Reviewerly/exHarmony huggingface dataset:
Development of Augmented Reality in Online Learning Education

ABC University is one of the largest private universities in Indonesia that has implemented online learning. Many teaching materials have been provided, including presentation materials, lecturer notes, videos, and practicum modules. The purpose of this research is to develop AR as a new technology in online learning. In addition, Augmented Reality (AR) is used as additional material to support success in online learning. This research took one of the courses in the three study programs used for research, namely management, computer science, and accounting with a total participation of seventy-nine people. This application is designed using the UML (Unified Modeling Language) model and is built using open-source applications, namely Unity, Blender, and Android Studio. The development of this learning system uses the ADDIE model design instructor framework. The assessment of this application was obtained for measurements using N-gain in the “moderate” and “quite effective” categories, while for measurements using usability testing the learnability value was 3.88, the memory value was 4.11, the efficiency value was 4.04, the error value of 4.10, and a satisfaction value of 3.91.
Consideration of Language Learning Service with Visualized Vocabulary Map Derived from WordNet

The authors aim to propose highly effective, efficient and satisfactory service of language teaching and learning by means of educational technology from the viewpoint of usability engineering. In the field of computational/quantitative linguistics based on corpora data, many linguists have been numerically analyzing lexical characteristics for each English vocabulary and compiled them as a vocabulary list which is machine-readable since 1960s. On the other hand, a lexical database of semantic relations called WordNet has also been developed in the field of computing science. This study consists of three components. One is to enrich, refine and integrate already-existing vocabulary information in terms of “frequency”, “difficulty”, and “relativeness”. Another is to develop the system of language learning and teaching service with the integrated vocabulary information. And the other is to evaluate the system from usability engineering point of view. So far, the authors reached and conducted the first two kinds of studies, succeeded in deriving the newly-integrated vocabulary list and visualizing the word relations in each experiment.
Cloud-Based Augmented Reality As a Disruptive Technology for Higher Education

Augmented reality (AR) within the context of higher education is an approach to engage students with experiential learning by utilising AR technology. This paper discusses the process undertaken by a teacher in higher education in designing and implementing cloud-based AR lesson for the students. The methodology engaged was case study at one institution of higher learning in Malaysia. The AR teaching process involves six stages, beginning with the selection of the course, followed by selection of the topic, designing of the AR teaching plan and the implementation of the AR lesson. Upon completion of the implementation of the AR lesson, the teacher and students would provide reflection of their experiences. The process concludes by the improvement of the AR teaching plan by the teacher. The study found that cloud based has indeed disrupted higher education in terms of providing richer learning experiences to the students, as well as enhanced teaching practices for the teachers. Hopefully, this paper would provide insights into the practices of AR teaching and learning approach for teachers in general, and within the context of higher education in particular. It is also intended that the six-steps process outlined in this paper becomes a reference and be duplicated by teachers at large who might be interested to design and implement AR lessons for their own courses.
Design And Analysis of Human Computer Interaction Using AI Intelligence

The improvement of user experiences, the performance of tasks, and overall quality of life is one of the declared goals of Human Computer Interaction, which refers to the interaction between humans and computers, artificial intelligence, and intelligent enhancement through the design, evaluation, and use of information and communication technology. In this paper, we design and develop a model for human computer interactions using artificial intelligence model. It is possible to capture both high-level interaction techniques and low-level behaviors in touchscreen grid interfaces on mobile devices when deep models and analytical comprehension are coupled. These interfaces are typically found on smartphones. The utilization of mobile devices enables this to become a reality. These findings show that deep learning has a significant promise for modeling sophisticated interaction patterns that are intractable to more typical heuristic-based models. Differential Evolution is utilized to enable the AI to operate on these models. The simulation is conducted to test the efficacy of the model and the results shows higher level of accuracy at the training and testing phases.
Smart Tracking System for Traffic using Android based Application

Cities and transportation have expanded together since the earliest significant human settlements. The same factors that tempt people to reside in densely populated areas also fuel the frequently atrocious levels of traffic congestion on city streets. Since the widespread use of vehicles, one of the primary issues modern cities confront is traffic congestion. A quick journey to the convenience store might take up to 30 minutes due to slowness or traffic congestion. Road rage, road bullies, and serious accidents are caused by traffic congestion. To overcome these challenges, we will be creating an app that will allow users to register their concerns so that assistance may be sent as quickly as possible in order to make the traffic management system and commuters' lives more convenient.
Application of Serverless Computing in Blockchain Distributed Technology

Serverless is a peer-to-peer system that is associated with the cloud architecture that aims to reduce the efforts required by the cloud manager to provide an efficient service. Blockchain enable collaborative distributed mechanism to enable a decentralized management of resource allocation. The resource allocation is been a major concern when blockchain is enabled in the serverless computing. In this paper, we develop a serverless computing model that is embedded with blockchain manner in a distributed fashion. The formulation is made in such a way that if fulfils the task allocation in a better way with increased makespan and reduced latency while a resource is allocated for required operation.
Best Ways Using AI in Impacting Success on MBA Graduates

It is not an easy decision to make when deciding whether or not to let a student continue their studies in a graduate program. There are several factors to take into consideration. An application is analyzed based on a variety of different criteria, and the results of this examination are utilized to provide a prediction of the applicant's likelihood of being successful. Through the course of human history, regression analysis has been used as a methodology for the development of many kinds of prediction systems. On the other hand, it has been demonstrated that the models that were presented in this research had a very limited capacity for predictive ability. An empirical examination of these relationships was carried out by these authors using survey data acquired from MBA students attending a private university. The structural equation models that were generated using this information were used in the investigation. It was found that the content of the courses themselves was the single most critical factor in correctly predicting all learning, satisfaction, and quality.
3D-CNN Architecture to Improve the Classification Accuracy of the Real-Time Images from IOT Devices

The classification of real time images from the fast data capturing devices in Internet of Things (IoT) environment is a critical task. It requires suitable processing and development of a model for increased accuracy in classifying the objects in real-time. Therefore, the necessity in improving the accuracy of classifying the instances is needed post performing the modelling, building and development of a model. In this paper, a three-dimensional (3D) Convolutional Neural Network (CNN) is developed to increase the process of classification for the objects in the real-time environment. The objects needed to train the classifier is supplied and the model is built in python environment. The results show an increased classification accuracy in detecting multi-objectives than the state-of-art models.
Sentiment Analysis Study of Human Thoughts using Machine Learning Techniques

The objective of our research is to investigate how machine learning methods can be utilized to analyze the emotions and attitudes expressed in human thinking. The study collected data from social media platforms and online forums, which included a mix of positive, negative, and neutral sentiments expressed by users. Several machine learning algorithms, including Naive Bayes, SVM, RNNs, CNNs and LSTM Networks, were employed for sentiment analysis of the data. The study found that the performance of these algorithms varied depending on the type of data being analyzed, with some algorithms performing better for short texts such as tweets, while others worked better for longer texts such as news articles. Additionally, the study found that combining multiple algorithms could improve the accuracy of sentiment analysis. According to the findings, it appears that the utilization of machine learning methods can serve as a potent means of scrutinizing human thoughts and emotions, which can have implications for a range of applications, including marketing, politics, and mental health. This article provides a comprehensive and organized review of sentiment analysis methods. The motive of the review is to analyze and categorize available techniques while comparing their strengths and weaknesses. The aim is to gain a deeper understanding of the challenges that exist in the field and to identify potential solutions and future directions. To facilitate this analysis, we also introduce several factors that can be used to evaluate the advantages and disadvantages of each method within its category.
Developing Fuzzy Rules for Small Scale Rearing of Black Soldier Fly Eggs

Over the years of research and study have revealed that the black soldier fly, a non-pest, and harmless insect, has the potential to solve agricultural waste management problems. One of the major factors to be considered in rearing the black soldier fly is the egg stage, their most vulnerable phase in their life cycle. The hatching duration and percentage of the black soldier fly eggs were found to be influenced by temperature and humidity. The researchers used numerical analysis and observations from three different temperature and humidity levels to construct fuzzy rules. 25 fuzzy rules were encoded to the Mamdani Fuzzy Inference System techniques, which include the application of triangular and trapezoidal function where five member functions for the input and output were used, construction of a 5x5 matrix, application of AND logic operators, and weighted defuzzification process. The consistency of the created model was evaluated by comparing the actual value of the dataset to the estimated value of the system. Showing an accuracy value of 93.22% for hatching duration and 95.38% for hatching percentage.
An Automation Innovation of Gearbox Vehicle Control by Using Machine Learning Based Robotic Operation

There are many vehicle exchanges around the world are using the manual transmission and automatic transmission are the most popular. At this time, many popular manufacturers have started using the robot version in their new products. The gearbox of the robot is, in fact, mechanical; it has an additional automatic clutch and gear shift. Accordingly, the operation of the transmission is not completely dependent on the driver, as in other options, but on the electronic control unit. For the proper functioning of the transmission the driver needs to correctly transmit only the incoming information. In this paper an automation innovation was proposed to control the vehicle gear shifting functions by using the machine learning based on the robotic operation model. An automaton or robot, after some time, must first learn the device of a new invention. The automatic gearbox received a friction type clutch. It is a disk set or a built-in separate mechanism. The most reliable and durable design can be called the double clutch design.
Automating Decision-Making for Hiring Brilliant People While Taking Risk Factors Into Account: A Data Mining Approach

In order to pick multi-talented employees from a large number of resumes, the human resource department (HR) is required to apply more accurate talent evaluation programs. However, rather of focusing on risk issues, the majority of talent evaluation tools evaluate talent. This article suggests a technique for selecting qualified competent staff resumes without taking risks into account using the technology for data mining called mining by association rules (ARM). The system's automatic intelligence agents (AIAS), which was created making decisions using a knowledge-based system based using logic principles and data gathered employing the ARM methodology, information from subject matter experts and prior learning experiences, directs the activities of the HR Department. The relevant experimental findings from AIAS allow HR departments to quickly decide who to hire for talent employees without wasting time for both candidates and employers during interviews. The useful experimental findings from AIAS allow HR departments to make quick selections for accurately hiring talented employees without squandering both employer and candidate time during interviews.
Machine Learning-Based Approach for Hand Gesture Recognition

Computer vision and Machine Learning are used to comprehend and extract information from photos and videos. This paper examines how computer vision and machine learning are used to recognize hand gestures, which are the most common form of everyday communication. The proposed method intends to demonstrate how to utilize computer vision and machine learning to identify motions and apply these techniques using Python and OpenCV. These allow us to distinguish hand gestures made in the air. The output, or the computer webcam stream, is displayed on the output screen.
An In-Depth study on Smart wearable Technology and their applications in monitoring human health

Savvy gadgets have been making human lives easier for a very long time, the explicitness of these has changed by the client needs and there have been difficulties in testing out the thought. There are a ton of brilliant gadgets on the lookout, including smartphones, smart watches, smart clothes, smart jewelry, smart footwear, and smart patches. Fitness has forever been a fundamental part of human existence, greater number of individuals these days are worried about their exercises, and their well-being and they don't have the opportunity or persistence to go for an exhaustive check on a daily basis, these gadgets have made it simpler for individuals to have a concise report of how their day seems to be as far as miles strolled, pulse, oxygen levels, no of steps taken, the complete calories consumed and so on. This paper means to frame the different present frameworks or gadgets accessible and spread out the challenges looked by the makers to send off these gadgets on the lookout.
Actual Effects of Cloud Computing Technology

The most widely used technology, i.e. cloud computing has been promoted as an essential component of the computing paradigm to provide useful solutions for any real and challenging scenario. These scenarios can affect education, health system, education etc. Not only the technology part, it is compromising various areas of development. Throughout the paper an attempt has been made to include realistic and probabilistic points of view to make a real understanding about cloud computing. With the introduction of cloud technology, its various components will also dominate the paper and the optimization system of cloud services will also help in what it is offering to the market. The paper also includes the most valuable survey covering various partners and their services. Also an outcome metric capable of identifying the proportion of cloud services company adopted versus non-adopted ultimately.
An Exhaustive Analysis on Security Issues concerning IoT using Blockchain

In today's world, the Internet of Things (IoT) has become an important part of daily life. Due to the exchange of information or data generated by sensors and various technologies used in IoT, the safety of an IoT system is becoming a major issue in technology-driven areas. On the other hand, blockchain, which started with Bitcoin, is now being actively explored for smart contracts that remove the need for a third- party and improve processes and transparency in different areas such as asset management, supply chain management, and healthcare networks. Furthermore, it has the potential to improve the quality of analytical systems by enabling users to trace immutable data from beginning to endpoint.The merger of both technologies, IoT and Blockchain, is one of the most noticeable advancements empowering digital transformation in every possible way. Blockchain endorses IoT devices, increasing reliability and clarity in the IoT system. With blockchain, IoT platforms, devices, and apps can run in a decentralized, scalable environment
Deep Learning Model on Blockchain for Secured Mobile Communication

The Internet of Things (IoT) is an open network model that aims to build and link the interactions between the devices and links. Conventional blockchain model aimed at increase the scalability but often it is limited by its capacity and performance. The deep learning algorithms aims to determine the parameters of the blockchain that finds the optimal value required to obtain an increased scalability without any limitations in its performance. In this paper, a deep learning model is integrated with the blockchain to improve the process of communication in a secured way. The deep learning model optimizes the necessary security parameters required to transfer the data in a secured way. The experimental validation shows an increased scalable task allocation than its predecessors.
Evaluation of Small Object Detection in Scarcity of Data in the Dataset Using Yolov7

Object detection had gained importance in previous decade due to large amount of data that is being generated throughout the world by cameras, mobile phones, satellite imaginary, medical image, social media, UAV etc. As hardware cost to render these images had been reduced significantly and we have access to plethora of algorithms, framework to detect the object and use this information to solve day to day problems. The object detection is most researched area but it still fails to detect and recognize small objects as detecting large objects had got more focus. But small object detection had got less attention and the algorithms and methodology developed for detecting large object does not yield the desired results and accuracy. In this paper we attempt to detect small objects by using state of art algorithm yolov7 and roboflow and try to evaluate the robustness of object detection with scarcity of data in dataset.
Disruptive AI Technology and Hate Speech: A Legal Redress in Malaysia

Artificial intelligence (AI) technology is becoming increasingly prevalent in society, offering a range of benefits and opportunities. However, with the rise of AI comes new challenges, particularly in hate speech. Hate speech, a type of expression that incites hatred or violence against individuals or groups based on ethnicity, religion, or other characteristics, has become a growing concern in Malaysia, with social media and online platforms becoming a breeding ground for such speech. In this context, AI technology has emerged as a potential solution for monitoring and regulating hate speech, but it also presents legal and ethical challenges that must be addressed. In view of double edge sword roles played by the development of AI, this article analyses the legal recourse available in Malaysia for disruptive AI technology and hate speech. The authors claim that AI systems are prone to errors and biases and that there is a risk of relying too much on such plans at the expense of human judgement. There are also concerns regarding the impact of AI on free expression and privacy rights. In addition, the author suggests that artificial intelligence be appropriately regulated to ensure that it is consistent with international human rights standards and national laws.
An Analysis of Image Captioning Models using Deep Learning

Developing a self-descriptive software for describing and explaining the activities seen in an image has become a necessary and interesting field of research in Deep Learning and Artificial Intelligence. The process of formulating precise description for an image that is syntactically as well as semantically correct is known as Captioning. The task commences with classifying the attributes of an image and extracting feature vectors using various CNN models. In the proposed work, we have demonstrated the working of three different CNN models i.e. Xception, VGG-16 and ResNet50 along with observing the impressive accuracy achieved by each model respectively. The dataset used in our proposed work is Flickr_8k dataset having 8091 images. The features extracted from these three models individually are fed to LSTM (Long Short Term Memory) model, an example of RNN model and further using this for sentence formation. We have generated captions using each model and verified the accuracy achieved using visual metric and BLEU (BiLingual Evaluation Understanding) metric. The three systems grant the good quality result and captions, on comparing the BLEU scores which are, 0.79 for Xception Model, 0.75 for VGG-16 and, 0.84 for ResNet50. ResNet50 is proven to be best for classification and feature extraction with 84% accurate captions on 50 epochs and also facilitates the benefit of overcoming the vanishing gradient problem. We believe this technology can help visually impaired people to recognize and interpret the correlation of activities happening in the image.
Privacy-Preserving Deep sigmoid NN Classification over Entropy based Cryptosystem in Cloud Environments

In recent years, privacy-preserving data mining has received a lot of attention. Signature creation methods, DNN categorization, and data mining with privacy are studied. Though there are various advantages to the existing privacy-preserving methods there will be some drawbacks too. To overcome the drawbacks in the existing methods, a novel efficient privacy-protection method called Deep NN classification protocol is preferable to an entropy-based signature cryptosystem is proposed for transferring the data kept in third-party CS. Initially, key creation is done and the outsourced data are encrypted, and a signature is generated. Based on the entropy, random numbers are selected. For that selected random numbers the helper value and entropy values are computed. To keep the data secure, an entropy-based signature is generated, and the ciphertext is shown without displaying the generated signature. To allow access to cloud-based data, the user needs to validate both the generated key and the entropy-based signature. The proposed technique protects database security. This approach outperforms other existing methods in terms of Cloud working mechanism time, Encryption, Decryption, Query-Encryption, and security, by the experimental results.