Leveraging Machine Learning for Network Intrusion Detection in Social Internet Of Things (SIoT) Systems

This research investigates the application of machine learning models for network intrusion detection in the context of Social Internet of Things (SIoT) systems. We evaluate Convolutional Neural Network with Generative Adversarial Network (CNN+GAN), Generative Adversarial Network (GAN), and Logistic Regression models using the CIC IoT Dataset 2023. CNN+GAN emerges as a promising approach, exhibiting superior performance in accurately identifying diverse intrusion types. Our study emphasizes the significance of advanced machine learning techniques in enhancing SIoT security by effectively detecting anomalous behaviours within socially interconnected environments. The findings provide practical insights for selecting suitable intrusion detection methods and highlight the need for ongoing research to address evolving intrusion scenarios and vulnerabilities in SIoT ecosystems

Exploring key parameters influencing student performance in a blended learning environment using learning analytics

Understanding the factors that influence students' results in hybrid learning environments is becoming increasingly important in today's educational environment.  The goal of this research is to examine factors that influence students' academic performance as well as their level of participation in blended learning environments.  A comprehensive study was conducted with 330 interested participants from the prestigious government polytechnics of the state of Karnataka in order to achieve this goal. Our data acquisition approach relied on the administration of a meticulously crafted survey questionnaire. The conceptual framework underpinning this study seamlessly integrates Transactional Distance Theory (TDT) principles with valuable insights derived from prior research. The Welch test and one-way ANOVA (Analysis of Variance) are two statistical approaches that we used selectively to reinforce our research which produced surprising results.  These findings underscore the pivotal role played by certain specific factors. The geographical location of learners and the medium through which they pursue their studies have emerged as critical determinants significantly influencing academic performance. Aspects like the frequency of login activities and active engagement in forum discussions have been found to exert a positive influence on learners' academic performance. In contrast, the duration of sleep did not show a significant impact on performance. These insights bear tangible implications for teachers and policymakers who are dedicated to the enhancement of the quality of BL programs with the ultimate goal of enriching the overall educational experience

Semi-Supervised Domain Adaptation and Collaborative Deep Learning for Dual Sentiment Analysis

Sentiment classification is a much needed topic that has grabbed the interest of many researchers. Especially, classification of data from customer reviews on various commercial products has been an important source of research. A model called supervised dual sentiment analysis is used to handle the polarity shift problem that occurs in sentiment classification. Labeling the reviews is a tedious and time consuming process. Even, a classifier trained on one domain may not perform well on the other domain. To overcome these limitations, in this paper we propose semi-supervised domain adaptive dual sentiment analysis that train a domain independent classifier with few labeled data. Reviews are of varying length and hence, classification is more accurate if long term dependency between the words is considered. We propose a collaborative deep learning approach to the dual sentiment analysis. Long short term memory (LSTM) recurrent neural network is used to handle sequence prediction to classify the reviews more accurately. LSTM takes more time to extract features from the reviews. Convolution neural network is used before LSTM layers to extract features resulting in the reduction of training time compared to LSTM alone

TKP: Three level key pre-distribution with mobile sinks for wireless sensor networks

Wireless Sensor Networks are by its nature prone to various forms of security attacks. Authentication and secure communication have become the need of the day. Due to single point failure of a sink node or base station, mobile sinks are better in many wireless sensor networks applications for efficient data collection or aggregation, localized sensor reprogramming and for revoking compromised sensors. The existing sytems that make use of key predistribution schemes for pairwise key establishment between sensor nodes and mobile sinks, deploying mobile sinks for data collection has drawbacks. Here, an attacker can easily obtain many keys by capturing a few nodes and can gain control of the network by deploying a node preloaded with some compromised keys that will be the replica of compromised mobile sink. We propose an efficient three level key predistribution framework that uses any pairwise key predistribution in different levels. The new framework has two set of key pools one set of keys for the mobile sink nodes to access the sensor network and other set of keys for secure communication among the sensor nodes. It reduces the damage caused by mobile sink replication attack and stationary access node replication attack. To further reduce the communication time it uses a shortest distance to make pair between the nodes for comunication. Through results, we show that our security framework has a higher network resilience to a mobile sink replication attack as compared to the polynomial pool-based scheme with less communication tim

Novel technique for implementation of Color Algorithm for LED used for general illumination

Abstract LED light sources are finding more applications than conventional light bulbs due to their compactness, lower heat dissipation, and most importantly, real-time color changing capability. Color mixing provides an important means to obtain white light of controlled color temperature by combining RGB arrays of LED's. The basic principle of color mixing relies in the utilization of a strong enough diffuser where the angular spread of the diffuser exceeds the angular spread of the LED sources. Accurate control of colors for RGB LED lights is a challenging task, which includes optical color mixing, color light intensity control and color point maintenance due to LED junction temperature change and device aging. This paper evaluates a color control algorithm for LED lights with independently changeable illuminance. The algorithm adjusts the light intensity to obtain desired color with alterable illuminance. To verify the validity of the algorithm, it was applied to control of a LED module (Red, Green, Blue, and White)

Tracing footprints for a greener tomorrow; A cross-sectional study to assess the carbon footprint of the urban households of Vijayapura city

Background: The concept of carbon footprint is rooted in measuring the volume of greenhouse gases, predominantly carbon dioxide (CO2), resulting from human activities. India has witnessed a significant surge in greenhouse gas emissions due to rapid economic growth and population expansion, making it the world's third-largest CO2 emitter. This upsurge intensifies the natural greenhouse effect, leading to global temperature rise, ocean acidification, and heightened risks to human health. Objectives: To assess the Carbon Footprint generated by urban households of Vijayapura. Material and methods: This study employs a cross-sectional approach targeting urban households residing within the operational area of the urban health center in BLDE(DU), Vijayapura City. The sample, comprising 150 households, was selected via systematic random sampling. Data was collected through household visits and interviews with the family heads using a semi-structured questionnaire. and analyzed utilizing SPSS Software Version 26. Results: The analysis of carbon emissions highlights that primary emissions surpass secondary emissions. Notably, households categorized under the upper socioeconomic class exhibit a statistically significant carbon emission rate of approximately 39.47 tonnes per month. Conclusion: This study's assessment of the carbon footprint emanating from urban households illuminates the pivotal connection between day-to-day choices and the broader ecological context

Linguistic Based Emotion Detection from Live Social Media Data Classification Using Metaheuristic Deep Learning Techniques

A crucial area of research that can reveal numerous useful insights is emotional recognition. Several visible ways, including speech, gestures, written material, and facial expressions, can be used to portray emotion. Natural language processing (NLP) and DL concepts are utilised in the content-based categorization problem that is at the core of emotion recognition in text documents.This research propose novel technique in linguistic based emotion detection by social media using metaheuristic deep learning architectures. Here the input has been collected as live social media data and processed for noise removal, smoothening and dimensionality reduction. Processed data has been extracted and classified using metaheuristic swarm regressive adversarial kernel component analysis. Experimental analysis has been carried out in terms of precision, accuracy, recall, F-1 score, RMSE and MAP for various social media dataset

Real World Face Mask Detection using MobileNetV2 and Raspberry Pi

On March 12, 2020, Corona Virus Disease 2019 (COVID 19) was declared a global pandemic. Because of its quick spread from one person to another, this disease was thought to be more hazardous. Face masks have proven to be a good and effective way to stop the spread of COVID 19. Detection of Face Mask is a challenging problem. This paper proposes the method to solve this challenge by using deep learning. This work uses Multi-Task Cascaded Convolutional Neural Network (MTCNN) for detection and identification of face. MobileNetV2 is used as an object detector for mask detection. A total of 3833 images from different data sources were chosen for this work. This is later implemented using Raspberry Pi and pi cam, this setup transmits live video data from a remote location and hence the prediction of wearing mask is accomplished. The amount of information lost in the process is decreased gradually at 20th epoch is 0.0199. The accuracy by which the mask/no mask detection is increased

BHnFDIA: Energy Efficient Elimination of Black Hole and False Data Injection Attacks in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are currently being used in a wide range of applications including military areas that demand high security requirements. WSNs are susceptible to various types of attacks as they are unsupervised in nature. Since sensor network is highly resource constrained, providing security to data transmission becomes a challenging issue. Attacks must be detected and eliminated from the network as early as possible to enhance the rate of successful transmissions. In this paper, an energy efficient algorithm is proposed to eliminate Black Hole and False Data Injection Attack (BHnFDIA) to overcome black hole attack in WSNs using a new acknowledgement based scheme with less overhead. Every intermediate node check the authenticity and integrity of the received packet. The authentic packets will be forwarded and malicious packets will be discarded immediately. The proposed scheme can eliminate false data injection by outside malicious nodes and Black hole attack by compromised insider nodes. Simulation results show that the scheme can successfully identify and eliminate 100% black hole nodes. Malicious packets are immediately removed with 100% filtering efficiency. The scheme ensures more than 99% packet delivery with increased network traffic

Effect of planting periods on production potential of potato (Solanum tuberosum) varieties under aeroponics

An experiment was conducted during 2017–18 in the aeroponic unit of ICAR-Central Potato Research Institute in Shimla, Himachal Pradesh, to evaluate 3 potato (Solanum tuberosum L.) cultivar’s, viz. Kufri Mohan, Kufri Lauvkar and Kufri Himalini growth and production behaviour under 2 different planting periods spaced 10 days apart during the autumnal season. The yield per plant and the total number of mini-tubers were shown to be strongly impacted by the planting season as well as the cultivar, according to the findings. The average number of tubers harvested was 32.7/plant in early (10th September) planting as against 21.2 mini-tubers/plant in late (20th September). Similarly, yield per plant was significantly higher (67.67 g/plant) with early planting than with late planting (35.99 g/plant). The production behaviour of the potato varieties under consideration varied significantly. The maximum yield/plant and mini-tuber numbers were recorded in Kufri Lauvkar (34.17 and 73.12 g/plant, respectively), which were significantly higher than the remaining 2 cultivars, which were statistically at par for both the number of yield/plant and minitubers. A delay in the planting of 10 days under aeroponics results in a significant reduction not only in the vigour of plants but subsequently in the number as well as weight of mini-tubers harvested. Thus, for attaining higher rates of multiplication under aeroponics during the autumn season, planting should not be delayed beyond 10th September in a hilly temperate wet zone