Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, a conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.

/pdf/hsic-bottleneck-based-distributed-deep-learning-model-for-2dc43gdryl.pdf

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

Taking student attendance is mandatory in an educational organization, and maintaining those attendance plays a vital role. The conventional way of taking student attendance in any institution is time-consuming and challenging, because in the conventional procedure taking attendance/Roll call is performed manually by calling student names as per their roll numbers and marking ‘absent(A)’ or ‘present(P)’ on the attendance/logbook accordingly in every class per day. To improve teaching efficiency/teaching time in classrooms by reducing the time required for Roll call’s, we have proposed a biometric student attendance system based on IoT. The proposed system records students’ attendance using the facial-based biometric system and stores the attendance details on the server through the internet. In this system, the Raspberry pi camera captures the student face images and compares them with the stored images in the database. If the captured image is comparable with the stored image, then the student’s attendance is recorded on the remote server as a present(P) in class; otherwise, attendance is recorded as absent (A). The developed system has been tested for sample classes, and the results proved that the system is simple, cost-effective, and portable for managing students’ attendance. 

Smart Attendance Management System using IoT

Taking student attendance is mandatory in an educational organization, and maintaining those attendance plays a vital role. The conventional way of taking student attendance in any institution is time-consuming and challenging, because in the conventional procedure taking attendance/Roll call is performed manually by calling student names as per their roll numbers and marking ‘absent(A)’ or ‘present(P)’ on the attendance/logbook accordingly in every class per day. To improve teaching efficiency/teaching time in classrooms by reducing the time required for Roll call’s, we have proposed a biometric student attendance system based on IoT. The proposed system records students’ attendance using the facial-based biometric system and stores the attendance details on the server through the internet. In this system, the Raspberry pi camera captures the student face images and compares them with the stored images in the database. If the captured image is comparable with the stored image, then the student’s attendance is recorded on the remote server as a present(P) in class; otherwise, attendance is recorded as absent (A). The developed system has been tested for sample classes, and the results proved that the system is simple, cost-effective, and portable for managing students’ attendance.

Abstract The paper draws on the theory of planned behavior (TPB) to investigate the dual effect of digital communication reinforcement drivers: positive (i.e., interactivity, argument quality, hedonic motivation, and perceived enjoyment online) and negative (i.e., intrusive concerns and privacy concerns) on purchase intention. This paper also examines the mediation effect of perceived usefulness and the moderation effect of habit. Using a time-lag approach, 490 responses were collected from Pakistan’s social media users and then analyzed using SmartPLS v.3.2.8. Findings showed that interactivity, argument quality, and privacy concerns significantly affected purchase intention. Furthermore, perceived usefulness was partially mediated, and habit was discovered to be a significant moderator in liking perceived usefulness with enjoyment online and purchase intention. This paper advances TPB understanding and develops an integrated model for businesses to better understand customer physiology on social commerce platforms through effective contributions in theory and practice. 

https://www.nature.com/articles/s41599-023-01539-8.pdf

The dual effect of digital communication reinforcement drivers on purchase intention in the social commerce environment

The purpose of this paper is to test relevance of the information quality (IQ) framework in understanding quality of advertisements (ads) posted by ordinary consumers.,The main objective of this study is to assess quality ads posted on customer-to-customer (C2C) social commerce platforms from an IQ framework. The authors deployed innovative text mining techniques to generate features from the IQ framework and then used a machine learning (ML) algorithm to classify ads into three categories ‐ high quality, medium quality and low quality.,The results show that not all dimensions of IQ framework are important to assess quality of ads posted on the platforms. Potential buyers on these platforms look for appropriate amount of information, which is objective, concise and complete, to make a potential purchase decision.,As the research focuses on specific product categories, it lacks generalisability. Therefore, it needs to be tested for other product categories.,The paper includes recommendation for C2C marketplaces on how to increase quality of ads posted by consumers on the platform.,This study has focused on the user-generated content posted by ordinary consumers on the C2C commerce platform to sell used goods. Though C2C model has been developed on ads posted on C2C platforms, it can be established for brands as it provides them with an insight into latent dimensions that a consumer shall look for in an ad on social commerce platforms.

Assessing advertisement quality on C2C social commerce platforms: an information quality approach using text mining

A market which provides an innovative way to allow customers to interact with each other called Customer-to-customer (C2C) market. In C2C communications, online communities play an important role in decision making to buy a product. This investigation develops a research model for online communities of Facebook commerce (F-Commerce) in Bangladesh region, which is based on Information Adoption Model (IAM). This study exhibits a model to influences of C2C communication on Bangladeshi consumers’ purchase decision in the online communities of F-Commerce. The proposed model used the Partial Least Squares (PLS) technique to test 120 effective survey data. This survey data has been taken from the Bangladesh Facebook users and strongly involved in product buy-sell at F-Commerce. The analyzed results show that Argument Quality (AQ), Source Credibility (SC) and Tie Strength (TS) positively influence Purchase Decision (PD) through Product Usefulness Evaluation (PUE). In addition, Tie Strength exhibits difference effect on Product Usefulness Evaluation between the contexts of consumers communicating with virtual consumers relationships. Theoretical and executive implications are discussed for constructing our proposed model.

/pdf/modeling-the-role-of-c2c-information-quality-on-purchase-17klqj8igy.pdf

Modeling the Role of C2C Information Quality on Purchase Decision in Facebook

Automated attendance management system will reduce complexity by eliminating plenty of manual processes involved in attendance system and calculating hours attended. This paper presents a simple technique of taking student attendance in the form of an Internet of Things (IoT) based system that records the attendance using fingerprint-based system and stores them securely in a database. We use NodeMCUV3, RFID Module and Fingerprint sensor module in our system. The fingerprint module is responsible for authentication of the students. RFID Module is used to scan the RFID tag and sends data to the central server. By using this information, the system will generate an attendance report which can be accessed for further use.

/pdf/iotsams-a-novel-framework-for-internet-of-things-iot-based-171c6c4516.pdf

IoTSAMS: A Novel Framework for Internet of Things (IoT) Based Smart Attendance Management System

Breast cancer is a prevalent and potentially lifethreatening disease that affects millions of individuals worldwide. Early detection plays a crucial role in improving patient outcomes and increasing the chances of survival. In recent years, machine learning (ML) techniques have gained significant attention in the field of breast cancer detection and diagnosis due to their ability to analyze large and complex datasets, extract meaningful patterns, and facilitate accurate classification. This research focuses on leveraging ML algorithms and models to enhance breast cancer detection and provide more reliable diagnostic results in the real world. Two datasets from Kaggle have been used in this study and Decision tree (DT), Random Forest (RF), Logistic Regression (LR), K-Nearest Classifier (KNN) etc. are applied to identify potential breast cancer cases. On the first dataset, A, the test's accuracy using Logistic Regression, SVM, and Grid SearchCV was 95.614%, however in dataset B, the accuracy of Logistic Regression and Decision Tree increased to 99.270%. The accuracy of Boosting Decision Tree was 99.270% when compared to other algorithms. To defend the performances, various ensemble models are used. To assign the optimal parameters to each classifier, a hyper-parameter tweaking method is used. The experimental study examined the findings of recent studies and discovered that LRBO performed best, with the highest level of accuracy for predicting breast cancer being 95.614%. Keywords—Breast cancer; prediction; machine learning algorithms; ensemble models; voting; stacking

Prediction of Breast Cancer using Traditional and Ensemble Technique: A Machine Learning Approach

Marriage is a momentous event in anyone’s life. It is not just an ordinary relationship. This partnership is supported legally by a civil contract between a man and a woman. According to Islamic values, the Muslim community keeps records of their marriage contract called Nikah-Nama. Currently, the Bangladesh government records the Nikah-Nama in a classic logbook. It is 2022, and this sector has not seen significant upgradation for decades. This system is highly inefficient, prone to impairment, and has fraud loopholes. Corrupt citizens use these loopholes to cheat life partners, cross the marriage limits and conduct an enormous number of offenses. This paper proposes an approach to revolutionize the entire marriage recording system of Bangladesh. It describes step-by-step procedures and the better way to implement a digital Muslim marriage data preservation system. Bleeding-edge technologies are prioritized in this work by keeping web 3.0 in mind to bring innovation to this segment. This paper reflects the minimal approach to the proper digitalization of this issue. The whole idea of this concept is highly scalable. This prototype implementation is ready for any community, group, religion, or Government with an affordable technical infrastructure. The demonstration version is developed according to the conventional marriage rules and guidelines of Bangladesh. Nevertheless, others can also adopt this software ecosystem with minor or further modifications. 

Blockchain-Based Islamic Marriage Certification with the Supremacy of Web 3.0

—Tumors in the brain are masses or clusters of abnormal cells that may spread to other tissues nearby and pose a danger to the patient. The main imaging technique used to determine the extent of brain tumors is magnetic resonance imaging, which ensures an accurate diagnosis. A sizable amount of data for model training and advances in model designs that provide better approximations in a supervised environment likely account for most of the growth in Deep Learning techniques for computer vision applications. Deep learning approaches have shown promising results for increasing the precision of brain tumor identification and classification precision using magnetic resonance imaging (MRI). This study’s purpose is to describe a robust deep-learning model that categorizes brain tumors using MRI images into four classes based on a convolutional neural network (CNN). By removing artefacts, reducing noise, and enhancing the image, unwanted areas of brain tumors are deleted, quality is improved, and the tumor is highlighted. Several CNN architectures, including VGG16, VGG19, MobileNet, MobileNetV2, and InceptionV3, are investigated to compare or get the best model. After getting the best model, a hyper parameter ablation study was performed on that model. Proposed BrainNet-7 achieved the best results with 99.01% test accuracy and 99.21% test and validation accuracy.

Amatul Bushra Akhi

Papers

Modeling the Role of C2C Information Quality on Purchase Decision in Facebook

IoTSAMS: A Novel Framework for Internet of Things (IoT) Based Smart Attendance Management System

Prediction of Breast Cancer using Traditional and Ensemble Technique: A Machine Learning Approach

Blockchain-Based Islamic Marriage Certification with the Supremacy of Web 3.0

BrainNet-7: A CNN Model for Diagnosing Brain Tumors from MRI Images based on an Ablation Study