Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitigating YouTube Recommendation Polarity using BERT and K-Means Clustering

YouTube's recommendation system is famous for its success in maintaining high retention rates. The cause of its success is its ability to learn and predict an individual user's preferences appropriately. An unintended consequence, however, is that users get stuck in what is known as their own “echo chambers” when dealing with and feeding users back their preferences. These echo chambers can cause increasing perspective bias within users, making it difficult for users to understand differing opinions. This work aims to prepare a model that counteracts YouTube's recommendation system by forcefully exposing users to content from varying viewpoints. The SSKA pipeline (Suno Sabki, Karo Apni) is a complementary deep learning model that involves Natural Language Processing (NLP) and K-Means clustering. It utilizes modern software libraries such as the YouTube API (Application Programming Interface) for data collection and was trained and tested on a varied set of users. The results prove that the model is successful in decreasing the bias recommendation by exposing users to the content of varying opinions and helping them break away from their echo chambers. The proposed methodology of explicitly exposing users to the content of varying opinions can positively impact local societies and the global community.
Reading Aid and Translator with Raspberry Pi for Blind people

Many people suffer with visual disabilities. As per data from WHO about vision impairment, there are 1.3 billion humans surviving with different kinds of eye vision problems while 36M people are with zero vision. Reading is one of the most important and difficult task for the Visually impaired people without anyone's help. This paper proposed a device which consists of the hardware incorporates a camera which will connects to Raspberry pi. The camera captures printed text which person wants to listen. Raspberry pi will use the Optical character recognition (OCR)scheme installed on it, which is used to convert text to audio speech. Once it is captured then it is scanned and then this captured text is spoken out by the TTS conversion unit (TTS ENGINE) placed in Raspberry pi with earphones. In this paper the implementation is done for two languages that is English and English text can be converted and read out in local language(telugu). So visually impaired people can use the language based on their choice to listen to the output from OCR. This device is not only helps Visually impaired people but also for many people who are unable to read different text documents which are in English.
Analysis of the Use of Gamification Elements in E-commerce Applications as a Purchase Intention Factor Using the Structural Equation Model (SEM)

Shopping through e-commerce has become a habit for Indonesians. Amidst many e-commerce, gamification is a method used to increase purchases on their platform. However, the influence of gamification on the intention to buy appears to requires additional research, particularly on Indonesian e-commerce platforms. The purpose of this research is to investigate the application of gamification elements in e-commerce as a purchase intention factor using the Structural Equation Model (SEM). A survey was conducted, and 157 valid questionnaires were collected from Indonesian e-commerce users. The result showed that utilitarian values, hedonic values, and spending less time/energy on gaming all have a notable influence on user intentions toward games play on e-commerce platforms. However, utilitarian values have no notable influence on user’s purchase decisions in e-commerce. Furthermore, social value and perceived enjoyment have a significant impact on the user’s purchase intention in e-commerce, whereas game use intention has no notable influence on the user’s purchase intention.
The Automated Screening of Ultrasound Images for Nuchal Translucency using Auxiliary U-Net for Semantic Segmentation

Nuchal translucency (NT) is the sonographic indication of a collection of fluid under the skin behind the foetal neck during the first trimester of pregnancy. The thickness of the nuchal translucency is a key sign for detecting chromosomal abnormalities such trisomy 13, 18, and 21. During the initial trimester of pregnancy, it is assessed by ultrasonography. This research has designed and created a one-of-a-kind system that recognizes the NT area from ultrasound images using a hybrid deep dense neural network, which is implemented using a U-Net architecture and a mix of reinforcement learning to enhance localization and region recognition. This research presents a completely automated method for calculating NT in ultrasound data. Experiments reveal that the algorithm recognizes the NT area efficiently and effectively and offers accurate NT measurement, implying that it is suitable for clinical usage. According to an experienced physician's ground-truth, we obtained a true positive rate of 99.98% for nuchal region recognition and around 70% of measures had an error of 20 pixels (which amounts to 0.02mm). When foetal NT is measured using a U-net automated methodology rather than the standard manual and semiautomatic methods, the results acquired more trustworthy.
Bug Predictive Models based on Data Analytics and Soft Computing Techniques: A Survey

As software projects become larger and more complex, bugs pose a significant threat to their consistency and efficiency. During early phases of software development, bug prediction essentially enhances vital aspects such as software efficiency reliability quality, cost of development. Hence, various software bug prediction techniques have been presented previously but there is no research that focuses on surveying logical analysis and Just-In-Time (JIT) bug predictions with the consideration of data analytics and soft computing techniques. Hence this research investigates Soft computing and hybrid logistic model by determining imperfect range of data, class labels with heuristics prediction of bugs. Also, survey data analytics techniques such as clustering and oversampling techniques with JIT prediction for forecasting software bug in future. The study also compares various software bug prediction techniques to determine their effectiveness. Then, mentioned the basic benefits and drawbacks of the various bug prediction techniques. Moreover, future directions are provided to overcome the existing impact of various techniques in software bug prediction.
Wearable Sensors in Daily Life: A Review

Wearable sensor technologies have improved people’s daily lives through their applications in almost every field. Sensor technologies of inventive kinds are used in an extensive variety of applications in lifestyle, healthcare, fitness, manufacturing, etc. There have also been crucial issues in making significant improvements to the actual mechanical, electrical, and optical sensing methods mainly in upgrading the precision of identification of wearable sensors to various stimuli. With an extensive study of the basic demands in wearable device technology as of now, the road map becomes clearer for creating greater innovations in the future. This is a review that gives an outline of types of wearable sensors by the score that is utilized in daily life.
A Comprehensive Study on Video Captioning Techniques, Benchmark Datasets and QoS Metrics

Video captioning is widely utilized nowadays in a variety of applications for people with special needs, and more specifically, people who are visually impaired. Technology improvements in object detection and natural processing have immediately infused the aforementioned common jobs. When an input image is provided to the system, one such example of this fusion produces image captions, which provide a brief description of what is shown in the image. With some adjustments to the current techniques, this image-related fusion was then shifted to be applied to videos. With the recent success of activity recognition on video, there is growing interest in the challenge of video captioning, which involves creating a sentence-or, more generally, a paragraph-to explain a video clip while capturing its visual semantics. In this article, we evaluate numerous video captioning techniques that have been proposed in the literature along with benchmark datasets for video captioning and commonly accepted evaluation metrics. We highlight a number of potential future directions for the issue of video captioning at the conclusion.
Depthwise Separable Convolution ResNet with attention mechanism for Alzheimer's detection*

Convolutional neural networks (CNNs) have been widely applied for brain disease detection based on neuroimaging data. It shows significant and accurate results. Alzheimer's disease is one of the most common neurodegenerative disorders affecting a person's ability and cannot be reversed. The early detection of this disease can slow its progression. Researchers adopt Magnetic Resonance Imaging (MRI) modality as a biomarker for Alzheimer's disease detection based on CNN. Building a CNN model that enhances reliable feature extraction and representation is challenging for Alzheimer's disease detection. The main drawback of CNN is that increasing model size could affect the model's performance. Furthermore, CNN cannot capture long relationships between image features. In this paper, we evaluated different machine learning algorithms for AD diagnosis. In addition, we proposed a Depthwise Separable Convolutional ResNet with an attention mechanism to address these challenges. The proposed network replaces the standard convolution operation with a depth-wise separable convolution to decrease the network parameters and size, which reduces overfitting. The attention mechanism is employed to boot the network feature representation capability. We evaluated our model using MRI data from the Open Access Series of Imaging Studies (OASIS) dataset. The proposed model outperforms different CNN models reaching an accuracy of 99%. We evaluated the proposed method for Multimodal binary classification of Alzheimer's disease and we obtained an accuracy of 92%.
Integration of Machine Learning Algorithms Classifiers and Sequential Forward Selection Features in Diabetes Prediction

Diabetes mellitus, commonly known as diabetes, is a medical condition in which blood sugar levels rise for an extended period. Diabetes is a chronic condition that affects thousands of people. It has resulted in the deaths of millions of people across the world. The only goal of this research is to create a machine-learning model for the early detection of diabetes utilizing supervised machine-learning algorithms and various feature selection strategies for the optimal number of features. On the diabetes dataset obtained from the online platform Kaggle, we employed three distinct machine learning classification algorithms, including Logistic Regression, Decision Tree classifier, and Random Forest classifier. All machine learning models scored roughly 86% accuracy, but the Random Forest model achieved 86.51 % accuracy on a test set with only a subset of four features. The first features are High Blood Pressure, High Cholesterol, Body Mass Index, and General Health, with an accuracy of 86.57% on the train set. The Sequential forward Selection algorithm was applied for the feature.
Cloud Based Electric Bike Speed and Safety Control System

Rate command is actually certainly not a common include discovered in electrical bikes. Lots of electrical bikes execute a RP2040 operator that doesn't consist of comments based upon noticing rate. Just like vehicles, rate command could be preferable for chauffeur convenience as well as security. Furthermore, precise rate command is actually likewise extremely useful when validating vibrant designs of single-track cars, which is actually our inspiration. This report explains an inexpensive RP2040operator continuously screens the strangle worths for an instrumented electrical bike. To accomplish this, our team utilized ETC transmission deal with as well as is actually fixed. The Strangle indicator is actually refined due to the operator as well as it after that runs the electric motor with electric motor chauffeur. The electric motor voltage is actually different according to strangle worths so as to command its own energy as well as rate. Likewise, the operator continuously screen rate sensing unit worths. The peed sensing unit deals with venue impact concept to continuously transfer the tire RPM. This RPM worth is actually shown on the LCD screen due to the operator. The electric motor rate as well as sensing unit keeping track of is actually shut off when the primary change s shut off. The finish procedure restarts as quickly as the change is actually switched on. Therefore our team effectively establish as well as examination our very personal Ebike operator utilizing RP2040.
A Polymorphous Energy Efficient Resource Allocation Approach (PEERA) in Cloud Computing

Cloud computing is a cutting-edge innovation in which a shared pool of remote services is provided on demand. One of the biggest challenges is lowering energy consumption without compromising performance in a virtualized cloud environment. It has become an issue of interest in the academic and business communities to mitigate energy requirements in virtualized cloud data centres besides achieving maximum efficiency. Allocating jobs for Virtual Machines (VMs) is the focus of Task Scheduling, which aims to optimise energy consumption, resource use, performance, and cost. A Polymorphous Energy Efficient Resource Allocation Approach (PEERA) is introduced to optimise energy and performance parameters like Energy Consumption, Makespan and Resource Utilisation. The simulation reveals that the proposed algorithm has better results for resource allocation while simultaneously lowering Makespan and Energy Consumption compared to its contemporary counterparts.
XX.—Studies in Practical Mathematics. II. The Evaluation of the Latent Roots and Latent Vectors of a Matrix

In many branches of applied mathematics problems arise which require for their solution a knowledge of the latent roots of a matrix A , sometimes only the root of greatest modulus but often the second and other roots as well, and the corresponding latent vectors. A few examples, among many that might be cited, are problems in the dynamical theory of oscillations, problems of conditioned maxima and minima, problems of correlation between statistical variables, the determination of the principal axes of quadrics, and the solution of differential or other operational equations. It is important, therefore, to have a choice of methods for obtaining latent roots and latent vectors without undue labour, and the object of the present paper is to augment the existing store of such methods.
Glioma Brain Tumor Segmentation using Deep Learning: A Review

Brain tumours are observed as the tenth major cause of death in the world, accounting for 85-90% of all primary central nervous system tumors. Every year, this fatal disease kills tens of thousands of individuals, including children, adolescents, and adults. Gliomas, one of the most common types of brain tumours, typically necessitate numerous stages of tumour excision because they are difficult to precisely detect due to their heterogeneous sub-regions, location uncertainty, and ambiguous borders. Magnetic resonance imaging (MRI) is largely recognised as one of the most effective imaging modalities for identifying brain tumours. However, manually diagnosing glioma brain tumours from MRI pictures by medical professionals, on the other hand, is a difficult, expensive, and time-consuming job. Deep learning-based automatic diagnosis has the potential to save medical practitioners time while greatly enhancing diagnostic accuracy. The purpose of this paper is to review current deep learning-based techniques for segmenting glioma brain tumours. In this paper we compared the Dice Score outcomes of the most successful strategies, examined the outcomes, and displayed them as bar graphs. In addition, we have highlighted some of the most often used MRI procedures and frequently occurring brain tumour types. Finally, we covered a number of unresolved research issues for upcoming researchers.
Digital Disparity in The Technological Era of Big Data and Artificial Intelligence

The following study is based on a research position which explicitly provides an assessment of the digital disparity faced by educational institutions of higher education in developing countries. The intention behind the disparities in three parameters – discourse, policy and sector are provided. Understanding the different approaches, there is a need for an inter-disciplinary study in future endeavours where direct observation and face-to-face interactions which educational institutions of developing regions can provide an in-depth analysis behind the presence of such discrepancy or disparity. The outcomes of the research, as a part of a positional study, helps in understanding the impact of unawareness, lack of educational development and absence of collaboration between educational administrations and institutions which have caused a distinctive disparity in the era of big data and artificial intelligence.
Comparative Evaluation on Various Machine Learning Strategies Based on Identification of DDoS Attacks in IoT Environment

IoT is a combination of networks that have the ability to collect and share the information through web. Though IoT is used in areas such as healthcare, smart cities, agriculture, industrial automation and home automation, it is also used in other fields. The challenges faced by the IoT devices are security, privacy, confidentiality, integrity, and technical complexity. In Distributed Denial of Service attacks, the attacker sends massive service request to the IoT device which cannot handle such high traffic, resulting in disruption of services to the end user. Modern types of DDOS attack are still more complex, and they are nearlyimpossible to identify or mitigate using classic intrusion detection systems and methods. In the last few years, Machine learning plays a significant role in identifying Distributed Denial of Service attacks effectively. We conducted a literature review on different types of DDoS attacks based on ML techniques. The model’s performance is tested on several classifiers to choose the optimal classifier with high accuracy. We next go over each of the ML methods for IoT security in detail. The limitations and problems of current research are discussed.
Monitoring and Predicting Performance of Students in Degree Programs using Machine Learning

In higher education, it is frequently necessary to find a solution to the issue of student retention. Managers and scholars in the field frequently rely entirely and expertise to recognise potential risk factors and students. This study looks at the research on the approaches and metrics being used now in learning analytics. While there are techniques available, our research shows that they are not focused on identifying difficult pupils as soon as possible. With the use of contemporary computer vision tools and methodologies, we design a novel statistical tool for class participation and develop a predictive model. It shows how pupils may be identified with 97% accuracy as early about week three. Additionally, we place this finding in a suitable pedagogical framework to support its application as a component of a more complete student assistance system. There is a wealth of publicly available learning data that can be utilised to analyse individual student behaviour, address their issues, improve the instructional climate, and enable data-driven choices, thanks to the support of software learning platforms. Based on that, the paper focuses on developing a system that can effectively monitor and predict the performance of students in degree programs by using the evolving machine learning. As retention of students has becoming a serious concern, it was necessary to develop performance monitoring and predicting system. Using a comprehensive research methodology this study has analysed all relevant information to the topic to draw an effective conclusion at the end.
Smart Water Meter Using IoT

The expanded living spaces requires more quality water distribution administration framework due to the increased living spaces in rural and urban areas. In order to achieve rise to amount of water conveyance to all of the natural surrounds, it is thus essential to design a planned water supply framework. Internet of things, another idea is the envisaged framework for controlled water circulation and defect identification. The key idea of this study is to provide a cost-effective framework to achieve better water supply by routinely monitoring and furthermore controlling it from a central server to avoid issues with the water supply to the habitats. The suggested design makes use of solenoid valves, an Arduino as a minicomputer, and a water flow sensor. The valve and flow meter are controlled by an Arduino board. Using the Cayenne application, where the water requirements for each habitat can be set, the purchase of water ought to be feasible. The Arduino Ethernet Shield V1 is used to connect the Arduino board to the internet. The Wiz net W5100 Ethernet chip determines this (datasheet). The Wiz net W5100 provides an IP stack that is suitable for handling both TCP and UDP packets. The proposed design addresses the issues of overflow, overuse, water acquisition, and distribution in an acceptable manner.
Risk Minimization Approach for Image Restoration Using L2 Penalty in EM Framework

This paper presents a modified expectation maximization (EM) technique for the restoring the image from its degraded model. The proposed methodology comprises the concept of minimization of the risk associated with the L2 penalty in the maximization step (M-step) of the algorithm. This method optimizes the worst case error, and at the same time also ensures the uniform shrinkage of the wavelet coefficients by using proper regularization parameter. The proposed methodology achieves 8.77 dB which is larger than the ISNR achieved by the state-of-the art methods.
Communication using RedTacton Devices for Tourism development

Technology that enables communication between humans and nearby objects is essential to any user-friendly services. In order to connect equipment situated in, on, or close to the human body, Body Coupled Communication (BCC) systems employ the human body as the medium through which a signal is carried. Three tiers of connectivity are required for the implementation of ubiquitous services: local area networks (LAN), wide area networks (WAN) and human area networks (HAN) for communication to shared data, media, and communication devices inside the considerably smaller communication regions. Using surface of human body as a fast and secure network transmission line is a technique called RedTacton which offers a wide range of applications, including wearable technology, user identification management, marketing, intuitive applications, and smartphones. In this work, an effective communication between RedTacton devices with Arduino UNO board is developed for tourism applications. This model achieved half-duplex communication at a 10Mbit/ s rate is supported by the human body as a transmission medium.