Journal of Recent Innovations in Computer Science and Technology

This paper demonstrates the practical operation of the Paillier cryptosystem in securing direct retrogression conclusion while conserving data sequestration. A customer- garçon armature is used, where sensitive input data is translated on the customer side using Paillier’s cumulative homomorphic encryption and reused on the garçon without revealing the raw values. The translated data is subordinated to a direct retro...

The proliferation of online services has brought even greater exposure to cyber-attacks, especially in the form of phishing and malicious URL-based threats that impersonate legitimate websites to steal user credentials and financial data. Traditional blacklisting and rule-based security solutions are not able to keep up with the changing phishing tactics, posing a challenge for developing intelligent and adaptive det...

The detection method described in this paper uses autoencoder-based feature learning to identify abnormal traffic patterns indicative of a Mozi infection and employs hashing techniques to track and enumerate the P2P botnet nodes. The Internet of Things (IoT) has emerged as a game-changer in today’s world, influencing numerous industries and lifelines. Detection of IoT botnets has multiple implications for the securit...

The contemporary product success is heralded through customization and personalization. The present paper proposes a machine learning-based system that enhances adaptive, personalised product configuration based on the analysis of user customization preferences, feedback, and behaviour data. The framework can learn the customer needs using clustering algorithms, predictive analytics, feature selection models, and dyn...

Precision Agriculture tries to ensure maximum use of resources to get maximum production of crops with minimum use of water, fertilizers, and agrochemicals under highly dynamic climatic and soil conditions. Most existing precision farming approaches, however, depend on static rule-like logic or are single-task machine learning models, which possess limited contextual reasoning capabilities, multi-objective coordinati...

This research presents Stock former, a Transformer-based deep learning model for financial time-series forecasting in the Indian stock market. Using hourly data from the top five NIFTY Bank stocks such as HDFC Bank, ICICI Bank, SBI, Kodak Bank, and Axis Bank, the model leverages the self-attention mechanism to capture temporal and inter-stock dependencies [3]. A Granger Causality test is employed to identify the most...

Cotton is the backbone of the global textile economy, yet it is highly vulnerable to diseases that cause substantial yield and quality reduction. Conventional manual detection is time-consuming, prone to errors, and cannot offer real-time data for monitoring agriculture at a large scale. In this paper, we present a deep learning method based on YOLOv10 for detecting and classifying the most common diseases in cotton...

This has increased the urgency to have proper emotion recognition structures that could recognize more than one co-occurring emotion due to the proliferation of textual information on social media generated by users. The purpose of this work is to create a deep learning model that will perform multi-label emotion classification with the use of Go-Emotions dataset. The overall aim is to identify five fundamental emoti...

In this paper, we investigated the most widely utilized group key management approaches, which enabled and best-effort perspective within a trusted domain, for the Internet of Things (IoT) devices. The protection of such data is extremely challenging, as a majority of IoT devices have limited computation capabilities, small memory size, and constrained battery life. Traditional security techniques are usually excessi...

Diseases of tomato leaves have a high impact on the production of crop and the yield can be reduced if the diseases are not identified at primary stage. The traditional way of detecting diseases by human experts is slow and laborious, and also less accurate in many cases, particularly for large farms. To address this issue, the paper proposes a deep learning based tomato leaf disease detection system utilizing the VG...

The need for proactive and intelligent healthcare solutions has increased due to the ageing population, chronic disease incidence, and a requirement for real-time patient monitoring, particularly in rural areas. This paper presents an Intelligent Healthcare Monitoring System incorporating fall detection, natural language understanding, prediction of health risk through hybrid machine learning, real-time data gatherin...

Distributed Denial of Service (DDoS) attacks are posing an increasing threat to SCADA systems, which are the core infrastructure for power grids, water treatment, oil and gas pipelines, and many other critical infrastructure systems. The growing connectivity of such systems and their dependence on legacy communication protocols make them quite susceptible to large-scale disruptions. This paper introduces a novel Loca...

The integration of artificial intelligence (AI) in Alzheimer’s disease detection has improved diagnostic capabilities. It enables accurate and early identification of the disease through advanced analysis of neuroimaging data, biomarkers, and cognitive assessments. The complexity of modern AI models is high and lack clinical adoption due to lack of transparency and interpretability. It creates a barrier for healthcar...

Mental health problems are becoming a significant international health issue, and a late diagnosis has, in most cases, caused serious social, economic, and clinical impacts. Early detection is consequently a requirement for timely intervention and better results. The latest developments in wearable Internet of Things (Iota) technology have enabled continuous, non-invasive measurements of physiological indicators, inc...

Classical computing is a traditional computing system currently used in digital devices like laptops, smartphones, servers and personal computers and works on binary bits concept where information represents in the form of 2 states, either 0 or 1. Classical computing follows deterministic and sequential computing approach leads to efficiently perform normal and daily computational task[3].On the another hand, quantum...

Data mining techniques are employed in online usage mining to evaluate user behavior on web pages and uncover usage patterns. There has been a dramatic uptick in interest in web usage mining from academics and industry professionals alike. The objective is to better comprehend how people use websites and cater to the requirements of web-based applications. Extensive research has already been conducted in web usage mi...

The Structure of the drone, which includes the frame, propeller, engine, power system, electronic control, and communication systemis covered as the first aspect of this article about drones and their applications. One device is called an aircraft. In addition to this other name for a UAV (unmanned aerial vehicle), it is necessary to mention that any drone can be referred to as “unmanned” if it is capable of flying w...

 Cloud computing has transformed the IT industry by enabling convenient access to data, programs, and files over the Internet. A variety of load balancing algorithms, including Round Robin, ESCE, Min-Min, Max-Min, and Throttled, are covered in the discussion along with their effects on throughput, fault tolerance, scalability, and overhead. The literature review stresses the dynamic nature of the cloud computing land...

Ad hoc networks are made up of groups of similar nodes, such as computers or embedded devices. Each node works on its own and talks to the others through a wireless channel. Networks like these are made up of groups of nodes that are either next to each other or connected by another wireless node. Nodes are grouped together along their wireless links to make clusters. Nodes that can talk to other nodes in their trans...

The Internet of Things is a vast system of interconnected devices. These gadgets are becoming increasingly commonplace in vital applications.  As a result, cybercriminals are directing more of their attention toward the IoT. To protect the IoT from intrusion, we employ Random Forest, a popular machine-learning algorithm for classification tasks, in this work. To implement the proposed method, labeled data depicting b...