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Implications of artificial intelligence chatbot models in higher education

10.11591/ijai.v13.i4.pp3808-3813
Hissan Khandakar , Syed Ali Fazal , Kazi Fattah Afnan , Khandakar Kamrul Hasan
Artificial intelligence (AI) is becoming increasingly influential in the academic sector, which is why it is important to explore the ethical dilemmas and concerns surrounding AI chatbots’ design, development, and deployment in educational contexts. Conducted as a thematic literature review, this paper explores existing research on AI in education, AI chatbots, and their integration with higher education to gather evidence and insights that discuss ethical implications and challenges. The study has analyzed several articles on AI chatbots and their integration into academic fields. Significant gaps have been identified, such as the need for more practical implications and the recognition of AI chatbots as a collaborative tool for academic purposes. More AI chatbots should be explicitly trained on data relevant to the learners’ study to examine their usefulness properly. The paper discusses the ethical dilemmas and concerns about the design, development, and deployment of AI chatbots in higher education. It seeks to provide insights and recommendations to ensure the ethical use of AI chatbots in higher education by identifying significant gaps in the existing literature and providing scenarios to expect in the development of AI in education.
Volume: 13
Issue: 4
Page: 3808-3813
Publish at: 2024-12-01

Optimal shortest path selection using an evolutionary algorithm in wireless sensor networks

10.11591/ijece.v14i6.pp6743-6752
Dhamodharan Udaya Suriya Rajkumar , Krishna Prasad Karani , Rajendran Sathiyaraj , Pellakuri Vidyullatha
A wireless sensor network comprises of distributed independent devices, called sensors that monitor the physical conditions of the environment for various applications, such as tracking and observing environmental changes. Sensors have the ability to detect information, process it, and forward it to neighboring sensor nodes. Wireless sensor networks are facing many issues in terms of scalability, which necessitates numerous nodes and network range. The route chosen between the source node and the destination node with the shortest distance determines how well the network performs. In this paper, evolutionary algorithm based shortest path selection provides high end accessibility of path nodes for data transmission among source and destination. It employs the best fitness function methodology, which involves the replication of input, mutation, crossover, and mutation methods, to produce efficient outcomes that align with the best fitness function, thereby determining the shortest path. This is a probabilistic technique that receives input from learning models and provides the best results. The execution results are presented well compared with earlier methodologies in terms of path cost, function values, throughput, packet delivery ratio, and computation time.
Volume: 14
Issue: 6
Page: 6743-6752
Publish at: 2024-12-01

Assessing community pharmacists' thoughts on telepharmacy in Bali province

10.11591/ijphs.v13i4.21536
I Gusti Ayu Rai Widowati , Ni Putu Aryati Suryaningsih , Ida Ayu Manik Partha Sutema , Ketut Agus Adrianta
Telepharmacy describes the use of technology in interactions between pharmacists and patients. Given that telepharmacy is the most recent development in the delivery of digital pharmacy services, the question of how prepared pharmacists are to provide high-quality services frequently comes up and receives attention. The purpose of this study was to determine the viewpoints of pharmacists in telepharmacy services. From February to April 2023, a cross-sectional survey was done using a Google form. The study population included 111 pharmacists in charge of network pharmacies in Bali Province. Their thoughts were assessed using a credible and reliable questionnaire created by the researchers. The questions were split into two sections: a professional demographic items, and a section describing the perspectives, benefits, and challenges of telepharmacy. To get a more detailed or comprehensive response, open-ended questions were added. In general, especially during a pandemic, pharmacists were aware of being involved in remote services (non-face-to-face interaction) and telepharmacy services. This study makes clear that pharmacists' involvement is essential for creating a long-distance relationship between them and their patients. Therefore, pharmacists should learn how crucial communication is to increase patient satisfaction.
Volume: 13
Issue: 4
Page: 2017-2023
Publish at: 2024-12-01

Faculty Assistant Bot-automation of administrative activities using robotic process automation

10.11591/ijece.v14i6.pp6797-6806
Mamidyala Durga Prasad , Balusu Nandini
In this paper, a process workflow for Bot is created using robotic process automation (RPA), associated with artificial intelligence that is used to stream line the administrative tasks and alleviate stress levels of faculty in handling administrative tasks while teaching in higher education. These activities are must for National Academic Audit Council (NAAC) accreditation and All India Survey on Higher Education (AISHE) surveys, which strive to bring quality in teaching higher education by shaping educational policies. Therefore, ensuring the accuracy of this data is paramount to avoid misleading decisions. The Bot automatically gathers student results from the website and saves them into individual files, eliminating the need for human intervention. It is trained to find the related file of student and update his results of upcoming semesters or backlogs. The Bot efficiently manages folders during file saving to enhance retrieval. Additionally, it maintains pertinent student details such as community, caste, and religion, beneficial for educational policy surveys aiming for improved quality. Moreover, it generates and updates reports post each process execution, ensuring data integrity, and can be trained for statistical analysis to predict student outcomes. The UiPath tool is used in the design and testing of the developed Bot
Volume: 14
Issue: 6
Page: 6797-6806
Publish at: 2024-12-01

Eligibility rate of applicant’s LinkedIn account: a naïve bayes classification and visualization

10.11591/ijai.v13.i4.pp4334-4343
Khyrina Airin Fariza Abu Samah , Nurul Athirah Ahmad , Anis Amilah Shari , Hana Fakhira Almarzuki , Zuhri Arafah , Lala Septem Riza , Amir Haikal Abdul Halim
In the digital era, social media platforms like LinkedIn have become famous for recruitment, and recruiters widely use them to find potential employees. The recruitment process is crucial in organizations, as it involves selecting qualified applicants from a diverse pool. However, the screening process and manual recruitment process entail significant time, high costs, and potential bias. Consequently, it may cause recruiting unqualified applicants and may affect the organizations. Thus, this study aims to classify and generate a list of potential job applicants by analyzing seven attributes of their LinkedIn accounts: title, location, skills, education, language, certification, and years of experience. Data are collected from LinkedIn profiles and then undergo data pre-processing. The naive Bayes (NB) algorithm is implemented as the classification algorithm and sets the classification as “eligible” or “ineligible”. The NB model achieved an accuracy testing of 89.8%, indicating good performance in classifying potential job applicants. At the same time, we measure the similarity cosine score to set the mean of the eligibility. The classification results are visualized for the suitable applicants in descending rank, allowing users to choose the applicants’ classification status efficiently. For the system usability, we managed to get 90% from the recruitment expert.
Volume: 13
Issue: 4
Page: 4334-4343
Publish at: 2024-12-01

Evaluation of distributed denial of service attacks detection in software defined networks

10.11591/ijai.v13.i4.pp4488-4498
Neethu S. , H. V. Ravish Aradhya
Software-defined networking (SDN) revolutionizes networking by separating control logic and data forwarding, enhancing security against threats like distributed denial of service (DDoS) attacks. These attacks flood control plane bandwidth, causing SDN network failures. Recent studies emphasize the efficacy of machine learning (ML) and statistical approaches in identifying and mitigating these security risks. However, there has been a lack of focus on employing ensembling techniques, amalgamating diverse ML models, selecting pertinent features, and utilizing oversampling techniques to balance categorical data. Our study evaluates 20 machine-learning models, emphasizing feature engineering and addressing class imbalance using synthetic minority oversampling technique (SMOTE). The results indicate that ensemble methods such as light gradient boosting machine (LGBM) classifier, random forest classifier, XGB classifier, decision tree classifier obtained near-perfect scores (almost 100%) across all metrics, suggesting potential overfitting. Conversely, models like AdaBoost classifier, k-neighbors classifier, and support vector classifier (SVC) exhibited slightly lower (99%) but realistic performance, underscoring the intricacy of accurate prediction in cybersecurity. Simpler models, including logistic regression, linear discriminant analysis, and Gaussian naive Bayes, demonstrated moderate to low accuracy, approximately around 70%. These findings stress the imperative need for a nuanced approach in the selection and fine-tuning of ML models to ensure effective DDoS detection in SDN environments. 
Volume: 13
Issue: 4
Page: 4488-4498
Publish at: 2024-12-01

Enhancement of energy and spectral efficiency for mm-wave based 5G communication network

10.11591/ijece.v14i6.pp6380-6388
Vishakha Gaikwad , Ashwini Naik
5G network is an enhanced communication network, designed to converge the requirements of quality of service (QoS) parameters, data and capacity, by means of signals with high QoS and high-speed data rate. Several state-of-art technologies are involved to obtain the requirement of energy efficient communication system with increased number of users, devices, higher data rate with low latency. This paper presents a system which demonstrates the energy and spectral efficiency achieved for various number of nodes in a specific area. This study addresses the improvement in energy and spectral efficiency when the proposed algorithm is used. The proposed algorithm is a combination of swarm based artificial bee colony (ABC) algorithm with neural network. Experimental results have been carried out to observe the performance of QoS parameters such as bit error rate (BER), throughput, power consumption and mean square error (MSE). The maximum energy efficiency achieved is 34% and Spectral efficiency is 36%.
Volume: 14
Issue: 6
Page: 6380-6388
Publish at: 2024-12-01

Factors influencing the intention to use m-commerce in Malaysian: an extended IS success model

10.11591/ijaas.v13.i4.pp961-973
Moussa Barry , Ahasanul Haque , Muhammad Tahir Jan
The progress of mobile technology has undergone substantial development in recent years, leading to the emergence of new and creative ideas. This paper investigates the factors influencing consumers' intentions to use mobile commerce in Malaysia. The DeLone and McLean updated information system success model served as the basis for this study's proposed model. A convenience sampling method was employed to collect 310 surveys from smartphone owners who conduct mobile commerce activities in Malaysia. A “two-stage structural equation modeling” (SEM) technique assessed the research model and the study's assumptions. The findings revealed that “information quality”, “service quality”, “system quality”, and “trust” significantly influence consumers' “intention to use mobile commerce in Malaysia”. The findings further reveal that “system quality” is the strongest factor influencing the “intention to use mobile commerce in Malaysia”. Therefore, the research outcomes will benefit academicians, researchers, policymakers, and practitioners in the mobile commerce industry in Malaysia. To the best of the authors’ knowledge, this is the first empirical study that expanded the “information system success model” by including “trust” in the context of Malaysian mobile commerce users’ “intentions”. However, further research is recommended to explore the factors influencing consumers' “intention to use mobile commerce in Malaysia”.
Volume: 13
Issue: 4
Page: 961-973
Publish at: 2024-12-01

Implementation of innovative approach for detecting brain tumors in magnetic resonance imaging using NeuroFusionNet model

10.11591/ijece.v14i6.pp6628-6641
Arpitha Kotte , Syed Shabbeer Ahmad
The goal of this study is to create a strong system that can quickly detect and precisely classify brain tumors, which is essential for improving treatment results. The study uses advanced image processing techniques and the NeuroFusionNet deep learning model to accurately segment data from the brain tumor segmentation (BRATS) dataset, presenting a detailed methodology. The objective is to create a high-precision system that surpasses current methods in key performance metrics. NeuroFusionNet demonstrates outstanding accuracy of 99.21%, as well as impressive specificity and sensitivity rates of 99.17% and 99.383%, respectively, exceeding previous benchmarks. The findings emphasize the system's ability to greatly enhance the diagnostic process, enabling early intervention and ultimately improving patient care in brain tumor detection and classification.
Volume: 14
Issue: 6
Page: 6628-6641
Publish at: 2024-12-01

The study of requirements for the workforce of the digital industries using web scraping techniques

10.11591/ijaas.v13.i4.pp864-881
Rachawit Tipsena , Wirapong Chansanam , Kulthida Tuamsuk , Tossapon Boongoen
This study investigates workforce requirements in Thailand's digital industries, focusing on qualification requirements across five industry groups: hardware and smart devices, software and software services, digital service, digital content, and telecommunication. Employing Python-based web scraping from selected job websites during 2022–2023, the data undergoes natural language processing (NLP) for analysis. Within Group 1 (hardware and smart devices), electrical engineers dominate with 92 positions, emphasizing a demand for engineering expertise. Group 2 (software and software services) sees a surge in programmer roles, totaling 244 positions, showcasing a need for robust programming skills. Group 3 (digital service) prioritizes information technology (IT) support, claiming 354 positions, indicating high demand for IT support qualifications. Graphic design leads Group 4 (digital content) with 587 positions, highlighting the need for a workforce in digital content production. In Group 5 (telecommunication), network engineers dominate with 37 positions, signaling a demand for top-tier network engineering skills. Most positions across groups specify a bachelor's degree and often require prior experience, highlighting the industry's preference for both academic and practical qualifications. This study underscores the digital industry's rapid growth and the sustained demand for a qualified workforce, emphasizing the importance of academic credentials and specialized skills for future employment in these sectors.
Volume: 13
Issue: 4
Page: 864-881
Publish at: 2024-12-01

Intervention programme to improve emotional regulation among adolescent with mental disorders: a systematic review

10.11591/ijphs.v13i4.24538
Siti Kholifah , Siti Khuzaimah Ahmad Sharoni , Ah Yusuf
Adolescence is an important period for developing social and emotional habits that are important for mental well-being. This research aimed to know the effectiveness of intervention programs to improve emotional regulation in adolescents with mental disorders. Preparation using the PICO approach, determination of inclusion and exclusion criteria, and a literature review of scientific English articles published between 2016 and 2023 were conducted. Data were obtained from the SCOPUS, Science Direct, SpringerLink, ProQuest, and SAGE databases. Assessment of the title, abstract, and full text of the article with the keywords "Adolescent," "Child," "Teenager," "Emotion regulation," "Emotion exposure," "Intervention," "Mental Disorders," "Randomised Control Trial." The systematic review report was reviewed critically to ensure that all PRISMA steps and items were included. Nine articles met the inclusion criteria. The choice of programs and methods of intervention may vary depending on the specific mental disorders and individual needs of adolescents. Nursing interventions, including cognitive behavioral therapy, mindfulness-based interventions, group therapy, support groups, and school-based programs, are widely used to manage and treat mental health disorders with the aim of improving well-being and recovery. The effectiveness of intervention programs varies, and selection should consider adolescent needs, resources, and cultural contexts. Consulting mental health professionals or organizations is recommended.
Volume: 13
Issue: 4
Page: 1749-1756
Publish at: 2024-12-01

Proactive monitoring and predictive alerts for COVID-19 patient management using internet of things, artificial intelligence, and cloud

10.11591/ijece.v14i6.pp7266-7274
Ennaceur Leila , Soufiene Ben Othman , Hedi Sakli , Mohamed Yahia
The coronavirus disease 2019 (COVID-19) pandemic has sparked changes across various domains, encompassing health, commerce, education, and the economy. Given the widespread impact of COVID-19 across numerous nations, it has strained hospital resources, oxygen reserves, and healthcare personnel. Consequently, there exists an urgent necessity to exploit sophisticated technologies such as artificial intelligence and the internet of things (IoT) to monitor patients effectively. This scholarly article proposes a prototype that integrates IoT and artificial intelligence (IA) for the surveillance of COVID-19 patients within healthcare facilities. Wearable IoT devices, equipped with embedded sensors, autonomously collect vital information like oxygen levels and body temperature. Notably, oxygen saturation and heart rate serve as significant markers in COVID-19 cases. These metrics are discerned through the deep learning capabilities of the TensorFlow library. The prototype aims to augment the intelligence of IoT sensors to identify these crucial signs through a trained model. A meticulously labeled dataset comprising oxygen saturation and heart rate data is amassed. Deep neural networks are deployed to prognosticate the disease's progression. The utilization of these technologies harbors the potential for rapid advancements in healthcare, thereby mitigating risks to human life and fostering more proactive responses to health crises.
Volume: 14
Issue: 6
Page: 7266-7274
Publish at: 2024-12-01

A comprehensive verification of the header format and bandwidth utilization to detect distributed denial of service attack in vehicular ad hoc network

10.11591/ijece.v14i6.pp6538-6550
Arun Singh Kaurav , K. Srinivas
Vehicular ad hoc network (VANET) is a promising technology for controlling traffic on roads. Nowadays, heavy traffic is a major issue, and the presence of attackers exacerbates the situation. The most important challenge in VANET is its security from malicious vehicles. In order to defend against distributed denial of service DDoS attacks, we propose a comprehensive verification header format bandwidth detection (CVHB) in VANET. The behavior of a DDoS attack is unknown for all the other normal nodes in network. The header format of packer contains all the information of nodes that are actively participating in routing. The attacker infection probability measured by 𝑃𝑏 and 𝑃𝑚 or (𝑃𝑏 > 0.9). If both the parameters are high means attacker presence confirm in network. The CVHB scheme checks the packet header format of the attacker node, and only the attacker is one of the nodes whose sequence number is frequently changing. So, CVHB blocks the flooding of unwanted packets that consume the limited bandwidth of a wireless link and identify packets that contain no useful information. To measure the performance of the network, the basic performance metrics that are used are dropping percentage, packet delivery ratio (PDR), throughput and delay. The result of CVHB is showing improvement as compared to multilayer distributed self-organizing maps (MSOM) in VANET.
Volume: 14
Issue: 6
Page: 6538-6550
Publish at: 2024-12-01

Bone fracture classification using convolutional neural network architecture for high-accuracy image classification

10.11591/ijece.v14i6.pp6466-6477
Solikhun Solikhun , Agus Perdana Windarto , Putrama Alkhairi
This research introduces an innovative method for fracture classification using convolutional neural networks (CNN) for high-accuracy image classification. The study addresses the need to improve the subjectivity and limited accuracy of traditional methods. By harnessing the capability of CNNs to autonomously extract hierarchical features from medical images, this research surpasses the limitations of manual interpretation and existing automated systems. The goal is to create a robust CNN-based methodology for precise and reliable fracture classification, potentially revolutionizing current diagnostic practices. The dataset for this research is sourced from Kaggle's public medical image repository, ensuring a diverse range of fracture images. This study highlights CNNs' potential to significantly enhance diagnostic precision, leading to more effective treatments and improved patient care in orthopedics. The novelty lies in the unique application of CNN architecture for fracture classification, an area not extensively explored before. Testing results show a significant improvement in classification accuracy, with the proposed model achieving an accuracy rate of 0.9922 compared to ResNet50's 0.9844. The research suggests that adopting CNN-based systems in medical practice can enhance diagnostic accuracy, optimize treatment plans, and improve patient outcomes.
Volume: 14
Issue: 6
Page: 6466-6477
Publish at: 2024-12-01

Systematic review of artificial intelligence with near-infrared in blueberries

10.11591/ijai.v13.i4.pp3761-3771
Liset Cayhualla Amaro , Sebastian Rau Reyes , María Acuña Meléndez , Christian Ovalle
The fruit quality has a direct impact on how the fruit looks and how tasty the fruit is. The correct use of tools to determine fruit quality is essential to offer the best product for the final consumer. This study has used the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The study objective was elaborate a systematic literature review (SLR) about research of the application of techniques based on artificial intelligence to analyze indicators obtained by near infrared spectroscopy (NIRS) and chemometrics to determine the quality of fruits, including blueberries. The most frequently addressed indicator is the soluble solids concentration (SSC) which was used in several studies with techniques such as support vector machines (SVM) and convolutional neural networks (CNN). According to the results obtained, it is possible to use these techniques to predict blueberry quality indicators. There was an acceptable performance and high accuracy of these models. However, future research could cover other techniques and help to provide better quality control of products in food industries.
Volume: 13
Issue: 4
Page: 3761-3771
Publish at: 2024-12-01
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