Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Prediction of Drugs using Machine Learning Techniques

In the era of increasing demand of data analytics in drug discovery domain, processing the complex high dimensional drug datasets is a challenging research area. High dimensionality refers to dataset having more number of columns than rows in which there may be irrelevant features that are futile. For reducing high dimensionality issue, feature selection techniques are widely adopted. Feature selection aims to identify relevant subset of features from original feature set before the training the machine learning model. From variety of feature selection techniques available one can select according to his requirement that can include faster processing, low resource consumption, high accuracy etc. In this paper, a high dimensional drug dataset is used to train the machine learning model. The rationale is to analyze and compare the performance of various feature selection techniques namely Pearson Correlation Coefficient, Chisquare test, Lasso, Treebased, Anova-f to produce best predictive performance. When a feature selection method is applied, the results are tested using K fold cross validation with state-of-the art machine learning algorithms. These numerical results of methods are presented and compared to acquire insight about these methods leading to better generalization and accuracy and also in making better future decisions.
Clustering Point Process Based Network Topology Structure Constrained Urban Road Extraction From Remote Sensing Images

To extract complicated road network from remote sensing images on urban scenes, this article presents a clustering point process (CPP) based network topology structure constrained road extraction algorithm. Firstly, the CPP is constructed to model the feature points, such as endpoints, bends, and crossroads in a road system. Based on that, an initial network topology structure is constructed by connecting the points with lines. Then, according to the network structure characteristic and the spectral characteristic of road, a network topology structure constraining model and a spectral measurement constraining model are constructed, respectively. By combining the models above, a road extraction model is built under the framework of Bayes’ theorem. Finally, to simulate from the road extraction model and extract an optimal road network, a solution strategy, reversible jump Markov Chain Monte Carlo (RJMCMC) simulation algorithm with related transfer operations, is designed according to the CPP and network topology structure. Several high-resolution remote sensing images on urban scenes are tested. According to a buffer evaluation method, and compared with the comparing algorithms, accuracy and extraction rate of results from the proposed algorithm are increased by 10.86% and 8.75% on average, respectively. It is proved that the proposed algorithm can extract the complicated road network effectively.
Outlier Analysis Based Intrusion Detection for IoT

With the world becoming more and more advanced in technology, every section of our society is dependent on computers and internet. Many interactions, data storage and data retrieval takes place over the internet. And with so many interactions comes the need to secure this data. Many researchers have built the Intrusion Detection Systems for the same. Many networks exist. One such network is of IoT. Furthermore, Internet of Things (IoT) is a fast growing network in which objects interact with other objects via the Internet. And IDSs are also required to protect such objects from unauthorised access and other vulnerabilities. So many physical objects including cars, refrigerators, watches, household objects, etc. interact with one another. There is high scope of expansion of such network in future and new attacks will come forward. For such new attacks, new Intrusion detection approaches will be explored. Here we explore some existing IDSs for IoT. We have also done comparative analysis of different IDSs for IoT based on different features like placement strategy, type, attacks handled, etc. Then we implemented 2 well know attacks, DIS attack and Version attack, on Cooja to study their impact on the network by analysing power consumption values of the nodes. Through statistical analysis and outlier detection we found out how a malicious node affects other nodes in the network.
Automated Detection of COVID-19 from X-ray Images using Deep Convolutional Neural Networks

Coronavirus, also known as COVID19, is a dangerous disease that has put many people’s lives in jeopardy around the world by damaging the lungs directly. The detection of coronavirus is a challenging medical procedure due to its increasing cases. Currently, the use of x-ray images for coronavirus diagnosis is commonly used. Recently, various deep learning based models have been used for image classification. These models have generated competitive results in terms of feature selection and classification. In this article, we proposed a set of seven pretrained neural network models (VGG16, VGG19, InceptionV3, ResNet50, Xception, DenseNet121 and InceptionResNetV2) for the detection of coronavirus infection using chest X-ray images collected from an open source. It was observed that out of these models, pretrained DenseNet121 yielded highest classification accuracy of 97% for the particular dataset.
The Application of Artificial Intelligence Technology in the On-site Decision System of Sports Competitions

In recent years, artificial intelligence technology is deepening and expanding, and many industries begin to use artificial intelligence technology to improve efficiency and accuracy. Artificial intelligence technology is more and more widely used in the field of sports. Machine vision, infrared technology, electronic induction technology and computer control technology in artificial intelligence can play a role in all aspects of the field of sports. Unlike other computer technologies, artificial intelligence can imitate the human brain for decision-making. Neural networks in artificial intelligence technology can reason based on Hebb rules and a large number of factual data. Moreover, the genetic algorithm in artificial intelligence can be continuously improved based on the coding of individuals in the population and the determination of fitness function, and the decision-making can be more and more accurate. The ability of artificial intelligence to make decisions can bring a new situation to sports competition. The fairness of sports competition will be improved because of artificial intelligence technology, and each team can make decisions according to the strategy analyzed by artificial intelligence technology.
Double Ridge Horn Antenna with Curved Dielectric Loading for Microwave Imaging Applications

This work describes a compact Double Ridge Horn (DRH) antenna utilized for ultra-wideband (UWB) Microwave Imaging (MI). The proposed DRH is appended with a dielectric lens in the flared region to improve its radiation performance. The suggested antenna achieves a wide impedance band from 1.2 GHz to 14 GHz with good impedance matching. The described antenna also attains a peak gain of 16 dBi at 8 GHz. The applicability of the put forward antenna for microwave imaging applications is tested by checking its Specific Absorption Rate (SAR) value and the antenna has been utilized to image a tumor in a realistic planar breast phantom.
Adaptive Algorithms Comparison and Analysis for Beamforming Application in Smart Antenna

In the field of wireless communications effective and authentic usage of spectrum will be an added advantage to achieve the desired capacity for a communication system. So minimizing the interference within the channel would give a greater dominance over the increase of channel capacity and the signal bandwidth. This means that improving the gain in the direction of arrival (DOA) and inserting null values at interference points is critical to increasing the capacities of a radio channels, which is why smart antennas are so important. To get the optimal weight vector of antenna array components for beamforming, adaptive algorithms play a vital role. When antenna elements in a uniform linear array were rearranged, we assessed the performance of three methods: the MATLAB-based Least Mean Square (LMS), the LMF-based LMF, and the LMMN-based LMMN techniques.
QuickLogS: A Quick Log Parsing Algorithm based on Template Similarity*

Logs are widely used in network security and management because they record runtime details in IT systems. It is difficult to gain insights from raw unstructured logs, so many researches first parse raw logs into structured templates. However, as the volume of logs grows rapidly, efficiency becomes a major concern in log parsing. In this paper, we propose a quick log parsing algorithm QuickLogS based on template similarity. QuickLogS utilizes regular expressions to replace the variables with wildcard and filters the reduplicate data to parse huge volume of unstructured logs into finite structured templates. To improve parsing efficiency, SimHash algorithm and Hamming distance are used to merge the similar templates of the same length. To the best of our knowledge, we are the first to apply the SimHash algorithm to log parsing. Besides, different with other work, we also merge the similar templates of different lengths based on the cosine similarity algorithm, which contributes to improve the parsing accuracy. QuickLogS is evaluated on six real public log datasets, and compared with four state-of-the-art log parsing algorithms. The experimental results show that QuicklogS outperforms the other parsers in terms of efficiency and accuracy.
A Wearable Health Monitoring System Using Arduino and IoT

In present days IoT plays an important role in the healthcare systems, remote health care monitoring has evolved at such a rapid pace, due to increased use of wearable sensors and smartphones. Even if the doctor is a significant distance away, IoT health monitoring aids in illness prevention and accurate diagnosis of one’s present state of health. This paper describes a wearable health monitoring system, which can continuously monitor the patient’s heartbeat, temperature, and other basic room parameters by Arduino. The obtained output is synced to the patient’s Thingspeak account, allowing the doctor to analyse the data.
Water Quality Identification System Using Arduino

Water is one of the main resources for humans and animals in their daily life. On average every human drink 4 to 5 liters of water per day. Drinking plenty of water can avoid many problems that they face in their daily life like kidney stones, Extra weight, … etc. Many of the rural area people and schools are not aware of the quality levels of water and their quality parameter values, by this they are taking the water that is available in their surrounding or storing. The government provided water which in some areas may not filter the water properly and drinking such impure water many people in rural are affected to diseases. Through this system, users can know whether the water is drinkable or not. In this module, the system consists of three sensors and a display which is included in a bottle and a display placed outside of the bottle which shows quality status of the water and which is safe for drinking or not. When we pour a sample of water in the bottle the sensors provide respective PH value, Chlorine value, Turbidity value and the values are displayed in the display along with the statement whether the water is safe or unsafe.
Achieving Efficient and Privacy-preserving Biometric Identification in Cloud Computing

Biometrics identification has been used in a growing number of fields in recent years, since it is more secure, classified and convenient. With the development of cloud computing, database systems are able to upload large amounts of biometric data to cloud server for storage and identification to save local memory and improve computational efficiency. However, this involves potential privacy concerns because of the introduction of third-party platforms. In this paper, we achieve computational and communication efficiency in biometric identification, while preserving the privacy of data. Specifically, the database system firstly encrypts all biometric data and query data. Then, it sends the ciphertext to a cloud server to carry out matching tasks. Finally, the cloud server returns the index of final matches to the system so that it can check whether the biometric vector is legal or not. Detailed security analysis indicates that the proposed scheme can resist powerful attacks. Beyond that, Experiments show that the scheme is more efficient in computation and communication than stat of art biometric identification schemes.
Backhaul-Aware Storage Allocation and Pricing Mechanism for RSU-Based Caching Networks

Remarkable prevalence of in-car entertainment systems empowers vehicular users to download multimedia-enabled contents in transit and creates a new business opportunity for content providers (CPs). However, the timely delivery of requested content is a major concern for CPs to improve the quality of service (QoS) of their subscribe users. Roadside unit (RSU)-based caching appears as a promising solution for CPs wherein CPs proactively store their content at the RSU to reduce the content delivery time. Since the RSUs are enabled with limited storage capacity, the competition among multiple CPs for storage space is unavoidable. Further, the CPs are connected with RSUs using capacity limited backhaul links. Hence the allocation of RSUs’ storage among CPs becomes a fundamental issue in RSU-based caching networks. In this paper, we design a market scenario in which the set of CPs competes for the storage space of RSUs. In the unavailability of utility and cost functions of the CPs and the RSUs, we introduce a market maker to manage the marketplace. Further, we employ iteration-based double-sided auction mechanism to compute the optimal storage allocation and corresponding payment transfer for CPs which maximizes the social welfare of the networks. The simulation results demonstrate the proposed auction mechanism improves the social welfare of the network by at least 29.3% compared to the benchmark schemes. Further, with the help of both analytical and numerical analysis, we show that the proposed auction mechanism also holds vital economical properties.
Parametric Extraction and Comparison of Different Voltage-Mode based Sense Amplifier Topologies

Sense amplifiers plays vital role in sensing the stored data in the memory core. Sense amplifier also helps in reducing overall sensing voltage and delay and effects theoverall power consumption for a memory block. So, it is important to study various sense amplifiers for better performance. This paper focuses on design and performance comparison of various sense amplifier designs. The sense amplifier is analyzed for energy, current, delay and power consumption. The paper also reports the comparative analysis between the various cross coupled voltage mode sense amplifiers and transmission gate voltage mode sense amplifier. The result shows the performance of transmission gate sense amplifier is better in case of energy power and current parameters. All the simulations done using LT spice at 180nm CMOS technology.
Big data Analysis of Regional Meteorological Observation Based : on Hierarchical Density Clustering Algorithm HDBSCAN

In this paper, the HDBSCAN(Hierarchical Density-Based Spatial Clustering of Applications with Noise) clustering technique is applied to the processing of meteorological data, and the data is standardized by using the observation data from the regional meteorological observation stations of Shenzhen government as the analyzed data set. Factors such as temperature and precipitation are selected as analysis indexes, the indexes are dimensioned down by PCA (Principal Component Analysis), and the HDBSCAN clustering algorithm is used to classify the data into climate. Based on the obtained classification results, specific analyses are performed for some categories, and relevant suggestions are made for the relevant neighborhoods.
SimGrade: Using Code Similarity Measures for More Accurate Human Grading

While the use of programming problems on exams is a common form of summative assessment in CS courses, grading such exam problems can be a difficult and inconsistent process. Through an analysis of historical grading patterns we show that inaccurate and inconsistent grading of free-response programming problems is widespread in CS1 courses. These inconsistencies necessitate the development of methods to ensure more fairer and more accurate grading. In subsequent analysis of this historical exam data we demonstrate that graders are able to more accurately assign a score to a student submission when they have previously seen another submission similar to it. As a result, we hypothesize that we can improve exam grading accuracy by ensuring that each submission that a grader sees is similar to at least one submission they have previously seen. We propose several algorithms for (1) assigning student submissions to graders, and (2) ordering submissions to maximize the probability that a grader has previously seen a similar solution, leveraging distributed representations of student code in order to measure similarity between submissions. Finally, we demonstrate in simulation that these algorithms achieve higher grading accuracy than the current standard random assignment process used for grading.
Recommending Knowledge Concepts on MOOC Platforms with Meta-path-based Representation Learning

Massive Open Online Courses (MOOCs) which enable large-scale open online learning for massive users have been play-ing an important role in modern education for both students as well as professionals. To keep users’ interest in MOOCs, recommender systems have been studied and deployed to recommend courses or videos that a user might be interested in. However, recommending courses and videos which usually cover a wide range of knowledge concepts does not consider user interests or learning needs regarding some speciﬁc concepts. This paper focuses on the task of recommending knowledge concepts of interest to users, which is challenging due to the sparsity of user-concept interactions given a large number of concepts. In this paper, we propose an approach by modeling information on MOOC platforms (e.g., teacher, video, course, and school) as a Heterogeneous Information Network (HIN) to learn user and concept representations using Graph Convolutional Networks based on user-user and concept-concept relationships via meta-paths in the HIN. We incorporate those learned user and concept representations into an extended matrix factorization framework to predict the preference of concepts for each user. Our experiments on a real-world MOOC dataset show that the proposed approach outperforms several baselines and state-of-the-art methods for predicting and recommending concepts of interest to users.
Experimental Evaluation of Similarity Measures for Educational Items

Measuring similarity of educational items has several applications in the development of adaptive learning systems, and previous research has already proposed a wide range of similarity measures. In this work, we provide an experimental evaluation of selected similarity measures using a large dataset. The used items are alternate-choice questions for the practice of English grammar for second language learners; the dataset contains thousands of items and over 10 million student answers. Our results provide warnings about the generalizability of results presented in EDM works: 1) the results vary signiﬁcantly between knowledge components and 2) the size of available data is an important factor.
A Gray Box Model for Characterizing Driver Behavior

Understanding the behavior of human drivers is critical for the successful deployment of autonomous vehicles. Driver modeling is challenging due to the uncertainty inherent in human behavior and the complex interactions among drivers. Recent efforts have focused on black-box models that are highly expressive but lack interpretability of underlying dynamics. White-box models, on the other hand, are more interpretable as they are typically defined by relatively simple rules. They can prevent undesirable outcomes but cannot model the variability of human behavior. This paper presents a gray-box driver model that combines rule-based models with data-driven learning. The parameters of rule-based driver models are learned from real-world data using expectation-maximization. We perform experiments on interactive driving scenarios with lane changes and evaluate our model based on prediction accuracy, data efficiency, and safety. Results show that our model can accurately replicate human driver behavior with less data.
Early Prediction of Conceptual Understanding in Interactive Simulations

Interactive simulations allow students to independently explore scientiﬁc phenomena and ideally infer the underlying principles through their exploration. Eﬀectively using such environments is challenging for many students and therefore, adaptive guidance has the potential to improve student learning. Providing eﬀective support is, however, also a challenge because it is not clear how eﬀective inquiry in such environments looks like. Previous research in this area has mostly focused on grouping students with similar strategies or identifying learning strategies through sequence mining. In this paper, we investigate features and models for an early prediction of conceptual understanding based on clickstream data of students using an interactive Physics simulation. To this end, we measure students’ conceptual understanding through a task they need to solve through their exploration. Then, we propose a novel pipeline to transform clickstream data into predictive features, using latent feature representations and interaction frequency vectors for diﬀerent components of the environment. Our results on interaction data from 192 undergraduate students show that the proposed approach is able to detect struggling students early on.
Cyber Security Threats And Their Solutions Through Deep Learning: A Bibliometric Analysis

Nowadays a major concern in the field of information technology is Cyber security. Although in the recent times there have been booming advancements in the field of technology, but it has also brought along various risks including malware attacks, cloud abuse hacking, insecure API, cyber scams, data breach and so on. Because cyber-attacks are convenient, cheap and involves less risks, these are the reasons that they go on multiplying [32]. A cybercriminal only needs a few expenses in addition to a computer and an active internet connection. There are no other constraints such as geography and distance[1]. Therefore, to tackle the increasing menace of cybercrimes, researchers have started their work on cyber security making it the most promising field of study. Therefore, in today’s world of business, cybersecurity has become an urgent need. Understanding this need, deep learning methods are being used by cybersecurity experts for dealing with various problems foisted by the cyber criminals[2]. This paper reviews various threats in the path of cyber security and it also brings to light various deep learning methods that have been employed to combat with these threats.