International Journal on Recent and Innovation Trends in Computing and Communication
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Air Pollution Detection and Control System Using ML Techniques
Get PDFIn present times, air pollution is increasing day by day, depriving the health of many people due to the various toxic components in air. So, it is necessary to monitor and detect the levels of pollution in various areas and try to control it by taking precautionary actions. Air pollution detection and control system is all about detecting the level of pollution in a particular area based on the amount of polluting components and proposing the measures to control the pollution. Analysis is made on the regions of Visakhapatnam city in Andhra Pradesh, India and grouped based on their pollution and displayed along with each component level, reasons for the pollution depending on each component and measures that can be followed. Apart from this, we also display list of top 10 regions with the highest values for each component which can be used to identify the harmful regions based on the toxic components
Dual-Level Secured Autonomous Bank Locker System
Get PDFThe proposed development intends to establish an autonomous bank locker using industry-standard innovative locker technologies to deliver more flexible and reasonably priced semi-autonomous bank security mechanisms with minimal human intervention. In this design, there are two layers of locker security. The system proposed in this effort is a better security system regarding the number of security tiers. Its primary base is facial recognition. The first level is implemented by asking the user to input a passkey. A matrix keypad and Python programming are both employed. The user is then authorized to continue to the subsequent stage if a match is confirmed to exist. The second level was implemented using Python programming, OpenCV software, and face detection and identification techniques. To make Windows compatible with third-party apps Putty and Xming, the Raspberry Pi was linked to the laptop using IEEE 802.3 Ethernet and X11 forwarding on the UBUNTU operating system. IEEE 802.11 USB Wi-Fi was used to connect devices to the Wi-Fi network. The HAAR OpenCV standard has been used for face detection because of its better Face Acceptance and Rejection Ratio. The EIGENFACES OpenCV standard is employed for face recognition due to its efficacy, robustness, and simplicity
Intelligent Early Diagnosis System against Strep Throat Infection Using Deep Neural Networks
Get PDFThe most frequent bacterial pathogen causing acute pharyngitis is Group-A hemolytic Streptococcus (GAS), and sore throat is the second most frequent acute infection. The immunological reaction to group A Streptococcus-induced pharyngitis results in Acute Rheumatic Fever (ARF). A genetically vulnerable host for ARF is a streptococcal infection. ARF, which can affect various organs and cause irreparable valve damage and heart failure, is the antecedent to Rheumatic Heart Disease (RHD). RHD, in many countries is Cardiovascular Disease (CVD) refers to a range of conditions that affect the heart and blood vessels, including coronary artery disease, heart attack, heart failure, and stroke. It is important to note that while this approach has demonstrated promising results, further studies and validation are necessary to establish its clinical feasibility and reliability. Further research can also be done to evaluate the generalization of the model to larger and diverse patient populations. The results showed that using Image Synthesis-based augmentation improved the ROC-AUC scores compared to basic data augmentation. The proposed method could be a valuable tool for healthcare professionals to quickly and accurately diagnose strep throat, leading to timely treatment and improved patient outcomes. The experimental findings indicate that the suggested detection approach for strep throat has a high level of accuracy and effectiveness. The approach has an average sensitivity of 93.1%, average specificity of 96.7%, and an overall accuracy of 96.3%. The ROC-AUC of 0.989 suggests that the approach is effective at distinguishing between positive and negative cases of strep throat. These results indicate that the suggested detection approach is a promising tool for accurately identifying cases of strep throat
Crime Prediction and Analysis against women Using LRSRI-Missing Value Imputation and FIPSO - Optimum Feature Selection Methods
Get PDFData investigation is the method of considering crude measurements in arrange to draw conclusions around them. Many statistics evaluation techniques and tendencies had been automated into mechanical techniques and algorithms in such a manner that they provided raw statistics for human consumption. Machine learning could be a portion of artificial intelligence that permits computer frameworks to "analyze" their own statistics and improve them over time without being explicitly programmed. Machine learning algorithms can understand patterns in statistics and analyze them to make their own predictions. Lost esteem ascription is one of the foremost vital procedures in data pre-processing and it is additionally the most prepare of information examination. Ascription of lost information for a variable replaces lost information with a esteem inferred from an assess of the dispersion of that variable. Basic accusation employments as it were one suspicion. Numerous ascriptions employments diverse gauges to reflect the instability in evaluating this dispersion. In this article, The proposed method LRSRI used for impute the missing values on Crime against Women Data-set(CAW).The Linear Regression Imputation and Stochastic regression imputations are used in this method.Feature selection is another important data preprocessing techniques.This is often called attribute selection or feature selection. The most important problem in predictive modeling is the mechanical selection of features in the data. In this work,the proposed method FIPSO implemented for feature selection.This is feature importance and Particle Swarm Optimization based method.The main objective of this work is predict the crime rate against women in India based on 2001 to 2021 crime recorded against women in India.This Data set is collected from Data.gov.in.Finally The predicted result is compared with recent NCRB crime report.The proposed method LRSRI and FIPSO has given 98.34% accuracy of crime prediction.In feature,This outcome will be valuable for the crime office to control the CAW in India
Enhancing Feature Extraction through G-PLSGLR by Decreasing Dimensionality of Textual Data
Get PDFThe technology of big data has become highly popular in numerous industries owing to its various characteristics such as high value, large volume, rapid velocity, wide variety, and significant variability. Nevertheless, big data presents several difficulties that must be addressed, including lengthy processing times, high computational complexity, imprecise features, significant sparsity, irrelevant terms, redundancy, and noise, all of which can have an adverse effect on the performance of feature extraction. The objective of this research is to tackle these issues by utilizing the Partial Least Square Generalized Linear Regression (G-PLSGLR) approach to decrease the high dimensionality of text data. The suggested algorithm is made up of four stages: Firstly, gathering featured data in vector space model (VSM) and training it with bootstrap technique. Second, grouping trained feature samples using a Pearson correlation coefficient and graph-based technique. Third, getting rid of unimportant features by ranking significant group features using PLSGR. Lastly, choosing or extracting significant features using Bayesian information criterion (BIC). The G-PLSGLR algorithm surpasses current methods by achieving a high reduction rate and classification performance, while minimizing feature redundancy, time consumption, and complexity. Furthermore, it enhances the accuracy of features by 35%
Implementation of Transmission Line Fault Detection System using Long Range Wireless Sensor Networks
Get PDFThis paper proposes a fault detection system designed for transmission lines using Long-Range Wireless Sensor Network (LoRAWSN). The system is designed to detect and locate faults across transmission lines in real-time, which can significantly improve the reliability and efficiency of power transmission systems. A WSN will be built across transmission lines over an area. The faults identified by these sensor nodes is then transmitted to a central control unit, which analyses and displays the data. The LoRaWAN technology enables the WSN to cover long distances while consuming minimal power, making it ideal for monitoring transmission lines. The proposed fault detection system is evaluated through real world experiments, which demonstrate the feasibility and effectiveness of the proposed system. Overall, this paper presents a novel and practical approach for fault detection on transmission lines, which has the potential to improve the reliability and efficiency of power transmission systems
Critical issues in Leveraging Blockchain in Healthcare Sector
Get PDFBlockchain innovation has brought various benefits to the healthcare sector. Utilizing blockchains in clinical contexts will reduce handling time since when a patient signs up for a review, the complete collected data will be accessible at once because of accessibility on the distributed ledger. Also, specialists will not need to stress over the patients giving them a legit clinical history, because of their capacity to progressively see the correct, credible, and quality source-recorded information. It eliminates any likely clinical history mistakes. Similarly, the patients will not need to stress over having a second assessment from another specialist, because of the straightforwardness of the information. Having patient records on a blockchain organization will prompt individuals to know and associate with various others, across the globe, with similar ailments as they have, which is not only valuable for their well-being, but also make the patients feel acknowledged, upheld, and have reinforced determination to battle the ailment. Patients will have total independence regarding their information, and they will choose who to impart the information to. In this paper, we present all the challenges and critical issues associated with implementing blockchains in the healthcare sector
A Survey on Sugarcane Leaf Disease Identification Using Deep Learning Technique(CNN)
Get PDFThe management of plant diseases is vital for the economical production of food and poses important challenges to the employment of soil, water, fuel and alternative inputs for agricultural functions. In each natural and cultivated populations, plants have inherent sickness tolerance, however there also are reports of devastating impacts of plant diseases. The management of diseases, however, within reason effective for many crops. sickness management is allotted through the employment of plants that square measure bred permanently resistance to several diseases and thru approaches to plant cultivation, like crop rotation, the employment of pathogen-free seeds, the given planting date and plant density, field wetness management, and therefore the use of pesticides. so as to enhance sickness management and to stay up with changes within the impact of diseases iatrogenic by the continued evolution and movement of plant pathogens and by changes in agricultural practices, continued progress within the science of soil science is required. Plant diseases cause tremendous economic losses for farmers globally. it's calculable that in additional developed settings across massive regions and lots of crop species, diseases usually cut back plant yields by ten percent per annum, however yield loss for diseases usually exceeds twenty percent in less developed settings. Around twenty-five percent of crop losses square measure caused by pests and diseases, the Food and Agriculture Organization estimates. to unravel this, new strategies for early detection of diseases and pests square measure required, like novel sensors that sight plant odours and spectrographic analysis and bio photonics that may diagnose plant health and metabolism. In artificial neural networks, deep learning is an element of a broader family of machine learning approaches supported realistic learning. Learning is often controlled, semi-supervised or unmonitored. to handle several real-world queries, Deep Learning Approaches are normally used. so as to differentiate pictures and acknowledge their options, coevolutionary neural networks have had a larger result. This article will do a Leaf Disease Identification Survey with Deep Learning Methods. It takes Sugarcane leaf as an instance to our paper
Securing IoT Networks for Detection of Cyber Attacks using Automated Machine Learning
Get PDFCybercriminals are always developing innovative strategies to confound and frustrate their victims. Therefore, maintaining constant vigilance is essential if one wishes to protect the availability, confidentiality, and integrity of digital systems. Machine learning (ML) is becoming an increasingly powerful technique for doing intelligent cyber analysis, which enables proactive defenses. Machine learning (ML) has the potential to thwart future assaults by studying the recurring patterns that have already been successful. Nevertheless, there are two significant drawbacks associated with the utilization of ML in security analysis. To begin, the most advanced machine learning systems have significant problems with their computing overheads. Because of this constraint, firms are unable to completely embrace ML-based cyber strategies. Second, in order for security analysts to make advantage of ML for a wide variety of applications, they will need to develop specialized frameworks. In this study, we aim to put a numerical value on the degree to which a hub can improve the safety of an ecosystem. Typical cyberattacks were carried out on an Internet of Things (IoT) network located within a smart house in order to validate the hub. Further investigation of the intrusion detection system's (IDS) resistance to adversarial machine learning (AML) assaults was carried out. In this method, models can be attacked by supplying adversarial samples that attempt to take advantage of the defects in the detector that are present in the pre-trained model
Empirical Research on Machine Learning Models and Feature Selection for Traffic Congestion Prediction in Smart Cities
Get PDFThe development of smart cities has occurred over the past ten years. One primary goal of “smart city” initiatives is to lessen vehicle congestion. Several innovative technologies, including vehicular communications, navigation, and traffic control, have been created by Vehicle Networking System to address this problem. The traffic data gathered by smart devices aids in the forecasting of traffic in smart cities. This project created an Intelligent Traffic Congestion Management System (ITCMS) that uses machine learning techniques and traffic data from Kaggle to decrease the amount of time spent stuck in traffic. This study aims to assess feature selection methods and machine learning models for traffic forecasting in smart cities. The feature dimension is reduced using feature selection techniques, such information gain, correlation attribute, and principal component analysis. The recommended model successfully predicted traffic flow, assisting in the alleviation of congestion. The principal component analysis with random forest model outperforms the other machine learning models and has a 95% accuracy rate