International Journal on Recent and Innovation Trends in Computing and Communication
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PolypNet: A Lightweight CNN Framework for Early Detection of Colorectal Polyps Using Deep Learning
Full text linkColorectal carcinoma is one of the most common reasons for carcinogenic death in the current world. Identifying the polyps that are present in the colon walls is one method to prevent this illness. However, a sparse number of research studies have been done to create a computer system that will detect the indisposition in the earlier stage. The enlargement of computer vision technology has accelerated the process by retrieving helpful information from the correlated data. Nonetheless, it is important to create an untrammelled system that will be able to sport colon polyps with better accuracy and training cost. In this research, we have delineated a Convolutional Neural Network (CNN) to emphasise Adenomatous, Hyperplastic and Serrated Lesions. The experiment of the network on the basis dataset has achieved an accuracy of 99.95% within a training time of only 18 minutes and 59 seconds. Stable learning efficiency was attained by the six-layer CNN with max-pooling and dropout regularisation
Unified Hybrid Multi Cloud Modernization Playbook Governance, Interoperability, and Operational Resilience
Full text linkIn this paper, a quantitative assessment of a Unified Hybrid Multi-Cloud Modernization Playbook that aimed to enhance governance, interoperability, and operational resilience will be introduced. The analysis of the data of three large cloud providers, AWS, Azure, and Google Cloud, was conducted to quantify the effects on the performance, cost efficiency, and reliability. The outcomes demonstrate the evident positive results in terms of the workload deployment speed, deployment compliance, and fault recovery time. The analysis and automation scripts were written in Python, which allows the researcher to point out quantifiable performance improvements across cloud environments. Generally, the playbook offers a highly systematic, scaled format to companies that hope to modernize their operation without compromising good standards of governance and interoperability
IoT Based Face Recognition Using Machine Learning for Women Safety
Full text linkDespite two decades of attention to stalking in developed countries, the issue remains understudied in India, where violence against women is higher. Few reported cases receive little notice, but victims face significant financial, social, and mental losses. Research indicates stalking often precedes sexual offenses and murder, highlighting the potential to reduce overall violence against women by preventing stalking at its early stages. In this paper we have implemented women safety system on Raspberry pi via Ultrasonic sensor and Pi camera. The usage of earlier systems in daily life was both expensive and time-consuming. The voice alert safety system (subject) used by a women is described in this study. This system recognises humans using an ultrasonic sensor network. It accurately calculates the separation between the user and other human come in danger zone (distance measure 20 cm from device) or out of danger zone (distance measure more than 20 cm). It identified humans come in danger zone and give verbal feedback to let the user know. Such voice messages are delivered to the subject using the speaker. It also uses camera and facial recognition algorithms to detect faces and recognize the person and inform the user through audio output. The intention of this research work is to create a cheap, portable voice alert safety system for women’s
Spectroscopic and Chromaticity Examination of Disappearing Ink
Full text linkThe increasing prevalence of disappearing ink pens in forgery cases has posed significant challenges to forensic document examiners. This study investigates disappearing ink's spectroscopic and chromaticity characteristics to establish a reliable method for determining the relative age of writing. Using a Video Spectral Comparator (VSC-6000/hs), the absorbance, reflectance, and fluorescence spectra of disappearing ink were analyzed over time. Chromaticity values were evaluated within the CIE L*a*b* color space to quantify changes in the ink's optical properties as it faded. Results demonstrate that the L* value, representing luminance, showed a strong correlation with time in absorbance and reflectance spectra (R² = 0.82 and 0.81, respectively). Statistical analysis confirmed the linearity of L* values, making it a significant indicator for estimating the time elapsed since writing. The study concludes that absorbance and reflectance measurements are more effective than fluorescence for analyzing disappearing ink. This research provides forensic experts with a sensitive, non-destructive, and reproducible method to analyze disappearing inks and estimate the relative time of writing, offering a valuable tool for addressing forgery-related challenges
Optimized AES with GAN Model for Secure Medical Image Transmission
Full text linkThe rapid technological development and increased computational capabilities, cybersecurity risks are on the rise. This has led to a growing need for cutting-edge security algorithms, especially in fields like healthcare where medical images play a crucial role in diagnosing various conditions. As these images are frequently transmitted over the internet, safeguarding them from cyber threats is essential. The new framework for encryption is named PSO-AES-GAN(PSAGA). This paper introduces PSO based AES for encryption and generative GAN (Generative Adversarial Network) for key generation to strengthen the security of medical images. The model leverages an AES with PSO (Particle Swarm Optimization) encryption, SHA- 256 hash table, and GAN deep learning techniques. A SHA- 256 hash-table-based equation and AES with PSO enhance key entropy. Differential Huffman Compression (DHC) is utilized to compress encrypted images low-loss. The medical images have undergone testing using this model and assessed using performance metrics such as entropy, Encryption time, Decryption time, and Compared encryption algorithms such as chaotic maps, DES, AES, and Blowfish with similarity. Results show that the suggested model outperforms current methods
Heart Disease Prediction using Integrated Technology of XGBoost, Random Forest and Multi-Layer Perceptron
Full text linkCardiovascular disease remains a leading cause of death worldwide, requiring prompt and accurate diagnosis to minimize patient mortality rates. More recent developments in artificial intelligence (AI) applications have demonstrated how to enhance prognostic performance and interpretability in clinical diagnosis. This research paper analyzes the application of machine and Deep Learning models for heart disease prediction by voting with a selection of models in order to develop a strong classifier. A weighted ensemble voting approach is employed and leverage is made from XGBoost, Random Forest, and Multi-Layer Perceptron (MLP) model strengths. Further, explainability is offered by SHapley Additive exPlanations (SHAP) to facilitate model decisions, allowing feature importance and decision-making insight. The proposed methodology is supported by established performance metrics, retaining clinical relevance. Results imply that AI-based approaches can achieve elevated predictive accuracy and interpretable diagnoses, informing the creation of automated cardiovascular risk stratification
A System Architecture Design: Integrating Random Forest, Natural Language Processing, and Internet of Things to Predict Technical Carnapping in the Philippines
Full text linkTechnical carnapping, or rent-tangay, is a new deceptive scheme of stealing a car by virtue of a rental contract. Typical car anti-theft systems can only provide the location and status of the car. This means that by the time the vehicle is stolen, it is often too late. Even with the use of technology, this makes it hard for car owners and operators to prevent this new illegal scheme. This study features an architecture design for a car anti-theft system that integrates the use of natural language processing (NLP), random forest (RF) model, and internet of things (IoT) in predicting technical carnapping or rent-tangay in the Philippines. It highlights three major components, which are the black box or the hardware module, the mobile application, and the website application. The black box is responsible for gathering data inputs, including geographical location, recorded audio, and sensor outputs. The NLP pipeline is responsible for mining and processing text-based data from the audio recording. Whereas the RF model is responsible for scoring all of the inputs and using them to predict technical carnapping. The model developed in the study scored 100% in recall, 96.30% in accuracy, and 85.71% in F1 score. This implies the success and effectiveness of the design in predicting technical carnapping. This achievement significantly contributes to the body of work that focuses on developing security systems for cars, especially by effectively and efficiently implementing NLP and machine learning to the system. The study pushes the technological boundaries that can be explored in designing and developing car security systems
MCP Agents for Automated Cloud Compliance and Governance
Full text linkCloud computing has helped in reinventing the way businesses are run by offering scalable, flexible and cost-effective solutions. Yet, with more and more services on the cloud there is a difficulty of ensuring compliance and governance for these cloud environments that come from their complex, varied and dynamic nature of the clouds. Having manual, time-consuming, and error-prone traditional compliance management methods can speed up the process of compliance audits. In this paper, to resolve this problem, we implement Multi-Cloud Platform (MCP) agents using Artificial Intelligence(AI) for automatic cloud compliance and governance. MCP agents can monitor, analyze and enforce policies across multiple cloud environments to ensure compliance with industry standards, regulatory needs and internal governance principles. This system is recommended to be powered by AI, built on machine learning and natural language processing technologies which allows integrators to take better control of risk detection and mitigation. These agents can autonomously sift through massive amounts of cloud activity data, detect problematic configurations, and offer a real-time fix or suggestion Thus, it reduces manual interventions and brings a more effective, scalable and consistent set of cloud governance being enforced. This paper describes an umbrella architecture for enabling multiple compliance frameworks on MCP Agents. We also demonstrate how these agents provides cross cloud capability and can be controlled centrally with full visibility from a single dashboard. Using sophisticated, AI-driven models, these agents can predict potential compliance risks and prevent violations early on — enabling organizations to quickly identify security gaps before a breach or regulatory penalties arise. The simulation experiments confirm that our approach for AI driven MCP agents is faster and more accurate than traditional compliance checks. And, with AI plus MCP agents in the mix, it all adds up to a groundbreaking service that accelerates cloud compliance and empowers enterprises to easily strike out multi-cloud worlds armed with robust governance
Event-Driven Architectures for Real-Time Data Synchronization: Lessons from Multi-Region Cloud Deployments
Full text linkThe current paper examines the role of event-driven architectures (EDA) to assist in ensuring the synchronization of real-time data in various locations of the cloud. The current global systems have numerous challenges such as delays in information, duplicate and regional interruptions in case information is modified at various points simultaneously. This work is created with the help of AWS serverless primaries (Lambda, Kinesis, DynamoDB Streams) and provides an event-driven model of synchronization that manages millions of users.
The experiment is an analysis of the latency, throughput, and error rates over the regions in the process of live replication. It further verifies idempotence event handling and cross region restoration during the event of failures. Findings indicate that pipelines supported by an event can continue with low latency rates, high availability, and a proper level of replication regardless of network failures. There are also quantitative results, small code samples, and specific visualization charts of the paper to explain the behavior of the system in a clear manner. Production-grade implementations are tweaked into lessons that can be generalized into a blueprint that can be implemented in other enterprises. This study can be used to construct stronger, smoother, and non-conformist data pipelines when using a distributed cloud system
Innovativeness, Skill Development, Competitive Efficiency, Capacity of Hard Work and Entrepreneurial Intentions among the Students of Higher Learning Institutions - An Assessment
Full text linkThis research explores the dimensions of innovativeness, skill development, competitive efficiency, capacity for hard work, and entrepreneurial intentions among students of higher learning institutions in Tamil Nadu. In the context of a rapidly evolving job market and economic landscape, these attributes are critical in shaping the future workforce. The research aims to identify the factors that influence these traits and their interrelationships, contributing to a holistic understanding of student readiness for entrepreneurial and professional careers. Information was collected through a view of students across various disciplines in higher learning institutions, including engineering, management, and arts and sciences. The research employed valued performing to assess the levels of innovativeness and entrepreneurial intentions among students, along with soft insights to understand the underlying motivations and barriers. The accumulation indicate that innovativeness is significantly associated with exposure to practical learning experiences and a supportive academic environment. Skill development was found to be influenced by access to advanced training and mentorship, while competitive efficiency correlated with participation in extracurricular activities and internships.
The capacity for hard work, though generally high, varied according to personal resilience and external support systems. Entrepreneurial intentions were notably higher among students who demonstrated a strong alignment between their skills and market needs, as well as those who had role models in entrepreneurship. However, challenges such as lack of financial resources, inadequate institutional support, and societal expectations were identified as major obstacles to entrepreneurial pursuits. The research suggests that educational institutions should incorporate entrepreneurship education, real-world problem-solving opportunities, and skill development support systems to equip students for competitive ventures and economic growth in Tamil Nadu.This research underscores the importance of a multidimensional approach to education that not only imparts knowledge but also cultivates the essential qualities needed for innovation and entrepreneurship in a dynamic global economy