Kartheeban Kamatchi

Medical image transmission across cloud-enabled healthcare networks has become increasingly common in modern telemedicine systems. However, existing approaches suffer from increased computational overhead, inadequate resistance against statistical attacks, poor reconstruction quality and so on. Hence, an efficient secure medical image transmission framework that integrates compression and encryption is needed. This work presents a ‘denoising-compression-encryption’ framework, which integrates Huang’s Block Truncation Coding (HBTC) with Gauss-iterated map with Inertia Weight-based Chimp Optimization Algorithm (GIW-CHO) optimized Elliptic Curve Cryptography (ECC) and termed as ‘OKBECC’. Initially, the images are denoised for intensity stabilization and then HBTC is employed to binarize and decorrelate the images. Finally, encryption is performed by ECC using the keys optimized by GIW-CHO algorithm. HBTC greatly influences the interpixel-correlation and histogram structure by reducing the entropy to approximately one before encryption, which makes it unpredictable. GIW-CHO helps explore larger key space and provides stronger optimal keys, which helps in attaining better quality of reconstructed image. The proposed work is tested across different image modalities including X-ray, ultrasound, Magnetic Resonant Imaging (MRI), Computed Tomography (CT) and histopathology upon standard performance measures. The proposed OKBECC shows an average NPCR, UACI and cipher entropy of about 99.6%, 33.4% and 7.99 bits, respectively, which outperforms multiple existing algorithms. In addition, the image reconstruction quality is proven with PSNR of about 68 dBs, which is clinically acceptable.

Cardiovascular diseases (CVDs) continue to have a negative impact on global health, which highlights the need for accurate and efficient prediction methods. Machine learning (ML) techniques as tools for forecasting CVD has recently showed potential. This paper presents a comprehensive analysis of research trends in the field, focusing on visualizing the impact of ML in cardiovascular disease prediction. We used data visualization techniques to identify patterns and trends in an extensive database of scholarly publications on this subject that were published in Scopus between 1991 and 2023. The analysis reveals a substantial growth in research output, demonstrating the growing demand for ML-based CVD prediction. It reveals essential stakeholders and potential collaborators while highlighting the institutions and authors who have contributed most to this domain. The study also identifies high-impact journals that have published significant research in this domain, facilitating researchers in selecting appropriate outlets for dissemination. The study helps researchers identify the most critical areas for further research and fosters cooperation among subject-matter experts by offering insightful information about machine learning-based cardiovascular disease prediction development. The data is analyzed using the tools VOSviewer and Biblioshiny.

This project proposes the development of a comprehensive real-time biodiversity monitoring system that harnesses sound data through a network of acoustic sensors and advanced artificial intelligence algorithms. The system analyzes sound recordings from various ecosystems to identify and classify different species, providing valuable insights into ecosystem health and biodiversity patterns while facilitating the detection of subtle changes in species presence and behavior over time. By addressing critical challenges such as noise pollution and species overlap, the system employs sophisticated filtering and classification techniques to ensure accurate and reliable monitoring, distinguishing between natural sounds and anthropogenic noise. Ultimately, this initiative aims to enhance our understanding of biodiversity dynamics and provide essential information to support effective conservation strategies and inform policy decisions, empowering stakeholders with actionable insights to protect and preserve vital ecosystems.

As able-bodied people, we often take our vision for granted. For people who are visually impaired, however, their disability can have a significant impact on their daily lives. We are developing proprietary headgear that will help visually impaired people navigate their surroundings, identify objects and people, read text, and avoid obstacles. The headgear will use a combination of computer vision, distance estimation with ultrasonic sensors, voice recognition, and voice assistants to provide users with real-time information about their environment. Users will be able to interact with the headgear through voice commands, such as ''What is that?'' to identify an object or ''Navigate to the front door'' to find their way around. The headgear will then provide the user with a verbal description of the object or spoken navigation instructions. We believe that this headgear has the potential to make a significant difference in the lives of visually impaired people, allowing them to live more independently and participate more fully in society.

This research can focus on virtualization technology supporting green computing in virtualization to allocate data center resources during the application process. By developing heuristics to avoid system overload for attained system efficiency. Experiment results and Tracedriven simulation demonstrate good performance. This proposed research work has been enhanced by the name as hybridized of multi-objective optimization dynamic resources implementation. The simulation findings demonstrate that obtained how to deploy a virtual machine by scheduling has a longer life span than the scheduling approach for energy savings and multi-virtual machine redistribution overhead. Without keeping the server hardware or any adjustments in configuration, an infrastructure can deliver the model to provide industry users with remote access to server resources aimed by resource scheduling within side the contemporary cloud computing surroundings can be attained in resource allocation for energy-saving systems. In this research work, the spaceshared policy and time-shared policy can be integrated with multi-objective optimization dynamic resources allocation and an experimental result show that scheduling can converge faster and produce comparable multi-objective optimization outcomes at the same calculation size.

Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used. But classifying many intricate pathology images by hand will be challenging for pathologists. The lack of labeling data makes the system difficult to build and costly. This article designs a Honey Badger Optimization with Deep Learning based Automated Osteosarcoma Classification (HBODL-AOC) model. The HBODL-AOC technique’s goal is to identify osteosarcoma’s existence using medical images. In the presented HBODL-AOC technique, image preprocessing is initially performed by contrast enhancement technique. For feature extraction, the HBODL-AOC technique employs a deep convolutional neural network-based Mobile networks (MobileNet) model with an Adam optimizer for hyperparameter tuning. Finally, the adaptive neuro-fuzzy inference system (ANFIS) approach is implemented for the HBO (Honey Badger Optimization) algorithm can tune osteosarcoma classification and the membership function (MF). To demonstrate the enhanced classification performance of the HBODL-AOC approach, a sequence of simulations was performed. The extensive simulation analysis portrayed the improved performance of the HBODL-AOC technique over existing DL models.

Due to the blooming of Industrial 4.0 such as internet of things (IoT), cloud computing, industrial IoT (IIoT), and artificial intelligence (AI), with their innovative ideas and opportunities, the cyber attacker's modus operandi against the cyber defense triage is incredible. The genre of advanced persistent threat (APT) actors/group are equipped with sophisticated and substantial resources of tools, techniques, and procedure (TTP) at a breakneck pace. The IoT gadgets such as sensors, intelligent devices, and various rapidly emerging resources with energy, memory, and processing power are exponentially prone to multiple vulnerabilities. The nature of IIoT prompts heterogenous and rapid changes ranging the vulnerabilities from simple to complex attacks. APT menace follows the covert TTPs to target the asset of any organization like the government, military, or financial industry.