IAES Institute of Advanced Engineering and Science

16/07/2025

Perceptions of the generative AI-enabled cognitive offload instruction in English writing
Hui Hong, Poonsri Vate-U-Lan, Chantana Viriyavejakul

Abstract

https://ijere.iaescore.com/index.php/IJERE/article/view/33138

This study examines the students’ perceptions of the generative artificial intelligence (AI)-enabled cognitive offload instruction and its effectiveness in improving their critical thinking skills in writing English essays. This qualitative research collects data from 120 students through focus group discussions and is analyzed by Word Clouds to generate a visual representation of the word frequencies. The findings reveal that generative AI-enabled cognitive offload instruction had: i) an impact on critical thinking and writing skills; ii) effective features of Skywork, ability to generate relevant prompts and provide constructive feedback; iii) use of Skywork in developing stronger arguments; iv) promoting critical examination of different perspectives; v) interactive nature and motivation; vi) enhanced analytical skills; vii) impact on essay structuring and organization; viii) feedback and revision process; and ix) transferability of critical thinking skills. This study concludes that the highest frequency was Skywork, ability, writing, feedback, evidence, skills, thinking, arguments, essays, and peers. Students recommend in-depth explanations for complex topics, advanced tutorials, regular updates, collaboration features, advanced modules, and personalized learning paces to enhance Skyworks’s integration into instruction.

Perceptions of the generative AI-enabled cognitive offload instruction in English writing

16/07/2025

Best practices of effective classroom management strategies supported by digital ICT in higher education
Enaz Mahmoud, Ali Khaled Bawaneh

Abstract

In the field of higher education, incorporating digital information and communication technology (ICT) into classroom management has gained significant attention due to its potential benefits in enhancing students’ engagement and learning. One of the significant challenges in higher education is managing students learning in the classroom effectively. Higher education students cannot gain effectively the targeted learning outcomes by using regular classroom management in teaching higher education subjects away from digital ICT. The purpose of this study is to explore the advantages of using digital ICT tools into classroom management in higher education and to discuss the challenges and considerations that are involved in this process. This study uses the descriptive research methodology that does not include or present students during preparing of the suggested framework, because it suggests a well-designed cohesive framework of preparing steps, best digital ICT software programs, and the most suitable practical techniques for ICT effective classroom management. It describes the best practices of using 10 professional ICT digital software tools for effective integration and provides examples of practical techniques at the higher education level using suggested ways of successful implementation. It finds that using these software programs can lead students to engage in classroom activities actively, increase their academic achievement, and maximize their technology communication skills. It recommends that providing instructors with enough ICT digital professional development programs and the availability of most updated digital infrastructure are crucial factors for effective classroom management.

Best practices of effective classroom management strategies supported by digital ICT in higher education

03/06/2025

Exploring how demographic factors shape perceptions of digital technologies and leadership in Greek elementary schools

In today’s classrooms, digital technology isn’t just a tool—it’s a necessity. But not everyone in education sees it the same way. A recent study from Greece looked into how teachers and principals in elementary schools perceive digital tools and leadership, depending on their age, education, and experience. Unsurprisingly, those with more teaching years or higher education levels tended to have more confident and positive views on technology and school leadership in the digital age.

Researchers surveyed over 500 educators across 215 schools in the Peloponnese region. What they found was telling: older and more experienced staff were more likely to value digital tools and leadership strategies that embrace change, while newer educators didn’t always feel as confident or aligned. Interestingly, factors like gender or the specific subject someone taught didn’t make much difference—what mattered more was their role in the school and how long they’d been in it.

The study makes a clear point: successful digital transformation in schools isn’t just about buying new tech—it’s about understanding people. School leaders need to recognize how different staff members experience change and tailor their support accordingly. When leadership and digital tools meet people where they are, real progress in education becomes possible.

Digital technologies and leadership practices in Greek elementary schools

26/05/2025

Deep learning approaches, platforms, datasets for behaviorbased recognition: a survey
Yunusa Mohammed Jeddah, Aisha Hassan Abdallah Hashim, Othman Omran Khalifa

Abstract

Video surveillance is an extensively used tool due to the high rate of atypical behavior and many cameras that enable video capture and storage. Unfortunately, most of these cameras are operator dependent for stored content analysis. This limitation necessitates the provision of an automatic behavior identification system. This behavior identification can be achieved using unsupervised (generative) computer vision methods. Deep learning makes it possible to model human behavior regardless of where they could be. We attempt to classify current research work to report the ongoing trends in human behavior recognition using deep learning algorithms. This paper reviews various aspects, like the ones associated with machine learning and deep learning models, human activity recognition (HAR), deep learning frameworks/tools, abnormal behavior datasets, and a variety of other current trends in the field of automatic learning. All these are to give the researcher a sense of direction in this area.

Deep learning approaches, platforms, datasets for behaviorbased recognition: a survey

26/05/2025

Internet of things based autonomous robot system architecture for home automation and healthcare services
Bhimunipadu Jestadi Job Karuna Sagar, Garapati Swarna Latha, Sreenivasulu Bolla, Jyotsna Amit Nanajkar, Pattabhirama Mohan Patnala, Praveen Mande, Mukund Ramdas Kharde, Jonnadula Narasimharao

Abstract

The internet of things (IoT) is playing a major role in the development of the health industry by enabling more accessible and affordable virtual and distant patient contacts through applications that are easy to use. The IoT and automated homes are becoming more popular in recent days. A network of connected devices, including hardware, equipment, and technical support, is known as the IoT. Their purpose is to allow data exchange with other systems through the internet. This paper presents, internet of things based autonomous robot system architecture (IoT-ARSA) for home automation and healthcare services. The primary goal of this secure home automation system is to help the elderly and disabled people by allowing them to operate home appliances. Additionally, the system uses a cloud server to predict the health conditions of patients and the elderly people, providing information to a guardian. The patient's health condition is determined using sensors like temperature, pulse, blood pressure, and oxygen level. Ultrasonic sensor and face detection are used for home automation. Each sensor will interact with the Raspberry Pi 4 to record data, which will then be processed and stored in the cloud. From results it is clear that described (IoT-ARSA) for home automation and healthcare services model is very efficient with high accuracy and high security. Health monitoring is achieved with this model continuously with great efficiency.

Internet of things based autonomous robot system architecture for home automation and healthcare services

26/05/2025

Skin cancer disease analysis using classification mechanism based on 3D feature extraction
Ramya Srikanteswara, Ramachandra A. C.

Abstract

Dermoscopic image analysis is essential for effective skin cancer diagnosis and classification. Extensive research work has been carried out on dermoscopic image classification for the early detection of skin cancer. However, most of the research works are concentrated on 2D features. Therefore, a 3D lesion establishment mechanism is presented in this work to generate 3D features from the obtained 3D lesions. The objective of this work is to reconstruct 3D lesion image from 2D lesion images and a multispectral reference IR light image. The 3D lesion establishment is achieved by designing an efficient convolutional neural network (CNN) architecture. Details of CNN design architecture are discussed. After reconstruction of 3D lesions, 2D and 3D features are extracted and classification is performed on the obtained 2D and 3D features. Classification performance is evaluated using the images from PH2 database. The mean classification accuracy using K-nearest neighbors (KNN) classifier based on the 3D lesion establishment using the CNN architecture is 98.70%. The performance results are compared against varied classification methods in terms of accuracy, sensitivity, specificity and are proved to be better.

https://ijeecs.iaescore.com/index.php/IJEECS/article/view/37583

26/05/2025

Design and analysis for robotic arm position for automatic electric vehicle
Mukund Ramdas Kharde, Sayyad Abdul Kalam, Kalyani Teku, Thumu Srinivas Reddy, Gollapalli Veera Satya Srinivas, Pavani Kollamudi, Shaik Baba Fariddin, Gopinati Pranay Kumar

Abstract

Nowadays electric vehicles (EV) utilization is increasing. Because of charging issues, EVs are troubling people at the time of the journey because of the lack of charging stations. Therefore, to overcome these issues, robotic arm position for automatic electric vehicle is introduced in this analysis. This vehicle is operated through solar, so charging issues are overcome. The robotic arm position for automatic electric vehicle is fully automated by 4 infrared radiation (IR) sensors, which are placed in variations, back and other sides with particular speed limit variations, so that accidents can be avoided. The Flux in hand gloves can operate without manual operation while driver is sleeping. This analysis uses Raspberry Pi, python software with machine learning (ML) algorithm (support vector machine). Hence, this robotic arm position for automatic electric vehicle shows better results in terms of charging issues, accident ratio and driver presence.

Design and analysis for robotic arm position for automatic electric vehicle

23/05/2025

Multiplexing is a technique that combines multiple signals using a single channel, increasing the capacity of communication channels and data transmission. This study examines digital logic design and simulation of multiplexer hardware configurations using Xilinx field programmable gate arrays (FPGA) and Xilinx ISE waveform simulator software. The analysis helps designers estimate chip performance, with the novelty of its scalable and programmable architecture, which can be linked to communication protocols.

Field programmable gate array simulation and study on different multiplexer hardware for electronics and communication

23/05/2025

The introduction of e-voting has revolutionized the electoral system, addressing vulnerabilities and privacy issues in traditional systems. Schorr's zero-knowledge identification protocol, which allows voters to confirm their identity without revealing personal information, can improve security and privacy in e-voting, making elections more transparent and trustworthy. This approach can meet the growing demand for safe, fair, and private elections.

Secure e-voting system using Schorr's zero-knowledge identification protocol

23/05/2025

The NIST cybersecurity framework is used in Oxygen Forensic® Detective, a digital forensic software that integrates various analytic tools to aid investigators in analyzing digital evidence. This comprehensive strategy combats cyber threats and enhances the productivity and effectiveness of law enforcement methodologies. The use of analytical tools in criminological inquiries has grown significantly in the digital era.

Power of analytic tools in Oxygen Forensic® Detective based on NIST cybersecurity framework

23/05/2025

A new matrix inversion approach is introduced using a multiple-input multiple-output (MIMO) channel matrix. The matrix is described by linear combinations of channel vectors, and the MIMO output is fed into adaptive filters. The filters minimize the response to match the input, equalizing channel vector impact. This method shows convergence and accuracy in Monte Carlo simulations.

Matrix inversion using multiple-input multiple-output adaptive filtering

21/05/2025

Detecting spam using Harris Hawks optimizer as a feature selection algorithm
Mosleh M. Abualhaj, Ahmad Adel Abu-Shareha, Sumaya Nabil Alkhatib, Qusai Y. Shambour, Adeeb M. Alsaaidah

The Harris Hawks optimization (HHO) was used in this study to enhance spam identification. Only the features with a high influence on spam detection have been selected using the HHO metaheuristic technique. The HHO technique's assessment of the selected features was conducted using the ISCX-URL2016 dataset. The ISCX-URL2016 dataset has 72 features, but the HHO technique reduces that to just 10 features. Extra tree (ET), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques are used to complete the classification assignment. 99.81% accuracy is attained by the ET, 99.60% by XGBoost, and 98.74% by SVM. As we can see, with the ET, XGBoost, and k-nearest neighbor (KNN) techniques, the HHO technique achieves accuracy above 98%. Nonetheless, the ET technique outperforms the XGBoost and KNN techniques. ET outperforms other methods due to its robust ensemble approach, which benefits from the diverse and relevant feature subset selected by HHO. HHO's effective reduction of noisy or redundant features enhances ET's ability to generalize and avoid overfitting, making it a highly efficient combination for spam detection. Thus, it looks promising to combat spam emails by combining the ET technique for classification with the HHO technique for feature selection.

Detecting spam using Harris Hawks optimizer as a feature selection algorithm