medical data, there should be proper security and privacy measures such as access control, passwords, multifactor authentication, and encryption of data generated, transmitted, or processed in the IoMT framework. In this paper, we identified the internet of things and its applications in smart healthcare systems. Additionally, the paper focuses on the architecture of IoMT, and several challenges, including the IoMT security and privacy requirements, and attack taxonomy. Furthermore, the paper thoroughly investigates both cryptographic and non‐cryptographic based security and privacy‐enhancing techniques for IoMT or healthcare systems with particular emphasis on advancements in key areas such as Homomorphic Encryption, Differential Privacy, and Federated Learning.

In today's fast‐paced society, the Internet of Things (IoT) is revolutionizing businesses by bridging the gap between the digital and physical realms. This transformative technology enables rapid changes in the way people conduct business. Its fundamental aim is to connect everything in our world—objects, individuals, locations, and processes—under a unified infrastructure, providing valuable information and control over the state of our environment. The IoT represents a novel approach to developing diverse and distributed systems, which have now evolved into a pervasive computing cloud infrastructure. However, due to limitations in computing power and storage resources dedicated to handling the vast amounts of IoT data, a cloud‐based architecture is often employed in the IoT realm. Unfortunately, this shift has introduced a range of complex security and trust concerns within the cloud‐based IoT environment. As the demand for widespread deployment of cloud‐based IoT continues to soar, significant security‐related threats have emerged, prompting numerous researchers to develop cryptographic algorithms aimed at enhancing the security of both the Cloud and IoT. This paper provides an overview of cloud‐based IoT technology and seeks to fulfill the primary objective of conducting a survey on encryption algorithms in the context of the cloud and IoT. The research delves into the strategies, challenges, and variations associated with encryption in cloud and IoT systems. Additionally, this work includes a comparative analysis of the computational complexity of these techniques.

The rapid growth of electronic assessment in various fields has led to the emergence of issues such as user identity fraud and cheating. One potential solution to these problems is to use a complementary authentication method, such as a behavioral biometric characteristic that is unique to each individual. One promising approach is keystroke dynamics, which involves analyzing the typing patterns of users. In this research, the Deep Belief Nets (DBN) model is used to implement a dynamic keystroke technique for secure e-assessment. The proposed system extracts various features from the pressure-time measurements, digraphs (dwell time and flight time), trigraphs, and n-graphs, and uses these features to classify the user's identity by applying the DBN algorithm to a dataset collected from participants who typed free text using a standard QWERTY keyboard in a neutral state without inducing specific emotions. The DBN model is designed to detect cheating attempts and is tested on a dataset collected from the proposed e-assessment system using free text. The implementation of the DBN results in an error rate of 5% and an accuracy of 95%, indicating that the system is effective in identifying users' identities and cheating, providing a secure e-assessment approach.