Reviewerly/exHarmony huggingface dataset:
Industry Recommendation for Undergraduate Internship using Decision Tree

The process of matching student profiles to industry profiles is critical to ensuring that students are placed in industries that are a good fit for their program. Therefore, to solve this problem, a system is presented that will give suggestions on suitable industrial types and internship placement from companies in the suggested industry for undergraduate students. This project maps student profiles from seven computer science programs and seven industrial types. There are 284 sample profiles collected from undergraduate students of Universiti Teknologi MARA. The profiles are gathered from previous records of placement for internship training. A decision tree model is constructed based on the sample profiles. The student’s Cumulative Grade Point Average (CGPA) and registered program are used as the main feature of industry recommendation. As a result, a web-based system for mapping students’ profiles to industries’ profiles has been developed. The application stores students’ and industries’ profiles and recommends suitable industries for each student’s profile.
The Evaluation of Cognitive Load Significance for Mobile Learning Application via User Interface Design Violations

COVID-19 has changed how the world operates, and education is one of the sectors that are highly affected by these changes. Previously, mobile learning is just an optional or a supplementary learning method. However, with the increased in the number of COVID-19 cases around the world, education system has switched from the traditional face-to-face mode in a classroom setting, to an online learning environment. Learning using a mobile device or mobile learning is a concept that is new to most learners, especially those who have never before experienced an online learning setting. One of the prevalent factors that leads to ineffective mobile learning process is badly designed user interfaces that will disengage learners from learning materials presented, and increase the cognitive load of the learners. Among the factors that results in bad user interface is the violation of a user interface guideline/framework. Therefore, the main objective of this research-work is to evaluate the learners’ cognitive load significance for mobile learning application by identifying Nielsen’s Heuristics’ violation. By implementing this study, important user interface design (UID) attributes that increase learner’s cognitive load can be identified. Understanding how UID can affect the learners’ cognitive load can assist designers in deciding which user interface designs that can improve or minimize learners’ cognitive load. The outcome of this research will enable mobile learning application designers, developers, educators, teachers and people who are interested in developing a mobile learning application to deliver an effective mobile learning experience to learners.
A Hybrid Prioritization Approach by integrating non-Functional and Functional User Stories in Agile-Scrum Software Development (i-USPA):A preliminary study

Due to its significance in the creation of software projects, the Agile-Scrum methodology has only lately become well-known in the field of software development. The Scrum technique, which is a process that gradually, iteratively, and continuously provides software based on time boxes, is another study that supports agile practitioners’ recent shift towards this method (sprints). It consists of user stories that are delivered during sprints by a Scrum team made up of team members, a Scrum Master, and a Product Owner. User stories are kept in product backlogs. The integrated needs prioritization methodologies have been created by a small number of scholars. However, the majority of the research is on non-agile software development. The prioritization of both functional and nonfunctional user stories simultaneously during Agile-Scrum Software Development (ASSD) is one of the main gaps that has been ignored by all of these methodologies, according to the literature review conducted by the researchers. The purpose of this study is to outline a research plan for creating an integrated user story prioritizing approach that combines non-functional and functional user stories of the ASSD (i-USPA). The preliminary findings demonstrate the critical importance of understanding the significance of non-functional user stories or requirements during the early stages of software development using the agile methodology, particularly in ASSD, in order to produce high-quality software while staying within budget and time constraints. The data was acquired from a small group of experts or software practitioners who use Agile, particularly the Scrum technique, in their organizations.
Automatic Keyword Extraction for Viewport Prediction of 360-degree Virtual Tourism Video

In 360-degree streaming videos, viewport prediction can reduce the bandwidth needed during the stream while still maintaining a quality experience for the users by streaming only the area that is visible to the user. Existing research in viewport prediction aims to predict the user’s viewport with data from the user’s head movement trajectory, video saliency, and subtitles of the video. While these subtitles can contain much information necessary for viewport prediction, previous studies can only extract these information manually, which requires in-depth knowledge about the topic of the video. Moreover, extracting these information by hand can still miss some important keywords from the subtitles and limit the accuracy of the viewport prediction. In this paper, we focus on automate this extraction process by proposing three types of automatic keyword extraction methods, namely Adverb, NER (Named entity recognition) and Adverb+NER. We provide an analysis to demonstrate the effectiveness of our automatic methods compared to extracting important keywords by hand. We also incorporate our methods into an existing viewport prediction model to improve prediction accuracy. The experimental results show that the model with our automatic keyword extraction methods outperforms baseline methods which only use manually extracted information.
Neural Style Transfer with distortion handling for Audio and Image

The advent of Artificial Intelligence (AI) has made major contributions in solving the most difficult problems in various research area. It has applications in domains such as medicine, agriculture, education, tourism, finance, etc.. Even though there is a lot of work done in Artificial Intelligence research, there is still huge hope in exploring its subdomain. As AI becomes an unstoppable technology force, it raises some difficult questions about the future role of humans in an increasingly automated world. The general consensus is that AI cannot replace human creativity. As AI makes inroads into various fields, even human never thought it would, it keeps challenging us to think in new ways. This is what inspired us to take this idea as a subject matter. This led into the technique of neural style transfer. The main idea of NST is to combine the style features of one image, with the content features of another image, called the content image to handle the distortion in images and audio inoder to increase the quality. Existing methods had distortion in their outputs. So, tuning of the loss function to provide a better output is explored in this work.
A Comparative Study on Email Phishing Detection Using Machine Learning Techniques

Phishing Email can be described as an email that looks exactly like a legitimate email, but it is designed by phisher with an intention to deceive the email’s user. The purpose of phishing email is to trick email user to visit fake website that looks exactly like a real one or to trick user to download the available attachment in the email without knowing that they are downloading virus into their machine. As the number of phishing emails are increasing from day to day and due to the complexity in detecting phishing email, there are numbers of continuous researches that have been done to improve existing detection tools or to develop a new one. To provide a thorough understanding of phishing attacks, this paper provides a brief explanation on phishing email and phishing attack. This paper presents the comparison of previous studies in commonly used Supervised Machine Learning techniques on detecting the phishing email attack such as Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Support Vector machine(SVM). The findings of this study concluded that SVM and RF are the best techniques that can be used to detect phishing email.
Securing IoT Medical Data using Cryptography and Steganography

With the rapidly evolving advancement in digital technologies multiple domains has been witnessing vast developments and enhancements ranging from home appliances to space dynamics. Especially automation sector has acquired maximum contributions from advanced computer technologies and thereby the necessity for automation in IOT has also increased dramatically. More manual tasks are getting automated these days using smart devices which can communicate with each other over internet. IOT enabled smart devices are used in many domains including Health care, Defense or military operations, space research etc. Due to the recent industrialization, IOT has seen tremendous growth. Multiple vendor products are available to complete a critical task. One downside to this advancement is there is no common / generic standard to be followed by these vendors which creates gap in data security. Data loss while exchanging data through smart devices need to be prevented so that personally identifiable health data or military information is not available public over the wire. In this study we have created a scheme that uses elliptic Galois cryptography protocol to encrypt medical data used in IOT. This encrypted data is converted into low complexity image using stenography technique. Adaptive firefly optimization algorithm is used to complete the work effectively. The study also covers necessary logic needed to decrypt the data from low complexity image.
Preliminary Analysis on Nursing Students Intention to Use Virtual Reality Application as a Learning Tool for Basic Human Anatomy Course

Virtual Reality (VR) refers to the use of computer technology to create a simulated environment. The simulated environment can be of something that represents a real-world scenario. The rapidly changing learning landscapes also indicate that learning methods in higher education institutions must evolve to keep up with the trend of incorporating integrated technologies into the learning modules. The existence of new millennial classrooms, blended learning applications and advance learning labs have allowed lecturers to experiment with new teaching styles. Nevertheless, these facilities may not exist in all faculties due to restricted budgets and lack of resources. Healthcare related faculties have been reported to be slow in acceptance and use of technologies in teaching fundamental subjects. A common fundamental subject across all health-related studies is Basic Human Anatomy. This subject requires visualization and memorization of human body parts that would be more interesting to have the use of technology devices as the supporting learning tool. As the uptake of technology namely VR is relatively low in the health education field, it is imperative to explore the suitability of VR learning tools for the human anatomy subject. A survey was conducted to first- and second-year nursing students who have taken and currently taking the basic human anatomy course. Data was analyzed using basic descriptive statistics to gauge the intention to use virtual reality application for the course. A proposed virtual reality intention to use model is conceptualized from previous learning models and results of the survey depicting factors that would affect learning of human anatomy subject through virtual reality.
Comparison of Meta-Heuristic Algorithms for Task Scheduling in Distributed Stream Processing

With the emergence of IoT and cloud computing, the demand for big data processing continues to rise. To expedite such big data processing, distributed stream processing systems (DSPS) are commonly used. However, because the rate of incoming messages to DSPS can vary depending on a stream application and execution environments such as like network conditions, it can be challenging to provide the necessary quality of services (QoS). Modern DSPS typically use heuristic or meta-heuristic algorithms to find near-optimal solutions to meet QoS requirements; however, it is still difficult to accomplish multiple QoS goals at once. In this paper, multiple meta-heuristic algorithms are evaluated to determine if they can simultaneously achieve multiple objectives, including response time and system failure. We implemented schedulers using various meta-heuristic algorithms operating within DSPS simulation environments. Then, we executed three stream applications utilizing various scheduling algorithms and demonstrated that meta-heuristic algorithms outperform a conventional algorithm.
Performance Enhancement of Improved Distinct Clustering Identification Algorithms for Multi-dimensional Datasets

The complexity of network environment increases day by day due to the increase of Internet usage. The network holds personal, official and sensitive medical banking data in a vast quantity and delivered to the receiver in a stimulated period of time. The detection of Intrusions in the network is most needed to reduce the cyberattacks. Now a days intrusion detection systems (IDSs) with Machine Learning(ML) plays a major role in the optimization of network issues. The major factor that affects the performance of IDS is outliers. In this proposed paper a Flawless feature selection algorithm combined with DBSCAN has been proposed to detect the noises and separate the given data into number of clusters. Then a Navie bayes classifier has been applied for empirical examination of different datasets. The results are analyzed with different classifiers and shows that the proposed method provides a better accuracy compared with other methods
Development of Tuition Centre Management System

Pro Eduelite Tuition Centre is a private learning institution for students. The issues of the tuition center management are caused by the weakness of the current method that is using the semi-computerized process to manage administrative work. Therefore, due to this issue, the Pro Eduelite Tuition Centre management system is proposed to assist the management activities by using a web-based approach. The user of this system consists of the manager, administrators, tutors, and parents. The system accepts registration of students, tutors, and staff, update of payment, update of class scheduling, report management, and others. The system development implements the software process model of system prototyping. It is developed by using Visual Studio Code and phpMyAdmin as the database system. The system is expected to reduce the workload of users when dealing with the involved task and contribute to the management activities. Lastly, testing is performed to validate and verify that the system meets the users’ needs and follows the stated requirement.
Twitter Bot Detection and Ranking using Supervised Machine Learning Models

The rise in use of social media has led to a sharp increase in the number of social bot accounts on platforms like Twitter. A majority of these accounts are used for malicious activities such as opinion mangling, hashtag spamming and directing users to spam URLs. These bots can cause societalunrest and manipulation if they are left unchecked and are not removed. This paper proposes a supervised machine learning approach with multiple models to not only detect these bots but also analyse the impact of these accounts to the community and rank them. Separate machine learning models are created to classify accounts based on user metadata, tweet content and posting history. The models are trained on the MIB dataset. A custom rule based algorithm is used to combine the predictions of these models to achieve an accuracy of 95.7%. This algorithm gives a better accuracy than any model gives in isolation.
Criminal Face Detection Algorithm Using LPB

Recognizable proof of criminal is done through thumb print or by coordinating the data with the “fir” records ID. Be that as it may, this sort of recognizable proof is impacted as a large portion of criminal these days getting cleverer to not leave their unique mark on the scene. With the presence of safety innovation, cameras especially CCTV are placed in a few public and individual regions to create police examination exercises. The recording of the CCTV will be acclimated lay out suspects on scene. In any case, because of confined code created to precisely locate the likeness between symbol inside the recording and recorded symbol of hoodlums, the law upholds unique mark distinguishing proof. During this paper, a programmed face acknowledgment framework for criminal data was arranged exploitation recognized Chief Part Investigation approach. This technique will be ready to locate face and recognize face precisely. This might work with the law requirements to locate or recognize suspect of the case assuming no unique mark gift on the scene. The outcomes show that in regards to 80th of information symbol will be coordinated with the model data. Challenges actually acknowledgment is among the normal objections connected with mental element maturing. The ongoing series of analyses in this manner researched face and individual acknowledgment in youthful and more established grown-ups. We tend to inspected anyway inside space and cross-area reiteration comparably as phonetics preparing meaningfully affect familiar face acknowledgment, and examined every action and occasion related mind potential (ERP) measures to detect explicit cycle phases old enough related deficiencies.
Convolution Neural Networks Machine Learning model to classify the polarity of tweet using Gensim Word2Vec

Twitter is a platform used by people to express their opinions and display sentiments on different occasions. Twitteris one of the non-traditional data sources with unlimited potential. It contains a large reserve of data sets which are easier to access and collect when compared to others. One of the available tools is twitter API which lets us collect data and various other information about the tweets. The tweets are collected through Tweepy and the tweets are being labeled through Text-Blob. The process of analyzing the tweets and the texts and emojis in them to classify the opinions of the people and their reaction towards the tweet, along with the viewpoint the owner of the tweet into positive, negative or neutral. To increase the processing of the tweets are preprocessed during collection of data itself. For accuracy we will create a vocab using the W2R (word to vector) model containing all the related words. The words are vectorized with the relationship among them. This makes the prediction easier. Then through implementation of CNN (conventional neural network) we insert the embedding layer which is formed with vectorized words and a LSTM (long-short-term-memory) layer. The accuracy value of the model is created and we can analyze the tweets of the particular domain. TheDataset for each text will be chosen separately by using Entity Identification. The major challenges such as Domain Dependency, World Knowledge and Pragmatics can be handled through selection of major entities and obtaining data related to it. This process can give us the current condition or status of the topic involved so that it will be easier to find out the anomalies. Another major challenge Thwarted Expectations can be handled using emoji and hashtags present in the obtained text. The more detailed preprocessed data helps in better end results and the model is a hybrid approach which will outperform the baseline.
Detecting Hostellers Using Face Recognition

For education, a large number of people are migrating to different cities. The majority of them live on campus in a dorm. The majority of hostel students leave the campus without authorization from the tutors and wardens. It is difficult for universities to identify a hosteller who has fled the campus when hundreds of students depart at the same time. Institution lacks a proper system for identifying them. Hostellers who leave the campus without authorization tend to engage in unethical activities, resulting in a negative reputation for the institution and the hostel. The overall goal of this paper is to design and implement a system to detect hostel students who are leaving campus. Face recognition technology will be used to detect hostellers, and if they are found, the warden of the hostel and the tutor of the particular student will be notified through SMS. To keep track of the records, a CSV file is also created. This project uses Histogram of Oriented Gradients (or HOG) method at backend to detect faces. Dlib library is used to find facial recognition and to wrap the image. Convolutional Neural Network (CNN) is used to create 128 measurements which is later used to find the identification of the person. OpenCV is used to access. This method requires no human involvement, making it simple for institutions to implement. This technology can be utilised to avoid problems and incidents caused by unauthorised hostellers leaving campus.
Cognitive Based Approach for Intellectually Disabled Person

Intellectual impairment (ID) is a condition in which a person’s intellectual functioning is significantly below average, with an IQ of 70 to 75. People with this condition have difficulty with both intellectual functioning and adaptive conduct. They have difficulty thinking, comprehending, caring for themselves, socialism, and communicating. Being unable to live alone due to the need for assistance and supervision. The severity of intellectual impairment varies. Intellectual disability is caused by a wide array of genetic and chromosomal disorders, which also includes down syndrome and fragile x syndrome, as well as infections encountered during pregnancy and accidents.ID individuals are prone to hyperactivity, restlessness, and aggression, and they are more likely than the general population to experience a wide range of emotions and psychological issues. They have trouble controlling their emotions, which can lead to major issues like an elevated heart rate or wandering of f and becoming lost. It is vital that they have a guardian or parents who will always watch after them. However, in today’s busy world, everyone has their own chores to fulfil, so we designed a device that would monitor the patient’s heartbeat and add GPS and GRS in order to make the lives of ID individuals easier and less reliant. The device can detect changes in a person’s heart rate or their disappearance and send a message to the guardian advising them of the changes or providing location information in the case of disappearance.
X - Ray Image Analysis in Identification of Bone Cancer Using Laws Features and Machine Learning Model

The human bone is significantly controlled by the humanoid system. The strange development and fragmentation of unit in bone prompts a bone tumor, and further development of bone growth prompts to bone disease. Generally, Image processing plays the vital role than lessens human judgment that provides exact outcomes. Our paper aims to specialize in the utilization of various strategies for the revelation of bone malignant growth in X-RAY images. In this study, bilateral filter (BF) is used as a pre-processing tool to reduce and smooth out noise. Segmentation is achieved by adaptive histogram followed by binary thresholding and well-founded location of the desired region. Statistical and Laws features are used to train the Support Vector Machine (SVM). The resultant result will be reviewed through different execution measures that incorporate accuracy, sensitivity and specificity.