There are several challenges with ICT use in education, notably moral and legal ones. Both educators and learners ought to have a basic awareness of the challenges and issues related to using ICT in the classroom. In respective capacities as teachers, students, or potential teachers, they must be beyond criticism. Incorporating modern technology in education is essential in the digital world, according to an increasing number of studies. Teachers and students have a lot more opportunities to collaborate online since educational programmes incorporate information and communication technology (ICT). However, various obstacles could make teachers hesitant to use ICT in the classroom and hinder them from introducing supplementary materials. Examining the challenges associated with implementing ICT in education can help educators get over them and incorporate the technology into routine instruction. The objective of this chapter is to learn more about how teachers view the obstacles and difficulties that impede them from integrating ICT in the classroom.

Credit card fraud poses a serious threat to financial institutions and their customers; hence, stringent detection protocols are necessary. This study introduces an approach known as Enhanced Learning for Credit Card Fraud Detection (ELCCFD) to enhance the accuracy of credit card fraud detection. To improve the fraud detection process, the proposed method combines the strengths of Convolutional Neural Networks (CNNs), AlexNet architecture, and Gradient Boosting Machines (GBM). The proposed approach begins with cleaning up the credit card data to get useful features, then trains a Convolutional Neural Network (CNN) using AlexNet to figure out complex patterns and representations on its own. This study generates a complete set of features by merging the CNN’s output with features generated using GBM. The final model is trained by using a combination of deep learning and other conventional machine learning techniques to achieve the best results. Experimental findings on benchmark datasets demonstrate the effectiveness of the ELCCFD methodology, achieving an accuracy rate of 98%. This study combines AlexNet with GBM to get a model to capture the complex patterns and is easier to understand with the feature importance analysis. With its strong accuracy and reliability, the proposed methodology offers a strong option to fight credit card fraud, and it shows the potential for actual use in financial systems.

Techniques for machine learning (ML) are advancing at breakneck pace, both in academic world and business world. However, various businesses are at different stages of development when it comes to effectively using ML approaches. As a result, the current state of machine learning usage in businesses are analysed. When compared to bigger businesses, small and medium-sized companies (also known as “SMEs”) have a harder time using machine learning technology, according to research findings. However, ML technologies are becoming more popular in business domains. In order to determine the variables that permit success and the factors that contribute to success, 18 organizations in variety of sectors are considered in this qualitative empirical research. The findings indicate that small and medium-sized enterprises, in particular, struggle to use machine learning technology owing to a lack of knowledge of ML. Nevertheless, this deficiency in resources may be compensated by partners and necessary instruments. Many strategies that might close the gap for SMEs are covered in this study.

The study examines deep learning (ML), a kind of artificial neural system (AI) that enables software developers to start improving prediction before they have completed training. Given the value of data, creating intelligent methods for controlling the already pervasive content systems is a crucial first step to fully independent agents. A number of businesses, including medical diagnosis, discussion of results and processing, science and research, and others, have benefited from machine learning. Patients may freely move about while their pulses are continuously monitored thanks to a number of small IoT devices for vital sign sensors that are already on the market. Although it is still challenging to guarantee very exact findings, the majority of contemporary equipment are capable of achieving up to 94 percent accuracy.It may be challenging to measure glucose concentration and cardiac rhythms, particularly for individuals who are addressed at medical institutions. Intermittent heart rate monitoring is insufficient to guard against sudden changes for vital signs, and conventional hospital cardiac rhythm monitoring methods include tethering patients to wired equipment all the time, restricting their mobility.

Cervical cancer is not new to the research at the same time it has more impact on society to motivate to find better solutions to predict at the earlier stage to avoid the severity of the patient. Cervical cancer has various stages of severity and it will be analyzed using various diagnosis methods. Therefore to identify the severity, this research study will use machine learning approaches to analyze the medical diagnosis inputs. In this study, the cancerous pap smear images are given as input on machine learning algorithms and predicted the severity of decease for better treatment. Therefore this research paper proposes a new framework to classify the cancerous and non-cancerous pap smear images using filtering techniques to improve the better accuracy of the existing research studies.

Smart agriculture using the Internet of Things (IoT) is an Industry 4.0 technology used in rural and urban environments. Industry 4.0 is a new class of the Industrial Revolution that focuses heavily on automation, interconnection, real-time data, and artificial intelligence (AI). With the growing demand for agricultural automation to increase farm output while meeting minimal criteria, smart farming has emerged as one of the fundamental applications of Industry 4.0. We propose an AI-based smart farming protocol since AI approaches are essential for the performance improvement of Industry 4.0 standards. We design Lightweight Clustering Protocol for Industry 4.0 Enabled Precision Agriculture (LCIPA) protocol via clustering and routing algorithms. Smart farming such as Industry 4.0, tackles long-distance communications, energy efficiency, computing efficiency, and assured QoS performance. In the clustering phase, we compute the direct and indirect sensor node parameters to produce the integrated fitness function value. The optimal selection of Cluster Head (CH) and reliable data forwarders is obtained using this novel fitness function. We establish the objective function for clustering and routing to reduce energy consumption and communication overhead while maximizing network performance using an integrated fitness function. The proposed fitness function is used to develop the Bacterial Foraging Algorithm (FBA) for optimum CH selection. A lightweight route discovery approach is based on periodic fitness ratings for inter-cluster and intra-cluster data transfer without specific processing. The simulation results reveal the efficiency of the LCIPA protocol compared to underlying methods.

The newer avid-19 corona virus created havoc for patients with a variety of complications that prompted health practitioners around the world to develop new technologies and treatment plans. Technologies based on Machine Learning (ML) have been a major factor in addressing complex issues and many businesses have been able to develop and adapt to the COVID-19 challenges. The diagnosis of illness can be used with different AI methods to monitor the present havoc. Since Machine Learning (ML) approaches have been commonly used in other domain fields, a great deal of demand is now being made for ML-supported diagnostic systems to screen, monitor, and forecast void-19 spread and find a cure. The article presents an overview of the role of ML to combat the virus so far, especially from the perspective of screening, prognosis, and vaccine. © 2022 Author(s).

The goal to promote human limits is for Artificial Intelligence (AI). It takes a posture on public administrations, represents the increasing availability of regaining clinical data and the rapid creation of intelligent strategies. The need to stress the need to use AI in the fight against the COVID-19 crisis. The paper outlines the main role played by Ai technologies in this unprecedented war and introduces a survey of AI methods used for multiple purposes in the fight against the outbreak of COVID-19. This paper also explains how the body temperature and coughing of the incoming person are assessed and whether the incoming person has not a protective facial mask. Should either of the above tests disqualify the participant, an alarming device invokes the local officials;the entrant may otherwise enter the premises after his/her hand has been sanitized. © 2022 Author(s).

The new technology of computer-oriented services is cloud computing. Cloud computing is becoming increasingly common and continues to expand due to rapid growth in the “cloud computing” sector. It makes major safety issues in big organizations more cost-effective since they pool valuable resources. As a result of the growing demand for more clouds and data, many security problems such as confidentiality, integrity and authentication could emerge in an open setting. These innovations have transformed the shape of conventional IT. This technology provides numerous advantages to IT companies, despite being challenged to meet their maturity. This paper has been debated Data security and privacy concerns are critical for both cloud architecture hardware and applications. The algorithm preserves confidential data across the cloud through encryption and decryption. This algorithm may be used for improving data confidentiality in the results assessment.

This work majorly focused on development of rescue robot systems for children falling into bore well environment. The countries like India there have been several accidents and dead are happening particularly children below 3 years due to bore well environment which is left uncovered. The lifesaving team even army members spend hours to days but 70% of the attempts are unsuccessful to save these little kids because of complicated process. Our robot system mainly involves following process; Approaching the children, Supervision the children, Taking children out. And also our work focused on Safe Handling of the children. Therefore our work deals with concepts of rescue robot systems in bore well environment with semi-automated mechanisms.

A analysis on the multilevel-level Cascaded Hybrid bridge (CHB) inverter as a DSTATCOM was presented in this paper. CHB inverter benefits are low harmonic distortions, reduced switch number and reduced losses. By eliminating the total distortion of harmonics (THD), the DSTATCOM increases the power factor by eliminating an NLDRL. This, too, is the path ahead. The D-Q Reference Frame Principle generates DSTATCOM Reference Currents, while the PI is used to regulate the voltage of the dc condenser. The shunt offset for an 11 kV supply system is the CHB inverter. Finally, the output of the CHB Inverter is investigated in a shifting stage (LSPWM) and a shifting step (PSPWM). The results are obtained through the software kit of Mat laboratory.

The striking property of satire is that it makes it difficult to bridge and bridge the gap between its literal and intended meaning. Identifying sarcastic behavior in the field of online social networks such as Facebook, Twitter, Instagram, surveys, etc. has turned into a fundamental task as they affect social and personal relationships. SARCASM detection is an important processing problem in natural language processing (NLP), which is needed for better understanding to serve as an interface for mutual communication between machines and humans. To understand this is to underline the basic problem behind it - being able to detect the contradiction. For this, a need arose to define contextual understanding and emotion. To accomplish this we need to do two things - gather a stack of target words that display sentiment shifts (sarcastic words) based on context; And with an objective word given an expression, how to naturally identify whether the objective word is used in an exact or sarcastic sense. Collecting information is done by the use of an information retrieval system, for example tweepee. For the latter, some distributed semantic methods are used to convert data into useful information and are then demonstrated using multiple classifier results that is, satire is identified.

In this investigation, the effect of laser power on tensile properties and microstructure of AISI 301 austenitic stainless steel joints were studied. Five joints were fabricated using five different level of laser power varied between 350W and 550W respectively. The other parameters such as, welding speed, energy, focal position were kept constant. Of the five different laser power used, the joints fabricated using 550W yielded superior tensile properties compared to other joints. Fine grain formation, negligible HAZ, prevention of sensitization and the presence of delta ferrite (delta) are the reasons for the superior tensile properties of these joints compared to other joints. (C) 2020 Elsevier Ltd. All rights reserved.

Sentiment Analysis is characterised as contextual manipulation of the text that recognises subjective information in the source material and then extracts it and also helps many companies understand their product or service's social sentiment. Nearly all professional and non-technical companies use sentiment analysis in order to reliably interpret customer input and wishes on how the product or service is fit. Sentimental research is used by top companies in nontechnology and technology. This is more or less organisational and not available to the public. More feedback applications are based on the organisational level and a standard interface that almost any company can use in any field can be very useful to recognise feedback easily using the natural language processing algorithm, without needing a particular programme or application at the organisational level.Customer reviews are digitally gathered and analysed using the NLP algorithm for fast and accurate review. The usage for such purposes can be used by top organisations which have revealed APIs and SDK resources. In this input review method APIs from organisations such as IBM, Parallel points etc can be improved like Twitter, Facebook and WordPress.

Brand touchpoints have a crucial role in enabling organizations to attain competitive advantage over the industry competitors as they are the means to performance perceptions, which further leads to adding value to customer experience and building eternal appreciations. The main objective of the present research is to determine and analyze the relationship of different fashion retail touchpoints and its impact on brand consideration in Bahrain as well as to determine brand consideration's relationship with different demographic characteristics. The proposed research model will examine the relationship between Brand consideration (the dependent variable) and five explanatory independent variables i.e. Corporate advertising, peer-to-peer conversation (WOM), in-store communications, traditional earned media and demographic variable. These touchpoints have proved to play a major role in shaping brand attitudes, specifically brand consideration. Quantitative research methodology is adopted for the study wherein the data is measured using self-administered. The respondents were selected through simple non- random sampling on a sample size of 170. The population targeted for the survey is all females who engage in purchase of fashion brands. The results of the study indicate that three touchpoints out of four have a significant relationship with brand consideration. This study will help in understanding the female fashion industry in Bahrain and allow marketers to benefit from identifying the essential touchpoints to be tapped, in order to gain optimum customer satisfaction and enhance the customer's shopping experience. Few practical guidelines are also provided which will help the marketers to manage their fashion retail touchpoints.

Purpose of the study: The purpose of this research is to find out the relationship between e-CRM and student satisfaction, to find out the relationship between service quality and student satisfaction, to find out the relationship between brand trust and student satisfaction. Methodology: The questionnaire is used as a data-gathering technique with a total sample of 88 respondents, and it is using a purposive sampling technique. The collected data are then analyzed using Microsoft Excel and SPSS 19. Main Findings: The research result shows that e-CRM is positive and has no relationship with student satisfaction, service quality is negative and has a relationship with student satisfaction, and brand trust is negative and has no relationship with student satisfaction. Applications of this study: This study can be used for the management of the Economy and Business Faculty of Budi Luhur University Jakarta as an evaluation guideline. Novelty/Originality of this study: E-CRM and brand trust variables are negative and have no relationship with student satisfaction. Some recommendations for future research are also made.