Reviewerly/exHarmony huggingface dataset:
Identification of Fraudulent Credit Card transactions using Machine Learning Algorithms

Ever since the existence of e commerce payments systems came into being, people have found new ways to access someone’s credentials illegally. This is a major issue in current era as more and more transactions are being done online. Every year fraud cost generated in the economy is more than $4 trillion internationally. This is not surprising, as the return on investment for fraud detection and prevention is massive. Cybercrime specialists estimate that an investment of 1 million dollars into fraud or attack can net up to$100 million. Financial institutions such as commercial and investment banks operations are increasingly being targeted and require some method to handle and support the progress of credit card fraud detection. To avoid fraud and to secure transactions systems they require some advanced technology that support the use of artificial intelligence (AI) and machine learning (ML) approaches to stay one step ahead of criminals. Being a classification type problem, we propose a ML model incorporating XGBoost algorithm along with SMOTE analysis. Our aim is to minimize the number of fraudulent transactions being predicted as legitimate transactions as the gravity of these type of errors outweighs that of the other in a real world scenario.
Two-Phase Device Authentication Scheme for Request-Response Computing

Request-response computing is a model of communications in which a client requests for service and the server responds with response. It is a model that is most widely used in modern day computing and is more reliable and resource aware. Security is a prime concern in this model of computing, where malicious entities may gain access to crucial data and resources illegitimately. An efficient authentication mechanism may establish a secure communication channel between client and server and avoid illegitimate access of resources. Here, we propose a two-phase authentication mechanism using address binding for securing the communications between clients and server. The first phase of binding is done using cryptographically generated address (CGA) and for the second phase, we have designed a novel Mathematically Generated Address (MGA), in which IPv6 address are masked using mathematical operations and it is lightweight and secure. The proposed mechanism is secure and efficient and is verified in security analysis and performance evaluation as well. The proposed mechanism is evaluated for security and performance and its analysis has found that the proposed mechanism works efficiently in terms of security and time in comparison to the existing mechanism.
Hybrid Differential Evolution and Tabu Search for Parameter Tuning in Software Defect

Software Defect Prediction (SDP) helps in identifying defective modules as early as possible in the Software Development Life Cycle (SDLC). Machine learning (ML) methods are employed as defect predictors to provide insights into a software’s health. However, without fine-tuning the parameters of these defect predictors, the insights can be inaccurate. Hence it becomes crucial to tune defect predictors and optimize performance. The goal of this work is to discuss and compare some basic and easy strategies for parameter tuning. To work with Tabu Search (TS), Differential Evolution (DE) and Hybrid Differential Evolution and Tabu Search (HDETS) as optimizers, we used datasets from open-source JAVA systems. The parameter tunings were then applied to the test datasets to compare and contrast the outcomes of three optimization strategies. We discovered that tuning increases performance in a large number of cases. Since there was a considerable improvement in performance, after parameter tuning, we can say that when predicting defects in software, it is insufficient to just present the outcome without conducting a thorough tuning optimization analysis. Additionally, it was revealed that DE, TS and HDETS did not produce the same outcomes. As a result, for the vast number of datasets, it becomes essential to conduct tuning using a variety of optimization strategies to arrive at the best potential outcomes.
Personalized College Recommender and Cutoff Predictor for Direct Second Year Engineering

In Maharashtra there are around 342 polytechnics providing 110 engineering courses wherein throughout every year approximately 90,000 to 1.5 lakhs students pass out. In addition, there are only 338 engineering colleges that provide Direct Second Year Engineering (DSE) admissions for B.E and B.Tech studies. However, the engineering admission process is hectic and more than that is to find the college according to student preference i.e stream, location, university and seat type, etc. During the admission process the students always need to check the previous years cutoff of each college by visiting numerous websites & pdf lists which certainly consumes a lot of time. In our web application we will provide cutoff prediction of each college by the data-analysis of previous years cutoff, recommendation system for colleges listing according to student preferences, furthermore providing detailed comparison between institutions of their choice. The application is developed to bestow a personalized system so as to reduce time and ease the student’s college selection process.
Design Study of the Spoke Structure for Joint Torque Sensor

The joint Torque Sensor is one of the important parts of robotic arm. It helps in measuring the torque experienced in each joint in a robotic arm. The torque on the joints creates tension and compression on the mechanical structure of the sensor. This develops a strain on the sensor structure, and we can measure it using strain gauges. Using the measured strain, we can find the torque applied on the joint. The torque sensor is having an inner hub and outer rim, both are joined using four spokes. Based on the structure of the spoke the strain will vary. If we use a simple cantilever beam as the spoke, the structure may not give higher strain values. So, while designing the sensor, we must make sure that the spoke structure gives maximum and uniform strain over the area where we fix the strain gauge. This paper is discussing the design study of the spoke using Solidworks software.
A Hybrid Cooperative Guided Local Search for Solving the Traveling Salesman Problem

In this paper, a hybrid cooperative Guided Local Search (GLS) algorithm for solving the Travelling Salesman Problem (TSP) is proposed. The idea of the algorithm is to run a number of agents of GLS and after a number of local search calls, the agents exchange information about their search utilizing the crossover operator to make their further search rational. The partially mapped crossover (PMX) is applied based on the penalty’s values of features to preserve the good components of the best solutions found by the agents during the search. The preliminary experimental results demonstrate that the proposed algorithm is promising and outperforms parallel GLS without collaboration.
Trip Planner and Recommender using Flutter and Tensor Flow

The topic that has been considered for this project is trip recommender and planner. The principal goal of the project is to develop a mobile application that will give its users customized recommendations according to his or her taste and interests in travel. The main motivation for selecting this idea is that even though the information pertaining to tours or traveling is available all over the internet, where those data are more generic to every user who searches for it. These points of Interests just recommend top preferred places or hotels but does not consider users preference. The preference of specific tourists adheres to their numerous temporal and spatial limitations. One of the alternatives for these issues is to involve tour agencies, but they tend to recommend standard packages which would profit them the most and would not prioritize the user preferences. In recent times, personalization of generic components in life has become a trend, like customizing the way in which searches on google can be made, in the same way, personalizing touring options and its recommendation for a particular person is mostly based on their preferences and time budget constrain in realtime. This application can help people who find it tedious to plan a trip and also execute it. This can make their process of planning and executing their getaway easily. Therefore, this paper mainly focuses on individuals who are in search of personalized tourist routes and plans.
Cognitive Complexity Reduction through Control Flow Graph Generation

The cognitive complexity of a software determines the human comprehension effort to determine its underlying logic. The human comprehension effort preliminary depends on the person, who deals with the software, the source code and the problem to be addressed. The understandability of a given source code is varying with each user such that the cognitive complexity results with a subjective measurement. The graphical representation of the logical behavior of a source code implies the individual to comprehend the logic easily rather than referring to its original code base. This paper evaluates the possibility of using control flow graph representation to reduce the cognitive complexity of a given source code, thereby ensuring a less software complexity.
Customer Purchase Intention Prediction Using Text Analytical Models

Identifying the potential users matters a lot for the growth of any company. This can be estimated well in advance based on several factors like the user’s opinion regarding a product before its sale is announced. Considering this, an application will be developed which will be easy to use, accurate intention prediction Website.The website is built using HTML, CSS, Javascript and Django. A text analytical model is built using an ensemble model, made by collecting tweets from users. The model will find out potential buyers who have tweeted about the product but are yet to purchase. The model would estimate the most likelihood of a customer’s purchase. Various Parameters like Accuracy, Precision, Recall and F1 score are calculated for getting the best result. Presently there are not any notable applications available that use multiple algorithms and are used for purchase intention prediction with this sophisticated architecture, which makes the application unique.
Quantitative Analysis of Transfer Learning for Image Classification

This work is a quantitative analysis of various transfer learning methods and some incremental learning techniques using the task of image classification. We also analyze the assumptions under which transfer learning should be applied and how true these assumptions are in most of the real-world scenarios along with a brief description of the disadvantages such as decreased accuracy, biased models, etc. of using transfer learning in general and when applied at the wrong problem. An extensive study is carried out to explore thoroughly the relevant literature and the results obtained after the analysis are also presented in the tabulated form at the end of the paper. Last but not least we have explored the problems with transfer learning frameworks and ways in which we can overcome some of them.
DegChain: Development of Blockchain Framework for Generation and Verification of Educational Certificates

Educational degree certificate is the most important document that gets issued to the candidates after the completion of their educational program. The generation process involves multiple participants which carry their own set of conditions and constraints. The candidate needs to be validated and approved from different units of the educational system. There is a challenge of coordination among these units, which causes delays and inefficiencies. Blockchain has been observed as the technology to mitigate such challenges. This paper presents DegChain, a private permissioned blockchain based framework for the purpose of generating and verifying educational degree certificates. Degree certificates are generated based on approvals from the participating entities, which is then uploaded to the DegChain ledger and a private key is shared to the candidate for the purpose of verification. The paper concludes with comparative analysis of DegChain with other related works.
Smart Surveillance System Using Face Detection for Residentials

The Internet of Things is helping humans to automate life by providing different tools and making it more easier. It can also be viewed as things connected in a network. This network can provide several smart services. On the other hand, face recognition is something which provides us a way to make our system intelligent enough to recognize people, which makes it easier to find the potential suspects from a security point of view. Nowadays security cameras are used to build safety, where a person is responsible to detect any problems/ suspects and report the same to concerned authorities. This paper presents an idea to automate processes using IoT and face recognition methods.
Optimised Smart AI Surveillance in Quantum Computing Age

With the advancements in the digital world, crime investigations & video surveillance has also become smart using Artificial Intelligence and IoT like technologies. And the security of the sensitive information transferred between IoT devices only relies on the most robust available classical cryptographic methods. But the prime concern involves the easy breaking of the classical approaches of cryptography soon with the relentless advancement of technology. Thereby, the research mainly focuses on two areas. First, optimising intelligent video surveillance for crime investigations by implementing the ideology of the blockchain, and Deep Learning Neural Networks algorithms for reduced processing yet faster and accurate results. Second, implementation of Quantum Cryptography for better future-proofing and further research for future improvements. The discussion of results shows the optimal usability of deploying this idea and future scopes for improved data security.
Real-Time Sign Language Recognition using Deep Learning Techniques

Sign language uses hand gestures, shifts of hand shapes, changes in body postures, and even facial emotion to recognize the actions conveyed and trace that information meaningfully to express it conventionally. It is the prime communication medium for people with hearing disabilities and language impairment. We have developed an application that can detect the most popular greeting sentences in American Sign Language. Our application gives real-time predictions of these sentences and words with an average accuracy of 75% for every word. For Gesture identification, we used a dual-model system of Single Shot Multibox Detector (SSD) and Machine learning model working on 2D-Pose coordinates of the user at runtime. Our application also supports ASL Alphabet detection in real-time to generate custom phrases. This module leverages Google’s Inception v3 for transfer learning and gives an accuracy of 89.91%. All the Machine learning and Deep Learning models for our application were trained on a custom dataset created by us.
Handwriting Slant Determination Using Convolutional Neural Networks and Transfer Learning

Handwriting slant is one of the major characteristics utilized in forensic handwriting analysis. 1539 handwritten sample images were obtained from the online IAM Handwriting Database and labelled by a forensic expert for handwriting characteristics. Artificial Intelligence based Convolutional Neural Network classifiers were developed for classifying handwriting slant into three classes, viz. left, right and center. Transfer Learning approach from three pre-trained networks VGG16, ResNet50 and MobileNetV2 was attempted but it was found that Transfer Learning did not provide expected advantages, since presumably ImageNet database on which these networks were pre-trained does not have many images of handwritten samples. Therefore, robust and sufficient features could not be extracted using transfer learning approach. For handwriting images, it may be best to go for training from scratch until a pre-trained network which is trained on handwriting samples becomes available. A network trained from scratch using individual sentence images and L2-regularization gave the best performance. With 76% accuracy.
Skeleton Based Periodicity Analysis of Repetitive Actions

This paper investigates the problem of detecting and recognizing repetitive actions performed by a human. Repetitive action analysis play a major role in detecting many behavioral disorders. In this work, we present a robust framework for detecting and recognizing repetitive actions performed by a human subject based on periodic and aperiodic action analysis. Our framework uses focal joints in the human skeleton for the analysis of repetitive actions which are substantiated by the principles of human anatomy and physiology. Using Non-deterministic Finite Automata (NFA) techniques, in this paper, we introduce a novel model to transform repetitive action count to differentiate the periodicity in human action. Experimental results on a dataset consisting of 371 video clips show that our algorithm outperforms the state-of-art (RepNet) [1] in simultaneous multiple repetitive action counts. Further, while the proposed model and RepNet give comparable results in counting periodic repetitive actions, our model performance surpass RepNet significantly on analysing non-periodic repetitive behavior.
An Experimental Study on No-Reference Image Quality Assessment of Image Datasets

Image quality assessment algorithms are of various types depending upon the the method they are employing. Over the years, these algorithms have found various applications like image compression, image restoration, image transmission etc. Image quality assessment algorithms have played a significant in these fields by appropriately evaluating the quality of the images. Based on the structure of these algorithms, in this paper, we further explored the application of Image Quality Assessment algorithm especially Non-Reference Algorithm in the world of AI. In this paper, we have showed how the Non-reference IQA algorithms can be used to assess the quality of the images in the data that are being used in for training the deep learning models. In our study, we specially focused on the datasets that are being used in the training of the deep learning models that are used in the healthcare sector. We have showed how non-reference algorithms can be used to create a more quality data so that the efficiency of the automated systems which heavily relies on the data quality can be increased.
Automatic News Summarizer Using TextRank

Natural Language Processing and Deep Learning have made it possible for machines to understand the text. This is accomplished by comprehending the interrelationships between words and sentences. In this modern era, everyone wants to stay abreast of the latest news but the overwhelming amount of information makes it difficult for one to keep track of the status quo. Hence, we propose a fast summarizer system, with the help of which people can get a glimpse of source-based news summaries. In this system, we extract news from 250 sources and provide them to the user in the form of summaries, which aid in getting a quick overview of the news articles. News is provided in a categorized way where the user can select his favorite new sources. Our system not only effectively summarizes the news articles but also uses caching to ensure faster retrieval and a good user experience. We have chosen a production-efficient algorithm to get quick summaries.
Opinion Mining and Analysis using Deep Learning

Opinion Mining, though seems to be everyday term in the field of NLP, yet has a lot of milestones ahead, waiting to be achieved. In this paper, focus was to find better "Opinion Mining with analyzed sentiments" Model based on accuracy of different deep learning techniques after comparing accuracy levels of different deep learning models on which experiments were done. Recently Deep Learning Techniques have been adopted for this task, as it has been showing very promising results in terms of accurately deducing the emotion behind a text lying in internet database. In this paper, experiments were done with different techniques deep learning has provided us with, in order to create a better Opinion mining model.With the help of the experiments that were conducted, using the well-known IMDb data set, it was found that a combination of carefully curated model of selective layers can produce better results than just adding on CNN layers in hopes of getting results. The model is then tested on other datasets and is found to be better than other similar state-of-the-art models.
Implementation of Android-based Smart Speakers in a Smart Home for Dementia Patients

Dementia is a broad category of brain diseases that cause long-term and often gradual decrease in the ability to think and remember. It affects the day-to-day activities of a patient. The purpose of our work is to implement a cost-effective, IoT solution for patients suffering with dementia. Our work is a fog computing based implementation of the proposal by Boumpa et.al. [1]. The paper [1] mentions that having an edge/fog architecture could lead to additional requirements for custom-owned platform and design of new smart speakers. In our implementation work, we use android phones as smart speakers. This removes the need of production of new smart speakers and a custom-made platform. Our system is modular and scalable. The data is saved locally owing to privacy requirements. The system helps locate the patient in the house by using the signal strength of Bluetooth Low Energy devices. After triangulating, the speakers play a sound associated with the relative or caretaker, so that the patient can identify the relative. Subsequently, the system also informs the relative of the location of the patient in the house.
Usage of Parallelization Techniques for Video Summarisation: State-of-the-art, Open Issues, and Future Research Avenues

Videos are one of the prime and important sources of information. They can be availed in huge numbers. Whenever something is to be searched for in the video, the whole video clip has to be searched for which is a very time-consuming process. The search time could be reduced using video summarisation technique which is used to extract the relevant frames or the key-frames from the video in order to get the quick summary of the event. The relevant frames are extracted based on the particular object detection, face recognition, event detection, movements in the frames, etc. Supervised and unsupervised approaches are used to achieve the desired result. As the research got deeper in this area, new advanced and efficient technologies were invented. Deep learning feature extraction algorithms were found to be more efficient as compared to the conventional feature extraction algorithms. To make the video summarisation process faster, a parallel processing approach was also used by the researchers. This paper presents a review of different techniques used for video summarisation available in the literature. We have specifically presented a comparative study of video summarisation techniques implemented using parallel processing and multi-core CPUs. While the state-of-the-art of the field is presented through a comprehensive and systematic study, the paper also presents open issues, challenges, as well as open research avenues in the area. It is concluded that there is a particular scope of improvement in the processing time and use of parallelization in various stages of video summarisation. It is also concluded that the empirical study in the field should make use of a large size of video data set collected from multiple cameras rather than a small dataset collected using a single camera.