Miracle-dz/UniFAR-FTUs huggingface dataset:
E-Commerce Customer Churn Prediction Scheme Based on Customer Behaviour Using Machine Learning

Customer retention is crucial for any company to succeed given the rapid growth in the number of companies in established sectors. To analyze and research client retention, numerous approaches (such as data mining and statistics) have been offered. Building digital (CRM) which is known as Customer Relationship Management systems is a new trend that is growing in the global economy as a result of the explosive rise of digital systems and related information technologies. In the telecoms sector, where businesses are progressively going digital, this trend is especially pronounced. Predicting customer attrition is a key function of contemporary telecom CRM systems. Keeping consumers has grown increasingly crucial for insurance businesses, and causes for churning are challenging, according to competition in Iran's insurance industry in recent years and the entry of the private sector. Churners have continually been a serious drawback for each business that provides services. Churning drives up a company's expenses whereas additionally lowering its gross margin. Customers WHO request service termination are usually exhibiting client attrition. those that offer the information keep in government databases, furthermore because the organizations WHO fund the gathering of such information, are at the same time turning into a lot of conscious of however tools that enhance analytical capabilities additionally offer threats to the privacy of information records. However, it is the potential to forecast in case a customer wishes to avoid the service using predictive analysis according to previous utilization, the performance of the service, and other kinds of patterns. Customer churn is a problem for many sectors, but it is especially severe in the fiercely competitive and currently widely liberalized mobile telecommunications sector. The average monthly churn count for mobile telecommunications is reportedly 2.2%. In addition to potential costs from lower revenues, losing customers also increases the demand for acquiring new ones.
Classical Review On Stock Prognosis Using Long Short Term Memory

The main plot of this project is to compare different methods that primarily finds the stock prediction. There are so many machine learning and neurocomputing fields to predict the stock values. Stock price forecasting uses machine learning effectively. Various learning methods are available, including Moving Average (MA), K-nearest Neighbors (KNN), LSTM (Long Short Term Memory), and ARIMA. LSTM is a type of ANN and RNN neural networks. Because in deep learning they are capable of storing the data in memory. But out of this Long Short-Term Memory(LSTM) is unique compares with other. Because it is used to create long term memory. LSTM performs better in big datasets. It has more additional space to store longer information and it stores longer period of time. While compare to other techniques they can’t store more data like LSTM does. In LSTM we use visualization method so, it is easy to compare the data and find the accuracy value. we used apple and google company datasets to perform LSTM. And here in the papers which we used as reference they performed on datasets namely- Chinese stock market data, Yahoo finance dataset(900000), BSE stocks Tick data, LM SCG, 4 year data of NASDAQ,100 stock market data of NASDAQ stocks. LSTM method gave us best accurate value so we choose LSTM method from others. The main objective of this paper is to find the accurate value of stock market using machine learning through best method out of all available. so we choose LSTM method.
Heterogenous Public Safety Wireless Networks with mmWave Small Cells: An IAB-based Approach

An efficient public safety communication network seeks minimal response time, optimal coordination between the disaster site and rescue team, with adequate situational awareness. Such a network requires an enduring and reliable architecture. This paper proposes an architecture for supporting emergency communications using an integrated access backhaul (IAB) in the heterogenous networks (HetNets). The macro base station (MBS) is connected to the wired backhaul, while the small cell base stations (SBSs) are wirelessly connected to the MBS using the IAB. The MBS is the IAB donor, while SBSs are the IAB nodes. As the IAB provides an economically viable solution for dense deployment in the wireless communication networks (WCNs), its use in public warning systems will serve as a robust solution during emergencies. Broadcasting the essential information with a wireless backhaul will involve the multi-hop networks to support minimum throughput requirements of the users within the respective small cell. A major benefit of using IAB is the spectral and infrastructure resource reuse for backhaul as well as the access network. An optimal split is essential for the available bandwidth, between the backhaul and access networks, for adequate system performance. Despite IAB being an attractive scheme, its implementation involves certain research challenges which have been stated in this paper.
Classroom Student Action Recognition Based on Dynamic Head

Students' behavior reflects their status and the degree of participation in the classroom. Therefore, it is particularly crucial to use the classroom camera to automatically detect the student's behavior. However, there are situations where students are densely distributed and there are many small objects in the classroom. To solve this problem, the dynamic head was introduced to effectively detect student behavior. Firstly, we collect a large number of videos from real university classrooms for data annotation to form a student behavior dataset and treat student behavior recognition as an object detection task. Secondly, we propose a model called YOLOv5-Dy, which includes a dynamic head. The dynamic head includes scale, space, and task attention mechanisms, which focus on the importance of different features from different perspectives. Experimental results show that the improved model is more adaptable to the student behavior detection task. Finally, we compare our method with state-of-the-art detectors, and it turns out that the proposed approach performs better on the student behavior dataset.
Virtual Reality Based Attention Simulator using EEG Signals

Attention is seen as the capacity of a person to focus on and persevere in a specific endeavor. Children's attention has long been a major worry for academics because it is crucial to learning. As a result, parents and teachers are concerned about how to increase children's attention. Virtual reality has been widely applied in education to enhance students’ motivation with prolonged attention spans. According to the experts, cognitive training combined with immersive VR improves attention. Additionally, they verified that it might lengthen the attention span of young people with behavioral issues. In this paper, we evolved VR surroundings to demonstrate how toddlers behave in attention seeking tasks. We also advanced a new interest - primarily based on taking neurofeedback using non-invasive brainwave signals. During sustained attention duties, scalp electroencephalography (EEG) signals were collected in actual time at periodic intervals of time. The attentional states and stages of the difficulty can be identified using collected EEG neurofeedback signals.
DSEAdd: FPGA based Design Space Exploration for Approximate Adders with Variable Bit-precision

Functional approximation methods have been used to exploit the inherent error tolerance of several applications. Approximate computing reduces the resources utilized at the cost of acceptable accuracy loss. Designers need to follow a systematic approach to arrive at an optimized design configuration based on certain constraints. In this work, we present DSEAdd: an FPGA-based automated design space exploration (DSE) framework targeting variable bit-width approximate adders. Given a certain area, timing or accuracy (ATA) constraint, the approach helps to identify the best adder configuration. We introduce a metric known as Figure of Merit (FOM) to quantify the area, performance and accuracy of the design. We test the DSE framework by running a set of 74 design configurations. We demonstrate the use of FOM as a metric to choose the best adder configuration. We observe that we can obtain an area-optimized design with a 9.7% reduction in resource usage at the cost of only 0.3% accuracy, but with a lower bit precision (8-bit instead of 32-bits). Further, at low bit precisions, a slight compromise in the area (0.35%) can help improve the accuracy dramatically (17.7%). To achieve the best trade-off between accuracy and resources, we propose a configuration with 2 or 3 sub-adders. Lastly, we note that a performance-optimized design is difficult to achieve at higher bit-precision.
ZOCHEN: Compression Using Zero Chain Elimination and Encoding to Improve Endurance of Non-Volatile Memories

Emerging Non-Volatile memories (NVM) offer immense possibilities to be considered as an alternative to DRAM in the main memory. However, adoption of NVMs in the memory hierarchy faces challenges due to their costly writes and poor write endurance. In this connection, developing policies to improve the endurance of NVMs has paramount importance. This paper presents a low overhead scheme based on compression and encoding to improve the lifetime of PCM-based main memory. We propose a compression technique by removing chains of zero-valued data bits that are prevalent in the cache blocks coming to PCM. On top of it, we encode the compressed blocks at finer encoding granularity, utilizing only a few tag bits. The overall procedure, called ZOCHEN improves PCM lifetime by reducing bit-flips in the PCM cells. We get a substantial reduction in bit-flips and improvement in lifetime compared to baseline and state-of-the-art policies.
A Survey of Few-shot Learning-based Compound Fault Diagnosis Methods for Industrial Processes

Once the fault occurs in the industrial processes, the fault maybe always compound faults which take place in different locations, forms, and degrees. How to diagnose the multiple faults with coupling and propagation characteristics, enhance the reliability of the system, and ensure the reliable operation of industrial processes is very important. In this paper, a survey on mechanism and manifestation of symptoms for compound faults is given. Meanwhile, difficulties and research status of compound fault diagnosis methods, especially the few-shot learning-based methods are analyzed and discussed. Moreover, the advantages, disadvantages, and research directions are mentioned.
Calibration of 3-D measurement system using multi-sensor with line structured-illuminated laser

An easy and accurate method of system calibrating the 3-D optical multi-sensor profilometry with line structured-illuminated laser was proposed.This method can efficiently and fast carry out the transformation b etween CCD pixel coordinate and world coordinate while required only one measure ment for the special model.Furthermore,it avoids the multifarious survey of the system parameters.As proved by experiments,the present method can effectively restrain the influence of speckle to the laser triangular measurement system and obtain calibration accuracy of about 0.05mm within 100mm depth range.
Feedback for Faculty on Student’s Asynchronous Learning Based on Classification using Topic Modelling

The emphasis on using virtual and, Information and Communication Tools (ICT) is increasing in higher educational institutions to enhance the general education standards for 21st century students. As a part of this, blended learning is emerging as a significant way for course delivery, especially after the hard times of COVID-19. One of the challenges of blended learning is inadequate information for faculty to understand how each student in their class has engaged with the content of the asynchronous video. Reading, assessing, and grading the long and reflective assessments is also tiresome. Therefore, in this study, we aim to provide feedback for faculty by classifying students into different levels based on the reflective answers’ content after the asynchronous learning mode. The site and context of the study are one of the modules named "Sustainability in Engineering" in the course "Engineering Exploration," offered to first-year undergraduate students. We adopted the qualitative text mining approach called Latent Dirichlet Allocation (LDA) widely used topic modeling technique to understand if the topics discussed by students align with the asynchronous content. The results of the study show that 12% of students have spoken only on sustainability, 25% of students have spoken about only engineering design, 68% of students have tried to establish a relationship between the sustainability and engineering design concept, and 20% of students spoke none of the above three topics.
Stability of couples in a quantum dating market

The statistical behavior of a quantum dating model is studied in order to characterize the stability properties of the model. The strategies applied by the players cause changes on the quantum state of the whole system that are reflected by the entropy of the system. Maxima and minima entropy points are used to find characteristic strategies that lead to stable and unstable states. Keywords: . 2000 Mathematics Subject Classification:
A Stock Prediction Method Based on Fake Information Identification and Machine Learning

In this paper, we study a stock prediction method based on false information identification and machine learning.Firstly, a web crawler and the Tushare data interface tool are used. The textual data and stock price data required for the study are obtained. Next, the data is pre-processed such as tokenizing, stemming, etc. Next, the paper uses bag-of-words, POS tagging and word2vec methods for feature selection. The performance of various classifiers was compared, and the better performing logistic regression classifier was chosen to calculate the truth probability score and determine the true/false. Finally, two stock price prediction models, LSTM and GRU, are written and their prediction results are compared in real stock data. The prediction results of GUR were found to be more accurate and to fit better over time.
Performance Analysis of Machine Learning Algorithms to Predict Cardiovascular Disease

Globally the rate of heart disease has increased drastically due to unhealthy eating habits and reduced physical activities. It has become one of the significant causes of death worldwide. As per the reports of the world health organization(WHO), 31% of all deaths worldwide are caused by cardiovascular diseases. This demands the development of a system capable of early detection of cardiovascular diseases at an affordable cost. With this as the objective, multiple machine learning algorithms have been selected to evaluate their performance in the early detection of cardiovascular diseases. This work utilizes available data sets of an individual’s vital parameters, demographic data, and exercise parameters for predicting cardiovascular diseases. An extensive evaluation is performed to identify the best-suited supervised machine learning classifier that could predict cardiovascular diseases using the available datasets. This research work details the nine different classification algorithms utilized for this analysis. For each algorithm, the F1-score, precision, recall, accuracy, and Area Under the Receiver Operating Characteristics (AUROC) values for each model have been determined and compared with the rest of the algorithms. The results show that random forest and gradient boosting models outperform others and demonstrate an F1-Score of 0.88 and an AUROC value of 0.92, respectively. This showcases that doctors could utilize this technique for the early identification of cardiovascular diseases. This will provide the opportunity to offer adequate medical treatments early, thus saving lives.
Efficient Real-Time Localization in Prior Indoor Maps Using Semantic SLAM

In this paper, a method for real-time global localization (registration) of an agent (robot or visually impaired person) in the presence of an a priori indoor map (e.g., an image of a floor plan) is presented. In the proposed algorithm, the SLAM map created by the agent equipped with RGB-D and IMU sensors is used in conjunction with an a priori architectural floor plan to find the global location of the agent. This involves extraction and vectorization of the semantic object locations from the a priori map image, implementation of real-time semantic SLAM using onboard sensors on the agent, and the use of a particle filter based optimization method for global localization. The proposed algorithm is applied in an indoor environment and localization results are presented showing the effectiveness of the approach.
Artificial Intelligence Deployment And The Potency Of Hrm Functions

The overall objective of the current study is to investigate the causality between the application of AI and the efficiency of human resource management (HRM) activities. The HRM activities covered by this research are talent acquisition, HR analytics, and recruitment and selection. This study employed a quantitative research design. Managers and staff working for companies in India’s three main cities, New Delhi, Mumbai, and Kolkata, completed a questionnaire with 30 questions. Data were gathered through the use of random sampling. The 150 participants’ data were analyzed by the study using SPSS software. This study’s objective is to investigate the causality between the application of AI and the efficiency of HRM activities. The HRM activities covered by this research are talent acquisition, HR analytics, and recruitment and selection. This study employed a quantitative research design. Data were gathered through the use of random sampling. The 150 participants’ data were analyzed by the study using SPSS software. The outcome of the analysis depicts that there exist a strong correlation between few HRM functions and AI implementation.
Routability-aware Placement Guidance Generation for Mixed-size Designs

Placement is a critical step in a modern physical design flow, and the routability of the placement result is a major issue that must be taken into account. In this work, we explore the possibility of using placement guidance to mitigate routing congestion and reduce design rule violations for mixed-size designs. By extracting the underlying knowledge of a mixed-size design using a graph neural network, we generate an embedding for each standard cell. Based on the embeddings, we cluster standard cells into groups and create the placement guidance. By adding the placement guidance to a commercial place-and-route tool, the tool will strive to avoid the fragmentation of standard cells with dense connections in the placement stage. Experimental results show that our placement guidance generation methodology helps the commercial tool reduce 26% routing overflow and 65% design rule violations for the test cases.
An Efficient Scalogram Generator for Partial Shading Analysis of Photovoltaic Array

Photovoltaic (PV) systems are negatively affected by many troubles, such as conductor faults, broken glass, cell aging, etc. Among them, partial shading conditions (PSC) not only reduce the output power of the PV systems but also cause hot spots on the surface of PV panels. This paper presents a Convolutional Neural Network-based method for PSC detection and classification. The highlights of this proposal are a scalogram extracted from the I-V characteristics of the PV array and a preprocessing phase implemented by a periodic function-based filter and the wavelet transform. The best accuracy of the proposed model is 98.41% through 819 test images after 50 training epochs, which proves its effectiveness. The future improvement of this work is also discussed in the conclusions.
Classification And Detection Of Skin Cancer Using Deep Learning Methods

The types of Skin Diseases are almost universal around the world because they are caused by genetic and environmental factors. Skin cancer has a very high mortality rate but is usually curable with minor surgical intervention [1]. Diagnosis of melanoma begins with clinical screening followed by dermatoscopy and histopathological examination. Melanoma skin cancer is highly curable if treated early buy detection is hard, the medical procedures are too long, and the doctor’s diagnosis cannot be 100% accurate. High speed cameras have 60-80% high accuracy stated by International Skin given Imaging Collaboration (ISIC). The aim of this project is to determine an accurate prediction of skin cancer and classify skin cancer as malignant or non-malignant. A quick and accurate diagnosis can be of great benefit to doctors and patients. In many areas of medicine, the performance of recent models based on deep learning approaches or even exceeds that of human experts. In this study, to help of dermatologists improve the efficiency of skin cancer analysis, we will use Convolutional Neural Networks (CNN) with Convnet model to complete the recognition of skin lesions images. This project aims to shorten the limitations by providing the predictive model using CNN to classify 9 types of skin cancer lesions from outlier lesion images. Proposal models are trained and evaluated using international standard dataset.
CNN-MFCC Model for Speaker Recognition using Emotive Speech

Finding the appropriate speaker using voice recognition is called "speaker recognition. " Emotive Environment Speaker Recognition (EESR) identifies speakers using distinct emotional speech. A real-life situation that becomes a requirement for many applications is speaker recognition, which utilizes various moods. If there is no emotion in the conversation, speaker recognition algorithms work almost flawlessly. This work aims to improve the accuracy of text-dependent and emotional speaker recognition system in emotional speech contexts. The proposed method is developed using Mel-Frequency Cepstral Coefficient (MFCC) feature and the classifier considered is Convolutional Neural Networks (CNN) for various emotions. The suggested system's performance is assessed based on emotional datasets from the Kannada Language and Emotional Database (EmoDB). These emotions are present in both datasets: happy, sad, angry, fear, and neutral. Due to the complexity of emotions, speaker recognition in various emotional states is challenging. The proposed system offers an accuracy of 96.2% in the EmoDB and 97.8% in the Kannada dataset. The proposed method provides a high recognition rate for different emotions.