Miracle-dz/UniFAR-FTUs huggingface dataset:
A Stacking Ensemble Learning Model for Rainfall Prediction based on Indian Climate

Machine learning (ML) techniques, which are becoming more popular, have been used in different climates to show which ones are better. On top of that, the ensemble learning model, which takes the best parts of different ML models and puts them together, deserves more attention. In this study, a stacking-based ensemble learning model is used to predict rain based on Indian climate parameters. Rainfall is vital for agriculture. Rainfall has been hard to predict in recent years because the weather is always changing. Predicting rain makes people more aware and helps them plan ahead so they can protect their crops from rain. There are now a lot of ways to predict rain. The goal of this paper is to give clients from different fields information about climate so they can understand how important changes in climate and the environment are. Precipitation, temperature, and other aspects of the atmosphere, such as humidity, etc. Estimating how much rain will fall is a very important question in the science of meteorology. Linear Regression, Random Forest, Logistic Regression, Extreme gradient boosting (XGB), and Support vector regression (SVR), are used as base models. As a second-layer learner, the weighting algorithm combines the results from the base models to make predictions. For testing, daily observations were thought about. We used India’s rainfall data to test the accuracy of regressors such as SVM, Linear Regression, Logistic Regression, Random Forest, Gradient Boosting, and XGBoost. Our goal is to get the best possible result and a more accurate prediction of rainfall. To do this, we stacked and ensembled the above-mentioned regressors, and the accuracy of these hybrid models increased exponentially, with a maximum accuracy of 81.2%. This means that using hybrid models helps predict rainfall more accurately. Overall, performance evaluation measures show that the proposed stacking ensemble multi-ML model can provide a flexible and reasonable framework for making predictions that can be used in other regions.
IoT Based System for Manhole Monitoring and Management

The purpose of a smart world is to provide society with cleaner and better facilities in the future. It's worth giving thought to smart subsurface infrastructure. Putting in place a smart world, Auditing the drainage systems is indispensable for maintaining the city's cleanliness and wellness. Since manual monitoring is inadequate, drainage issues are managed gradually and take more time to address. The system, which uses a wireless sensor network made up of various sensor nodes, is created to address all these problems. The proposed method utilizes cloud services to monitor the state of the manhole using various kinds of sensors. Some helpful manual analytics may be created for the welfare of the population with the aid of the data collected from the sensors. It is low cost, low maintenance, and Internet of Things based. With the use of this method, the danger of mortality for manual scavengers who clean the subterranean drains are being warned and provides information when the manhole cover is in open.
SPIDER: a new balloon-borne experiment to measure CMB polarization on large angular scales

We describe SPIDER, a novel balloon-borne experiment designed to measure the polarization of the Cosmic Microwave Background (CMB) on large angular scales. The primary goal of SPIDER is to detect the faint signature of inflationary gravitational waves in the CMB polarization. The payload consists of six telescopes, each operating in a single frequency band and cooled to 4 K by a common LN/LHe cryostat. The primary optic for each telescope is a 25 cm diameter lens cooled to 4 K. Each telescope feeds an array of antenna coupled, polarization sensitive sub-Kelvin bolometers that covers a 20 degree diameter FOV with diffraction limited resolution. The six focal planes span 70 to 300 GHz in a manner optimized to separate polarized galactic emission from CMB polarization, and together contain over 2300 detectors. Polarization modulation is achieved by rotating a cryogenic half-wave plate in front of the primary optic of each telescope. The cryogenic system is designed for 30 days of operation. Observations will be conducted during the night portions of a mid-latitude, long duration balloon flight which will circumnavigate the globe from Australia. By spinning the payload at 1 rpm with the six telescopes fixed in elevation, SPIDER will map approximately half of the sky at each frequency on each night of the flight.
Heart Attack Prediction Using Artificial Neural Networks

Heart disease is responsible for 12 Million deaths yearly according to the World Health Organization. The National Crime Records Bureau estimated that the number of deaths caused by heart attack has been increasing since 2014. In that year 18,309 deaths occurred due to heart attack disease, year after year the number of deaths are growing and stated 28,005 lives in 2019, up by 53% in 5 years. With growing population diagnosing and starting treatment at the early stage is difficult. The annual deaths from cardiovascular disease in India is from 2.26 to 4.77 million during 1990 to 2020. The occurrence of heart disease in India is around 1.6% to 7.4% in rural and 1% to 13.2% in urban population. The intention of the study is to identify the features of heart disease and predict the risk using Artificial Neural Networks(ANN). Data analysis is done in Python using Jupyter Notebook to verify the Artificial Neural Networks.
Brain Tumor Classification with Inception V3 Network Model Using Transfer Learning

One of the main pieces of clinical picture investigation is_brain tumor. Since extensive tumor growth are believed to be forerunners to disease, precise cerebrum cancer arrangement can save lives. Accordingly, strategies in light of CNN are ordinarily utilized for grouping malignant tumors. Be that as it may, there is an issue: CNNs are adjusted to tremendous measures of preparing information to give predominant outcomes. This is while move learning proves to be useful. In this exploration, we have a three-class profound learning design for recognizing Glioma, Meningioma, and Pituitary tumors, these are normal type of brain cancers. Our approach, which utilizes the thought of move learning, utilizes a pre-prepared InceptionV3model for ordering cerebrum X-ray pictures by separating qualities from them. The recommended approach accomplishes a mean characterization exactness of close to 100%, surpassing all earlier methods tried with a patient-level five-overlap cross-approval methodology on figshare’s CE-X-ray dataset. In this review, other execution measurements including as accuracy, review, and F-score are additionally utilized.
Stubble Burning Detection and Prevention using Machine Learning and Blockchain

Stubble burning in India contributes significantly to air pollution by emitting harmful gases and particles into the atmosphere. This annual practice is responsible for the severe pollution that envelops the country every winter, particularly in the National Capital Region. Lower temperatures and steadier air in winter exacerbate the impact of rice stubble burning. However, research on using machine learning with blockchain to address this problem is limited. This study aims to provide a conceptual framework for using machine learning and blockchain to prevent and detect stubble burning. With the help of machine learning and satellite images, areas originating the pollution can be identified. This can be done with pre-processed images sent to pre-trained models to identify the place of origin of the pollution. If the pollution crosses a threshold limit, then the location will be stored in the blockchain. A decentralized application will be created from where these details can be accessed by concerned authorities.
An Efficient ADA Boost and CNN Hybrid Model for Weed Detection and Removal

This paper aims to improve weed detection and removal-using AI based techniques such as convolution neural network (CNN). Weed is an unwanted plant developing wild, especially one that grows on developed ground to the rejection of the ideal yield. Strong and positive weed affirmation is a fundamental for achieving higher yield in agriculture. Due to the low weed control rate and the availability of poor weed removal techniques in the wheat sector we have proposed a weed control program in wheat fields marked on the RGB combination of image elements with key features. Earlier research papers talks about convolutional Neural networks works in leaf detection but in this paper CNN model is taken together with AdaBoost estimation to improve the weed distinguishing proof execution. The strategy explores the limits of the 2-D topographical highlights shed from RGB pictures while seeing grass weeds like wheat. Test results showed that the weed assurance rate in the planting phase was 0.2 and the stabilization rate was 88%. The weed demand rate in the composting phase was 0.69 and the weed detection rate was 81.08%. Test Results shown by algorithm during process was having 94.7% accuracy. Combination of Both the algorithms used gives a high accuracy in estimation of different types of weeds than previously used strategies. The system used in the agriculture area for weed detection.
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks

Network Intrusion Detection System (NIDS) is an essential tool in securing cyberspace from a variety of security risks and unknown cyberattacks. A number of solutions have been implemented for Machine Learning (ML), and Deep Learning (DL) based NIDS. However, all these solutions are vulnerable to adversarial attacks, in which the malicious actor tries to evade or fool the model by injecting adversarial perturbed examples into the system. The main aim of this research work is to study powerful adversarial attack algorithms and their defence method on DL-based NIDS. Fast Gradient Sign Method (FGSM), Jacobian Saliency Map Attack (JSMA), Projected Gradient Descent (PGD) and Carlini & Wagner (C&W) are four powerful adversarial attack methods implemented against the NIDS. As a defence method, Adversarial Training is used to increase the robustness of the NIDS model. The results are summarized in three phases, i.e., 1) before the adversarial attack, 2) after the adversarial attack, and 3) after the adversarial defence. The Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS-2017) dataset is used for evaluation purposes with various performance measurements like fl-score, accuracy etc.
Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India

The COVID-19 Pandemic has been around for four years and remains a health concern for everyone. Although things are somewhat returning to normal, increased incidence of COVID-19 cases in some regions of the world (such as China, Japan, France, South Korea, etc.) has bred worry and anxiety in world, including India. The scientific community, which includes governmental organizations and healthcare facilities, was eager to learn how the COVID-19 Pandemic would develop. The current work makes an attempt to address this question by employing cutting-edge machine learning and Deep Learning algorithms to anticipate the daily incidence of COVID-19 for India over the course of the next six months. For the purpose famous timeseries algorithms were implemented including LSTM, Bi-Directional LSTM and Stacked LSTM and Prophet. Owing to success of hybrid algorithms in specific problem domains- the present study also focuses on such algorithms like GRU-LSTM, CNN-LSTM and LSTM with Attention. All these models have been trained on timeseries dataset of COVID-19 for India and performance metrics are recorded. Of all the models, the simplistic algorithms have performed better than complex and hybrid ones. Owing to this best result was obtained with Prophet, Bidirectional LSTM and Vanilla LSTM. The forecast reveals flat nature of COVID-19 case load for India in future six months.
Smart Contracts Resilient against Malicious Attacks in Permission-less Blockchain

The applications of Blockchain are rapidly growing across all the sectors and blockchain has become a one- stop solution to achieve data integrity. For both small and large businesses, blockchain can be used to enhance security, privacy, and data openness. A block gets added in the blockchain after reaching an agreement among the peers using a consensus algorithm. In a permission-less blockchain, the peers can behave maliciously. A consensus algorithm should prohibit malicious nodes/peers from adding invalid blocks to the blockchain, controlling the network while also granting equal rights to all nodes and mandating their participation in the consensus process. Smart contracts are the programs stored in a blockchain that run when specific conditions are met. There are certain attacks on blockchain smart contracts — Reentrancy attacks, Arithmetic overflow/underflow attack, Short address attack, Denial of service etc. The root cause of such attacks on smart contracts are the vulnerabilities existing in the smart contracts. This paper aims at correcting the vulnerabilities in the smart contracts thus eliminating such malicious attacks in the permission-less blockchains.
Analysis of Bidirectional Dual Input Single Output DC-DC Converter for EV Application

This paper proposes the charging application of electric vehicle(EV) using Bidirectional dual input single output (BDISO) dc-dc converter. BDISO converter is used to convert direct current(DC) from one voltage level to another voltage level. A dual input mechanism where one dc input is taken from solar PV array while the other dc input is taken from utility grid where AC is converted to DC with the help of rectifier and are given to the BDISO DC-DC converter. The resultant DC from the BDISO is given to the battery and used for the purpose of charging the EV. The running of the converter along with the charging criteria is discussed in this paper through the simulation model in simulink.
Detection and Classification of Pneumonia Using Deep Learning by the Dense Net-121 Model

A chronic respiratory disease known as pneumonia can be devastating if it is not identified and treated in a timely manner. For successful treatment and better patient outcomes, pneumonia must be identified early and properly classified. Deep learning has recently demonstrated considerable promise in the area of medical imaging and has successfully applied for a few image-based diagnosis tasks, including the identification and classification of pneumonia. Pneumonia is a respiratory illness that produces pleural effusion (a condition in which fluids flood the lungs). COVID-19 is becoming the major cause of the global rise in pneumonia cases. Early detection of this disease provides curative therapy and increases the likelihood of survival. CXR (Chest X-ray) imaging is a common method of detecting and diagnosing pneumonia. Examining chest X-rays is a difficult undertaking that often results in variances and inaccuracies. In this study, we created an automatic pneumonia diagnosis method, also known as a CAD (Computer-Aided Diagnosis), which may significantly reduce the time and cost of collecting CXR imaging data. This paper uses deep learning which has the potential to revolutionize in the area of medical imaging and has shown promising results in the detection and classification of pneumonia. Further research and development in this area is needed to improve the accuracy and reliability of these models and make them more accessible to healthcare providers. These models can provide fast and accurate results, with high sensitivity and specificity in identifying pneumonia in chest X-rays.
Robust analysis and spectral-based deep learning to detect driving fatigue from EEG signals

Driver fatigue is a major cause of traffic accidents. Electroencephalogram (EEG) is considered one of the most reliable predictors of fatigue. This paper proposes a novel, simple and fast method for driver fatigue detection that can be implemented in real-time by using a single-channel on the scalp. The study has two objectives. The first consists of determining the single most relevant EEG channel to monitor fatigue. This is done using maximum covariance analysis. The second objective consists in developing a deep learning method to detect fatigue from this single channel. For this purpose, spectral features of the signal are first extracted. The sequence of features is used to train a Long Short Term Memory (LSTM), deep learning model, to detect fatigue states. Experiments with 12 EEG signals were conducted to discriminate the fatigue stage from the alert stage. Results showed that TP7 was the most significant channel, which is located in the left tempo-parietal region. A zone associated with spatial awareness, visual-spatial navigation, and the cautiousness faculty. In addition, despite the small dataset, the proposed method predicts fatigue with 75% accuracy and a 1.4-second delay. These promising results provide new insights into relevant data for monitoring driver fatigue.
Motion Pattern Recognition of Lower Limb Exoskeleton Based on SAPSO-SVM

Accurate motion pattern recognition is the key to achieving human-computer cooperative control of the lower limb exoskeleton. This paper puts forward a motion pattern recognition method of lower limb exoskeleton based on an optimized support vector machine (SAPSO-SVM) via inertial sensor. This method introduces simulated annealing (SA) mechanism into particle swarm optimization (PSO) algorithm, which solves the problem that PSO algorithm is easy to converge locally to a certain extent, so as to acquire better parameters of classification model. Based on the RMS feature values of joint angle signals, we classified and recognized different lower limb motion patterns. The results reveal that the average recognition accuracy of SAPSO-SVM in single motion pattern is approximately 96.93%, and the Kappa coefficient is 0.9617, which has excellent consistency. The SAPSO-SVM method can further improve the effect of lower limb exoskeleton motion pattern recognition, and has good application value.
Detection of Invisible Solar PV Installations in Power Distribution System: A Review

Climate change has squeezed people to have massive coordination in carbon neutrality. Clean energy must prevail over power demand to achieve our goal. The net zero carbon emission roadmap encourages the energy sector to produce energy from alternative energy resources. Solar Photovoltaic (PV) systems, a distributed energy resource, are a promising solution to produce power with less carbon emissions. However, the high adoption of solar PV systems can cause negative impacts on distribution networks. The network operators should realize the capacity of distributed energy resources in their systems to maintain power supply and perform active local energy management. This study aims to fill the gap in the detection of invisible solar PV installations. In this paper, proposed PV identification and PV capacity estimation techniques are investigated. The processes of study are determined and summarized. The coming steps of this research area will be discussed in possible ways.
Stock Market Prediction Using Sentiment Analysis and Incremental Clustering Approaches

Sentimental analysis is one of the techniques of Natural Language Processing. It helps us to determine the polarity of the given data: Negative, Positive or Neutral. We know that predicting the prices of Stocks are hard as they are Unstable. Sentimental Analysis helps us to predict the prices of Stocks. The stock prices can be predicted by taking raw data from various social media platforms and these are converted into valuable data by using Sentimental analysis. We observed that we can get more accuracy when we supply more data. DB Scan is an algorithm which is used to form clusters dynamically with the help of eps value. The number of clusters formed will be dependent on the eps value given to the algorithm. And comparing the results of different algorithms tells us which algorithm is best for stock price prediction. Developing the DBSCAN from scratch will gives the flexibility to change the algorithms accordingly and also, we can use the values like centroids in the model. and these results are compared with the algorithms like KMEANS, LSTM, CNN, with the parameters like MAE, MSE, RMSE.
An Extended Fr echet Distribution: Properties and Applications

In this paper, we introduce an extended four-parameter Fr´echet model called the exponentiated exponential Fr´echet distribution, which arises from the quantile function of the standard exponential distribution. Various of its mathematical properties are derived including the quantile function, ordinary and incomplete moments, Bonferroni and Lorenz curves, mean deviations, mean residual life, mean waiting time, generating function, Shannon entropy and order statistics. The model parameters are estimated by the method of maximum likelihood and the observed information matrix is determined. The usefulness of the new distribution is illustrated by means of three real lifetime data sets. In fact, the new model provides a better fit to these data than the Marshall-Olkin Fr´echet, exponentiated-Fr´echet and Fr´echet models.
Effective Feature Extraction via N-Skip Gram Instruction Embedding Model using Deep Neural Network for designing Anti-Malware Application

Due to increase in third party software release in open licensed platform, there is widespread on malware integration in such downloadable applications targeting individual or Corporate’s private data’s. On the other hand, due to variety of malware’s increase in market, it’s difficult to identify the new malware’s embedded in it. Alternatively, feature extraction and processing becomes very difficult for categorizing benign and malicious files altogether. By considering the above issues, this papers proposes an effective Instruc2vec framework for effective detection of malware’s dynamically. Existing works is demonstrated with static malware’s which lacks in identifying new types of malware’s. Initially, open licensed software’s are converted to instruction format via IDA Pro [19]. Extracting the features in instructions is a well-challenging task and feature representation also requires domain expert knowledge. This framework deploys Deep Neural network model naming convolutional neural networks to extract the operational opcodes from the instructional file. This framework extract the hidden patterns also in the file by dynamic learning process. Instruc2vec follows the word2vec process for feature selection and extraction procedures. In this experiment, real data collected from Linux popular packages and third party software downloads are used with different optimization and architecture to ensure the correctness of the application.