0 Machine Learning| Final Year B.E/B.Tech, M.Tech, MCA Mechanical Projects in Bangalore

2021/2022 List of Final Year BE/B.Tech/M.tech/MCA Machine Learning Projects

Problem Statement: 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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Problem Statement: 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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Problem Statement: 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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Problem Statement: 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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Problem Statement: 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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Problem Statement: 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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Problem Statement: 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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Final Year Projects for the Academic Year 2020-2021. For Project Synopsis Click here or Call 9844628808