Project Code: RML001
Abstract: Agricultural intelligent decision system has a positive practical significance for guiding agricultural production, which can provide scientific basis for agriculture. Big data analysis technology can effectively improve the performance of intelligent decision system. The research development of the agricultural intelligent decision system is given. The classification of the agricultural decision system is introduced. The frame designation of the intelligent decision system is studied, and the design process is given.

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Project Code: RML002
Abstract: The security of outsourced data has been a key issue in cloud applications. Previous researches have focused on the remote data auditing and verification schemes. Although some of them have the properties of dynamic update and public audit, most of these schemes implicitly assume an honest client. Actually, the client has the motivations to corrupt the verification results against server, and the server may suspect the verification results under the situation where the client is dishonest. Up to now, few solutions have been proposed when both the client and server are not fully trusted and either side could be malicious. In this paper, a strict verifiable database (sVDB) protocol is proposed to tackle this problem. The concrete construction based on the bilinear pair technique can support client’s real-time query and batch update operations. We prove the security of the scheme by contradiction under the square computational DiffieHellman assumption. We also implement the protocol and carry out several experiments to compare its performance with the state-of-the-art VDB scheme using vector commitment. All the results show that the proposed sVDB scheme achieves good security performance and high computation efficiency.

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Project Code: RML003
Abstract: Study includes basic information about machine learning and recommender systems with their examples. More broadly addressed was the topic of machine learning’s algorithms, which are used in such systems. The paper mainly focused on filtering algorithms based on the neighborhood of users or objects, and based on content. The description of these algorithms includes: similarities, disadvantages and advantages, measures for evaluating the algorithm, and calculation of the sample value of the evaluation prediction. The design part of the work begins with the description of the used databases from the MovieLens portal. Afterwards, the technology and practical implementation of the algorithms described above are then presented. The next part contains an analysis of the results and conclusions based on the simulations carried out on the computer to assess how the algorithms work. At the end of the work, there is a summary, performance evaluation of recommendation systems, and lessons learned from the project, as well as a proposal for further work on the issue of such systems.

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Project Code: RML004
Abstract: Frequent itemsets mining with differential privacy refers to the problem of mining all frequent itemsets whose supports are above a given threshold in a given transactional dataset, with the constraint that the mined results should not break the privacy of any single transaction. Current solutions for this problem cannot well balance efficiency, privacy and data utility over large scaled data. Toward this end, we propose an efficient, differential private frequent itemsets mining algorithm over large scale data. Based on the ideas of sampling and transaction truncation using length constraints, our algorithm reduces the computation intensity, reduces mining sensitivity, and thus improves data utility given a fixed privacy budget. Experimental results show that our algorithm achieves better performance than prior approaches on multiple datasets

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Project Code: RML006
Abstract: Prior to buying a product, people usually inform themselves by reading online reviews. To make more profit sellers often try to fake user experience. As customers are being deceived this way, recognizing and removing fake reviews is of great importance. This paper analyzes spam detection methods, based on machine learning, and presents their overview and results.

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Project Code: RML007
Abstract: Prediction system is very essential to curb the curiosity of anything. Many sports prediction systems are in great demand and data analysis plays a great role in prediction. Previous efforts in sports data analysis have resulted in prediction of sports such as football, prediction of next shot location in tennis, performance of athletes in Olympics, slam dunk shots frequency in Basket Ball and many more. Cricket prediction is comparatively difficult as there are many factors that can influence the result or outcome of the cricket match. Earlier basic prediction systems for cricket match consider only the venue and disregard the factors like weather, stadium size, captaincy etc. The factors like venue of the match, pitch, weather conditions first batting or fielding all play a vital role in predicting the winner of the match. Suitable models are necessary to predict and data mining makes it possible to extract required information from the data files. This paper presents the usage of Google Prediction API to analyse the data of previous cricket matches and predict outcome of a given cricket match.

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Project Code: RML008
Abstract: —Collaborative filtering (CF) algorithms have been widely used to build recommender systems since they have distinguishing capability of sharing collective wisdoms and experiences. However, they may easily fall into the trap of the Matthew effect, which tends to recommend popular items and hence less popular items become increasingly less popular. Under this circumstance, most of the items in the recommendation list are already familiar to users and therefore the performance would seriously degenerate in finding cold items, i.e., new items and niche items. To address this issue, in this paper, a user survey is first conducted on the online shopping habits in China, based on which a novel recommendation algorithm termed innovatorbased CF is proposed that can recommend cold items to users by introducing the concept of innovators. Specifically, innovators are a special subset of users who can discover cold items without the help of recommender system. Therefore, cold items can be captured in the recommendation list via innovators, achieving the balance between serendipity and accuracy. To confirm the effectiveness of our algorithm, extensive experiments are conducted on the dataset provided by Alibaba Group in Ali Mobile Recommendation Algorithm Competition, which is collected from the real e-commerce environment and covers massive user behavior log data

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Project Code: RML009
Abstract: The successful use of online material in political campaigns over the past two decades has motivated the inclusion of social media platforms—such as Twitter—as an integral part of the political apparatus. Political analysts are increasingly turning to Twitter as an indicator of public opinion. We are interested in learning how positive and negative opinions propagate through Twitter and how important events influence public opinion. In this paper, we present a neural network-based approach to analyse the sentiment expressed on political tweets. First, our approach represents the text by dense vectors comprising subword information to better detect word similarities by exploiting both morphology and semantics. Then, a Convolutional Neural Network is trained to learn how to classify tweets depending on sentiment, based on an available labelled dataset. Finally, the model is applied to perform the sentiment analysis of a collection of tweets retrieved during the days prior to the latest UK General Election. Results are promising and show that the neural network approach represents an improvement over lexicon-based approaches for positive/negative sentence classification.

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Project Code: RML010
Abstract: The ubiquity of smartphones has led to the emergence of mobile crowdsourcing tasks such as the detection of spatial events when smartphone users move around in their daily lives. However, the credibility of those detected events can be negatively impacted by unreliable participants with low-quality data. Consequently, a major challenge in quality control is to discover true events from diverse and noisy participants’ reports. This truth discovery problem is uniquely distinct from its online counterpart in that it involves uncertainties in both participants’ mobility and reliability. Decoupling these two types of uncertainties through location tracking will raise severe privacy and energy issues, whereas simply ignoring missing reports or treating them as negative reports will significantly degrade the accuracy of the discovered truth. In this paper, we propose a new method to tackle this truth discovery problem through principled probabilistic modeling. In particular, we integrate the modeling of location popularity, location visit indicators, truth of events and three-way participant reliability in a unified framework. The proposed model is thus capable of efficiently handling various types of uncertainties and automatically discovering truth without any supervision or the need of location tracking. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art truth discovery approaches in the mobile crowdsourcing environment

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Project Code: ML33
Abstract: Since coronavirus has shown up, inaccessibility of legitimate clinical resources is at its peak, like the shortage of specialists and healthcare workers, lack of proper equipment and medicines etc. The entire medical fraternity is in distress, which results in numerous individuals demise. Due to unavailability, individuals started taking medication independently without appropriate consultation, making the health condition worse than usual. As of late, machine learning has been valuable in numerous applications, and there is an increase in innovative work for automation. This paper intends to present a drug recommender system that can drastically reduce specialists heap. In this research, we build a medicine recommendation system that uses patient reviews to predict the sentiment using various vectorization processes like Bow, TF-IDF, Word2Vec, and Manual Feature Analysis, which can help recommend the top drug for a given disease by different classification algorithms. The predicted sentiments were evaluated by precision, recall, f1score, accuracy, and AUC score. The results show that classifier LinearSVC using TF-IDF vectorization outperforms all other models with 93% accuracy.

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Project Code: ML32
Abstract: Social media (and the world at large) have been awash with news of the COVID-19 pandemic. With the passage of time, news and awareness about COVID-19 spread like the pandemic itself, with an explosion of messages, updates, videos, and posts. Mass hysteria manifest as another concern in addition to the health risk that COVID-19 presented. Predictably, public panic soon followed, mostly due to misconceptions, a lack of information, or sometimes outright misinformation about COVID-19 and its impacts. It is thus timely and important to conduct an ex post facto assessment of the early information flows during the pandemic on social media, as well as a case study of evolving public opinion on social media which is of general interest. This study aims to inform policy that can be applied to social media platforms; for example, determining what degree of moderation is necessary to curtail misinformation on social media. This study also analyzes views concerning COVID-19 by focusing on people who interact and share social media on Twitter. As a platform for our experiments, we present a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020. The tweets have been labeled into positive, negative, and neutral sentiment classes. We analyzed the collected tweets for sentiment classification using different sets of features and classifiers. Negative opinion played an important role in conditioning public sentiment, for instance, we observed that people favored lockdown earlier in the pandemic; however, as expected, sentiment shifted by mid-March. Our study supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic.

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Project Code: ML31
Abstract: Chronic Kidney Disease (CKD) is one of worldwide medical challenges with high morbidity and death rate. Since there is no symptom during the early stages of CKD, patients often fail to diagnose the disease. Patients with HIV have more chances to be affected with CKD in critical conditions. Early detection of CKD helps patients to obtain prompt care ald delays the further progression of the disease. With the availability of pathology data, the use of machine-learning techniques in healthcare for the classification and prediction of disease has become more common. This paper presents the classification of CKD using machine learning models. Based on the glomerular filtration rate, the CKD stages are also calculated for patients diagnosed with CKD. DNN model outperforms with 99% of accuracy in classifying CKD patients with HIV.

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Project Code: ML30
Abstract: Recognition of a person on the basis of his/her ear features is an upcoming area of research in the biometric field. In compression to other biometric traits, datasets with a large number of images are not available and as such performance gains due deep learning methodology is still unexplored. In this paper aggressive data augmentation is carried out to artificially increase the number of samples in the AMI ear database which has only seven images per class. The augmented dataset is subjected to feature extraction by deep learning and classification by a classical model to obtain a hybrid deep learning classical model. The deep learning architecture inceptionV3 is used as feature extraction mechanism in which input to avgpool layer is taken as feature vectors and the dimensions of the feature vectors are reduced using principal component analysis. The dimensionality reduced features are classified using linear and quadratic classifier and best accuracy rate of 98.1% is obtained on quadratic SVM classifier.

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Project Code: ML29
Abstract: Malaria is the deadliest disease in the earth and big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under the microscope by lab or qualified technicians. This process is inefficient and the diagnosis depends on the experience and well knowledgeable person needed for the examination. Deep Learning algorithms have been applied to malaria blood smears for diagnosis before. However, practical performance has not been sufficient so far. This paper proposes a new and highly robust machine learning model based on a convolutional neural network (CNN) which automatically classifies and predicts infected cells in thin blood smears on standard microscope slides. A ten-fold cross-validation layer of the convolutional neural network on 27,558 single-cell images is used to understand the parameter of the cell. Three types of CNN models are compared based on their accuracy and select the precise accurate - Basic CNN, VGG-19 Frozen CNN, and VGG-19 Fine Tuned CNN. Then by comparing the accuracy of the three models, the model with a higher rate of accuracy is acquired.

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Project Code: ML28
Abstract: A significant attention has been made to the accurate detection of diabetes which is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the IoT e-healthcare environment. Internet of Things (IOT) has emerging role in healthcare services which delivers a system to analyze the medical data for diagnosis of diseases applied data mining methods. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a IOT based diagnosis system using machine learning methods, such as preprocessing of data, feature selection, and classification for the detection of diabetes disease in e- healthcare environment. Model validation and performance evaluation metrics have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning Decision Tree algorithms, such as Ada Boost and Random Forest are also used for feature selection and compared the classifier performance with wrapper based feature selection algorithms also. Machine learning classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the Decision Tree algorithm based on selected features improves the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high as compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and GL, DPF, and BMI are more significantly important features in the dataset for prediction of diabetes disease. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would be effectively detected diabetes disease and can easily be deployed in IOT wireless sensor technologies based e-healthcare environment.

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Project Code: ML27
Abstract: Social media is a platform where many young people are getting bullied. As social networking sites are increasing, cyberbullying is increasing day by day. To identify word similarities in the tweets made by bullies and make use of machine learning and can develop an ML model automatically detects social media bullying actions. However, many social media bullying detection techniques have been implemented, but many of them were textual-based. The goal of this paper is to show the implementation of software that will detect bullied tweets, posts, etc. A machine learning model is proposed to detect and prevent bullying on Twitter. Two classifiers i.e. SVM and Naïve Bayes are used for training and testing the social media bullying content. Both Naive Bayes and SVM (Support Vector Machine) were able to detect the true positives with 71.25% and 52.70% accuracy respectively. But SVM outperforms Naive Bayes of similar work on the same dataset. Also, Twitter API is used to fetch tweets, and tweets are passed to the model to detect whether the tweets are bullying or not.

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Project Code: ML26
Abstract: Object detection, which aims to automatically mark the coordinates of objects of interest in pictures or videos, is an extension of image classification. In recent years, it has been widely used in intelligent traffic management, intelligent monitoring systems, military object detection, and surgical instrument positioning in medical navigation surgery, etc. COVID-19, a novel coronavirus outbreak at the end of 2019, poses a serious threat to public health. Many countries require everyone to wear a mask in public to prevent the spread of coronavirus. To effectively prevent the spread of the coronavirus, we present an object detection method based on single-shot detector (SSD), which focuses on accurate and real-time face masks detection in the supermarket. We make contributions in the following three aspects: 1) presenting a lightweight backbone network for feature extraction, which based on SSD and spatial separable convolution, aiming to improve the detection speed and meet the requirements of real-time detection; 2) proposing a Feature Enhancement Module (FEM) to strengthen the deep features learned from CNN models, aiming to enhance the feature representation of the small objects; 3) constructing COVID-19Mask, a large-scale dataset to detect whether shoppers are wearing masks, by collecting images in two supermarkets. The experiment results illustrate the high detection precision and real-time performance of the proposed algorithm.

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Project Code: ML25
Abstract: Advances in Information and Communications Technology (ICT) have increased the growth of Massive open online courses (MOOCs) applied in distance learning environments. Various tools have been utilized to deliver interactive content including pictures, figures, and videos that can motivate the learners to build new cognitive skills. High-ranking universities have adopted MOOCs as an efficient dashboard platform where learners from around the world can participate in such courses. The students learning progress is evaluated by using set computer-marked assessments. In particular, the computer gives immediate feedback to the student once he or she completes the online assessments. The researchers claim that student success rate in an online course can be related to their performance at the previous session in addition to the level of engagement. Insufficient attention has been paid by literature to evaluate whether student performance and engagement in the prior assessments could affect student achievement in the next assessments. In this paper, two predictive models have been designed namely students assessments grades and final students performance. The models can be used to detect the factors that influence students learning achievement in MOOCs. The result shows that both models gain feasible and accurate results. The lowest RSME gain by RF acquire a value of 8.131 for students assessments grades model while GBM yields the highest accuracy in final students performance, an average value of 0.086 was achieved.

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Project Code: ML24
Abstract: Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profits or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, the spam review detection problem has gained much attention from communities and researchers, but still, there is a need to perform experiments on real-world large-scale review datasets. This can help to analyze the impact of widespread opinion spam in online reviews. In this work, two different spam review detection methods have been proposed: (1) Spam Review Detection using Behavioral Method (SRD-BM) utilizes thirteen different spammers behavioral features to calculate the review spam score which is then used to identify spammers and spam reviews, and (2) Spam Review Detection using Linguistic Method (SRD-LM) works on the content of the reviews and utilizes transformation, feature selection and classification to identify the spam reviews. Experimental evaluations are conducted on a real-world Amazon review dataset which analyze 26.7 million reviews and 15.4 million reviewers. The evaluations show that both proposed models have significantly improved the detection process of spam reviews. Specifically, SRD-BM achieved 93.1% accuracy whereas SRD-LM achieved 88.5% accuracy in spam review detection. Comparatively, SRD-BM achieved better accuracy because it works on utilizing rich set of spammers behavioral features of review dataset which provides in-depth analysis of spammer behaviour. Moreover, both proposed models outperformed existing approaches when compared in terms of accurate identification of spam reviews. To the best of our knowledge, this is the first study of its kind which uses large-scale review dataset to analyze different spammers behavioral features and linguistic method utilizing different available classifiers.

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Project Code: ML23
Abstract: Rice is one of the major cultivated crops in India which is affected by various diseases at various stages of its cultivation. It is very difficult for the farmers to manually identify these diseases accurately with their limited knowledge. Recent developments in Deep Learning show that Automatic Image Recognition systems using Convolutional Neural Network (CNN) models can be very beneficial in such problems. Since rice leaf disease image dataset is not easily available, we have created our own dataset which is small in size hence we have used Transfer Learning to develop our deep learning model. The proposed CNN architecture is based on VGG-16 and is trained and tested on the dataset collected from rice fields and the internet. The accuracy of the proposed model is 92.46%

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Project Code: ML22
Abstract: Breast cancer is a type of tumor that occurs in the tissues of the breast. It is the most common type of cancer found in women around the world and it is among the leading causes of deaths in women. This article presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used. Our main focus is to comparatively analyze different existing Machine Learning and Data Mining techniques in order to find out the most appropriate method that will support the large dataset with good accuracy of prediction. The main purpose of this review is to highlight all the previous studies of machine learning algorithms that are being used for breast cancer prediction and this article provides the all necessary information to the beginners who want to analyze the machine learning algorithms to gain the base of deep learning.

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Project Code: ML21
Abstract: The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. Four stock market groups, namely diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange, are chosen for experimental evaluations. This study compares nine machine learning models (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Naïve Bayes, K-Nearest Neighbors (KNN), Logistic Regression and Artificial Neural Network (ANN)) and two powerful deep learning methods (Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators from ten years of historical data are our input values, and two ways are supposed for employing them. Firstly, calculating the indicators by stock trading values as continuous data, and secondly converting indicators to binary data before using. Each prediction model is evaluated by three metrics based on the input ways. The evaluation results indicate that for the continuous data, RNN and LSTM outperform other prediction models with a considerable difference. Also, results show that in the binary data evaluation, those deep learning methods are the best; however, the difference becomes less because of the noticeable improvement of models performance in the second way.

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Project Code: ML20
Abstract: It investigates opinion mining by means of supervised learning techniques to search out the emotion of the student input bolstered characterized choices of instructing and learning. The examination led includes the apparatus of a blend of AI and common language preparing systems on understudy input data accumulated from module investigation overview consequences of VR Siddhartha Engineering College, Vijayawada. Additionally, to offer a grade by grade clarification of the technique of accomplishment of opinion mining on or after scholar remarks using the open source tool Python, the work additionally offers a comparative overall performance take a look remarks supported, extracted alternatives like examination, teaching and so on. The consequences are as compared to be trying to find out higher overall performance with relevance several evaluation standards designed for the various techniques.

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Project Code: ML18
Abstract: Suicide is a disturbing general medical issue and increasing fatal every year around the world. This work naturally removed casual inactive subjects from online web-based life twitter and communicating self-destructive ideations. Right off the bat emotionally assessed the idle points and afterward comprehensively contrasted them with chance variables proposed by space specialists. As long-range interpersonal communication destinations have gotten progressively normal, clients have embraced these locales to discuss strongly close to home points, among them their considerations about suicide. The tweets are significant for investigation since information shows up at a high recurrence and calculations that procedure them must do as such under extremely severe imperatives of capacity and time. Right now, we can separate Emoticons and Synonyms Feature and utilized n- gram model which is a mix of Unigram, Bigram, and Trigram with half breed word reference for score computation. This model utilizing the casual points to anticipate the earnestness of the posts using machine learning algorithms. In this research, we also compare different approaches like SVM, NB, and RF.

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Project Code: ML19
Abstract: With the evolution in wireless communication, there are many security threats over the internet. The intrusion detection system [IDS] helps to find the attacks on the system and the intruders are detected. Previously various machine learning [ML] techniques are applied on the IDS and tried to improve the results on the detection of intruders and to increase the accuracy of the IDS. This paper has proposed an approach to develop efficient IDS by using the principal component analysis [PCA] and the random forest classification algorithm. Where the PCA will help to organise the dataset by reducing the dimensionality of the dataset and the random forest will help in classification. Results obtained states that the proposed approach works more efficiently in terms of accuracy as compared to other techniques like SVM, Naive Bayes, and Decision Tree. The results obtained by proposed method are having the values for performance time [min] is 3.24 minutes, Accuracy rate [%] is 96.78 %, and the Error rate [%] is 0.21 %.

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Project Code: ML17
Abstract: Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm [FCMIM] is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system [FCMIM-SVM] achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.

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Project Code: ML16
Abstract: Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance-broadcast [ADS-B] messages are received, pre-processed, and integrated with other information such as weather condition, flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory [LSTM] is capable of handling the obtained aviation sequence data, but overfitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest-based model can obtain higher prediction accuracy [90.2% for the binary classification] and can overcome the overfitting problem.

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Project Code: ML15
Abstract: As we know that people around the globe work hard to keep up with this racing world. However, due to this, each individual is dealing with different health issues, one of the most known issue is depression or stress which may eventually lead to death or other brutal activities. These abnormalities can be termed as the Bipolar disorder which can be treated by undergoing some treatment suggested by specialists. For this research, data has been collected from working people which comprises of all kinds of questions for despondent detection and the dataset has been run through some machine learning algorithms. The Random Forest algorithm gives the highest accuracy as 87.02% compared to the other algorithms.

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Project Code: ML14
Abstract: In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and is widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. This paper addresses the challenges introduced by the unknown characteristics of fake news and diverse connections among news articles, creators, and subjects. This paper introduces a novel gated graph neural network, namely FAKEDETECTOR. Based on a set of explicit and latent features extracted from the textual information, FAKEDETECTOR builds a deep diffusive network model to learn the representations of news articles, creators, and subjects simultaneously. Extensive experiments have been done on a real-world fake news dataset to compare FAKEDETECTOR with several state-of-the-art models.

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Project Code: ML13
Abstract: Malicious social bots generate fake tweets and automate their social relationships either by pretending like a follower or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweet in order to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features [such as URL redirection, frequency of shared URLs, and spam content in URL] consumes less amount of time in comparison with social graph-based features [which rely on the social interactions of users]. Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, a learning automata-based malicious social bot detection [LA-MSBD] algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants [users] in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes theorem, and the indirect trust is derived from the Dempster-Shafer theory [DST] to determine the trustworthiness of each participant accurately. Experimentation has been performed on two Twitter data sets, and the results illustrate that the proposed algorithm achieves improvement in precision, recall, F-measure, and accuracy compared with existing approaches for MSBD.

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Project Code: ML12
Abstract: Online Social Network [OSN] is a network hub where people with similar interests or real world relationships interact. As the popularity of OSN is increasing, the security and privacy issues related to it are also rising. Fake and Clone profiles are creating dangerous security problems to social network users. Cloning of user profiles is one serious threat, where already existing users details are stolen to create duplicate profiles and then it is misused for damaging the identity of original profile owner. They can even launch threats like phishing, stalking, spamming etc. Fake profile is the creation of profile in the name of a person or a company which does not really exist in social media, to carry out malicious activities. In this paper, a detection method has been proposed which can detect Fake and Clone profiles in Twitter. Fake profiles are detected based on set of rules that can effectively classify fake and genuine profiles. For Profile Cloning detection two methods are used. One using Similarity Measures and the other using C4.5 decision tree algorithm. In Similarity Measures, two types of similarities are considered - Similarity of Attributes and Similarity of Network relationships. C4.5 detects clones by building decision tree by taking information gain into consideration. A comparison is made to check how well these two methods help in detecting clone profiles

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Project Code: ML11
Abstract: Electronic mail has eased communication methods for many organisations as well as individuals. This method is exploited for fraudulent gain by spammers through sending unsolicited emails. This article aims to present a method for detection of spam emails with machine learning algorithms that are optimized with bio-inspired methods. A literature review is carried to explore the efficient methods applied on different datasets to achieve good results. An extensive research was done to implement machine learning models using Naïve Bayes, Support Vector Machine, Random Forest, Decision Tree and Multi-Layer Perceptron on seven different email datasets, along with feature extraction and pre-processing. The bio-inspired algorithms like Particle Swarm Optimization and Genetic Algorithm were implemented to optimize the performance of classifiers. Multinomial Naïve Bayes with Genetic Algorithm performed the best overall. The comparison of our results with other machine learning and bio-inspired models to show the best suitable model is also discussed.

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Project Code: ML10
Abstract: Online marketplaces often witness opinion spam in the form of reviews. People are often hired to target specific brands for promoting or impeding them by writing highly positive or negative reviews. This often is done collectively in groups. Although some previous studies attempted to identify and analyze such opinion spam groups, little has been explored to spot those groups who target a brand as a whole, instead of just products. In this article, we collected the reviews from the Amazon product review site and manually labeled a set of 923 candidate reviewer groups. The groups are extracted using frequent itemset mining over brand similarities such that users are clustered together if they have mutually reviewed [products of] a lot of brands. We hypothesize that the nature of the reviewer groups is dependent on eight features specific to a [group, brand] pair. We develop a feature-based supervised model to classify candidate groups as extremist entities. We run multiple classifiers for the task of classifying a group based on the reviews written by the users of that group to determine whether the group shows signs of extremity. A three-layer perceptron-based classifier turns out to be the best classifier. We further study behaviors of such groups in detail to understand the dynamics of brand-level opinion fraud better. These behaviors include consistency in ratings, review sentiment, verified purchase, review dates, and helpful votes received on reviews. Surprisingly, we observe that there are a lot of verified reviewers showing extreme sentiment, which, on further investigation, leads to ways to circumvent the existing mechanisms in place to prevent unofficial incentives on Amazon.

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Project Code: ML9
Abstract: Online social networks [OSNs] have become an integral mode of communication among people and even nonhuman scenarios can also be integrated into OSNs. The evergrowing rise in the popularity of OSNs can be attributed to the rapid growth of Internet technology. OSN becomes the easiest way to broadcast media [news/content] over the Internet. In the wake of emerging technologies, there is dire need to develop methodologies, which can minimize the spread of fake messages or rumors that can harm society in any manner. In this article, a model is proposed to investigate the propagation of such messages currently coined as fake news. The proposed model describes how misinformation gets disseminated among groups with the influence of different misinformation refuting measures. With the onset of the novel coronavirus-19 pandemic, dubbed COVID-19, the propagation of fake news related to the pandemic is higher than ever. In this article, we aim to develop a model that will be able to detect and eliminate fake news from OSNs and help ease some OSN users stress regarding the pandemic. A system of differential equations is used to formulate the model. Its stability and equilibrium are also thoroughly analyzed. The basic reproduction number [R0] is obtained which is a significant parameter for the analysis of message spreading in the OSNs. If the value of R0 is less than one [R0 <; 1], then fake message spreading in the online network will not be prominent, otherwise if R0 > 1 the rumor will persist in the OSN. Realworld trends of misinformation spreading in OSNs are discussed. In addition, the model discusses the controlling mechanism for untrusted message propagation. The proposed model has also been validated through extensive simulation and experimentation.

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Project Code: ML8
Abstract: Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community, the recognition and detection of traffic signs are a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced driver-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs [around 50 categories out of several hundred] and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network [CNN] approach, the mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 200 traffic-sign categories represented in our novel dataset. The results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with a large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of the traffic-sign inventory management.

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Project Code: ML7
Abstract: In recent years, the increasing prevalence of hate speech in social media has been considered as a serious problem worldwide. Many governments and organizations have made significant investment in hate speech detection techniques, which have also attracted the attention of the scientific community. Although plenty of literature focusing on this issue is available, it remains difficult to assess the performances of each proposed method, as each has its own advantages and disadvantages. A general way to improve the overall results of classification by fusing the various classifiers results is a meaningful attempt. We first focus on several famous machine learning methods for text classification such as Embeddings from Language Models [ELMo], Bidirectional Encoder Representation from Transformers [BERT] and Convolutional Neural Network [CNN], and apply these methods to the data sets of the SemEval 2019 Task 5. We then adopt some fusion strategies to combine the classifiers to improve the overall classification performance. The results show that the accuracy and F1-score of the classification are significantly improved.

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Project Code: ML6
Abstract: In India, we all know that Agriculture is the backbone of the country. This paper predicts the yield of almost all kinds of crops that are planted in India. This script makes novel by the usage of simple parameters like State, district, season, area and the user can predict the yield of the crop in which year he or she wants to. The paper uses advanced regression techniques like Kernel Ridge, Lasso and ENet algorithms to predict the yield and uses the concept of Stacking Regression for enhancing the algorithms to give a better prediction.

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Project Code: ML5
Abstract: Machine learning [ML] based forecasting mechanisms have proved their significance to anticipate in perioperative outcomes to improve the decision making on the future course of actions. The ML models have long been used in many application domains which needed the identification and prioritization of adverse factors for a threat. Several prediction methods are being popularly used to handle forecasting problems. This study demonstrates the capability of ML models to forecast the number of upcoming patients affected by COVID-19 which is presently considered as a potential threat to mankind. In particular, four standard forecasting models, such as linear regression [LR], least absolute shrinkage and selection operator [LASSO], support vector machine [SVM], and exponential smoothing [ES] have been used in this study to forecast the threatening factors of COVID-19. Three types of predictions are made by each of the models, such as the number of newly infected cases, the number of deaths, and the number of recoveries in the next 10 days. The results produced by the study proves it a promising mechanism to use these methods for the current scenario of the COVID-19 pandemic. The results prove that the ES performs best among all the used models followed by LR and LASSO which performs well in forecasting the new confirmed cases, death rate as well as recovery rate, while SVM performs poorly in all the prediction scenarios given the available dataset.

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Project Code: ML4
Abstract: Crime prediction is of great significance to the formulation of policing strategies and the implementation of crime prevention and control. Machine learning is the current mainstream prediction method. However, few studies have systematically compared different machine learning methods for crime prediction. This paper takes the historical data of public property crime from 2015 to 2018 from a section of a large coastal city in the southeast of China as research data to assess the predictive power between several machine learning algorithms. Results based on the historical crime data alone suggest that the LSTM model outperformed KNN, random forest, support vector machine, naive Bayes, and convolutional neural networks. In addition, the built environment data of points of interests [POIs] and urban road network density are input into LSTM model as covariates. It is found that the model with built environment covariates has better prediction effect compared with the original model that is based on historical crime data alone. Therefore, future crime prediction should take advantage of both historical crime data and covariates associated with criminological theories. Not all machine learning algorithms are equally effective in crime prediction.

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Project Code: ML3
Abstract: COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19s spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 [as of May 22 2020], it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence [AI]. Some Deep Learning [DL] methods have been illustrated to reach this goal, including Generative Adversarial Networks [GANs], Extreme Learning Machine [ELM], and Long/Short Term Memory [LSTM]. It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

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Project Code: ML2
Abstract: Digital data trails from disparate sources covering different aspects of student life are stored daily in most modern university campuses. However, it remains challenging to [i] combine these data to obtain a holistic view of a student, [ii] use these data to accurately predict academic performance, and [iii] use such predictions to promote positive student engagement with the university. To initially alleviate this problem, in this article, a model named Augmented Education [AugmentED] is proposed. In our study, [1] first, an experiment is conducted based on a real-world campus dataset of college students [N =156 ] that aggregates multisource behavioral data covering not only online and offline learning but also behaviors inside and outside of the classroom. Specifically, to gain in-depth insight into the features leading to excellent or poor performance, metrics measuring the linear and nonlinear behavioral changes [e.g., regularity and stability] of campus lifestyles are estimated; furthermore, features representing dynamic changes in temporal lifestyle patterns are extracted by the means of long short-term memory [LSTM]. [2] Second, machine learning-based classification algorithms are developed to predict academic performance. [3] Finally, visualized feedback enabling students [especially at-risk students] to potentially optimize their interactions with the university and achieve a study-life balance is designed. The experiments show that the AugmentED model can predict students academic performance with high accuracy.

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Project Code: ML1
Abstract: Chronic kidney disease [CKD] is a global health problem with high morbidity and mortality rate, and it induces other diseases. Since there are no obvious symptoms during the early stages of CKD, patients often fail to notice the disease. Early detection of CKD enables patients to receive timely treatment to ameliorate the progression of this disease. Machine learning models can effectively aid clinicians achieve this goal due to their fast and accurate recognition performance. In this study, we propose a machine learning methodology for diagnosing CKD. The CKD data set was obtained from the University of California Irvine [UCI] machine learning repository, which has a large number of missing values. KNN imputation was used to fill in the missing values, which selects several complete samples with the most similar measurements to process the missing data for each incomplete sample. Missing values are usually seen in real-life medical situations because patients may miss some measurements for various reasons. After effectively filling out the incomplete data set, six machine learning algorithms [logistic regression, random forest, support vector machine, k-nearest neighbor, naive Bayes classifier and feed forward neural network] were used to establish models. Among these machine learning models, random forest achieved the best performance with 99.75% diagnosis accuracy. By analyzing the misjudgments generated by the established models, we proposed an integrated model that combines logistic regression and random forest by using perceptron, which could achieve an average accuracy of 99.83% after ten times of simulation. Hence, we speculated that this methodology could be applicable to more complicated clinical data for disease diagnosis

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