درخت حوزه‌های تخصصی

مدیریت دانش و IT

حوزه‌های تخصصی
ادبیات اقتصاد تاریخ تربیت بدنی جغرافیا حقوق روانشناسی زبان شناسی علم اطلاعات و دانش‌شناسی علوم اجتماعی علوم اسلامی علوم انسانی علوم تربیتی علوم سیاسی فلسفه و منطق مدیریت مطالعات زنان هنر و معماری
اخبار مدیریت تئوری های مدیریت گروههای ویژه مدیریت آموزشی مدیریت استراتژیک مدیریت بازرگانی مدیریت دانش و IT مدیریت دولتی مدیریت سازمانی و منابع انسانی مدیریت صنعتی مدیریت مالی – حسابداری نقد و بررسی کتاب
پیش بینی، اجرا و ارزیابی تکنولوژی تکنولوژی و توسعه حقوق مالکیت سرمایه گذاریهای خارجی و انتقال تکنولوژی سیستم های پشتیبانی تصمیم گیری سیستمهای خبره کلیات مدلهای انتقال تکنولوژی مدیریت دانش مدیریت فناوری اطلاعات منطق فازی و هوش مصنوعی
ترتیب بر اساس: جدیدترینپربازدیدترین
فیلترهای جستجو: فیلتری انتخاب نشده است.
نمایش ۲٬۷۶۱ تا ۲٬۷۸۰ مورد از کل ۲٬۸۶۶ مورد.
۲۷۶۱.

Design, Realization and Measurements of Printed Patch Antenna with Circular Slots for UWB and IoT Applications(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۴, Issue ۴, ۲۰۲۲ 57 - 68

کلیدواژه‌ها: IoT Patch Antenna Slots UWB 5G

حوزه‌های تخصصی:
تعداد بازدید : ۱۴۳ تعداد دانلود : ۱۳۴
This paper presents the design, simulation and realization of a patch antenna for IOT applications. The patch antenna consists of a radiating element printed on one face of a dielectric substrate, when the ground plane is placed on the other face. In this work, two techniques are used to design a miniaturized patch antenna: the set-up of slots on the radiating element and the use of defective ground plane. Also, the slot’s radius and Length of inset point effects on the performances of the antenna is illustrated. All the simulated results are performed with FEKO, a solver based on a Moments Method and measurement is made using Vector Network Analyzer Anritsu MS2026C. The propose antenna resonates in three frequency bands 3.91, 4.86 and 5.16GHz for different characteristics such as radiation pattern, gain, return loss, which makes it suitable for many wireless communication applications such as IoT applications.
۲۷۶۲.

F-MIM: Feature-based Masking Iterative Method to Generate the Adversarial Images against the Face Recognition Systems(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Special Issue: EIntelligent and Security for Communication, Computing Application (ISCCA-۲۰۲۲), ۲۰۲۳ 80 - 93

کلیدواژه‌ها: Adversarial attack Black-box attack Dodging attack Face Recognition Feature based attack

حوزه‌های تخصصی:
تعداد بازدید : ۱۴۳ تعداد دانلود : ۸۱
Numerous face recognition systems employ deep learning techniques to identify individuals in public areas such as shopping malls, airports, and other high-security zones. However, adversarial attacks are susceptible to deep learning-based systems. The adversarial attacks are intentionally generated by the attacker to mislead the systems. These attacks are imperceptible to the human eye. In this paper, we proposed a feature-based masking iterative method (F-MIM) to generate the adversarial images. In this method, we utilize the features of the face to misclassify the models. The proposed approach is based on a black-box attack technique where the attacker does not have the information related to target models. In this black box attack strategy, the face landmark points are modified using the binary masking technique. In the proposed method, we have used the momentum iterative method to increase the transferability of existing attacks. The proposed method is generated using the ArcFace face recognition model that is trained on the Labeled Face in the Wild (LFW) dataset and evaluated the performance of different face recognition models namely ArcFace, MobileFace, MobileNet, CosFace and SphereFace under the dodging and impersonate attack. The F-MIM attack is outperformed in comparison to the existing attacks based on Attack Success Rate evaluation metrics and further improves the transferability.
۲۷۶۳.

Comparative Study on Different Machine Learning Algorithms for Neonatal Diabetes Detection(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 5 - 26

کلیدواژه‌ها: Voting Classifiers Meta-Classification Technique Diabetes Risk Prediction Biomedical Clinical Risk Factors Random Forest Logistic regression Gradient Boosting Support Vector Machines

حوزه‌های تخصصی:
تعداد بازدید : ۱۴۴ تعداد دانلود : ۱۱۴
This paper gives a performance analysis of multiple vote classifiers based on meta-classification methods for estimating the risk of diabetes. The study's dataset includes a number of biological and clinical risk variables that can result in the development of diabetes. In the analysis, classifiers like Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks were used. In the study, each classifier was trained and evaluated separately, and the outcomes were compared to those attained using meta-classification methods. Some of the meta-classifiers used in the analysis included Majority Voting, Weighted Majority Voting, and Stacking. The effectiveness of each classifier was evaluated using a number of measures, including accuracy, precision, recall, F1-score, and Area under the Curve (AUC). The results show that meta-classification techniques often outperform solo classifiers in terms of prediction precision. Random Forest and Gradient Boosting, two different classifiers, had the highest accuracy, while Logistic Regression performed the worst. The best performing meta-classifier was stacking, which achieved an accuracy of 84.25%. Weighted Majority Voting came in second (83.86%) and Majority Voting came in third (82.95%).
۲۷۶۴.

Range of Publications for E-Government Services: a Review and Bibliometric Analysis(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۲, ۲۰۲۳ 164 - 182

کلیدواژه‌ها: Government Public e-services bibliometric analysis Network analysis E-government Researchv

حوزه‌های تخصصی:
تعداد بازدید : ۱۴۲ تعداد دانلود : ۱۲۳
With the rapid advancement of information and communication technology (ICT), public administration has adopted the concept of e-government. The academic literature produced many studies in the field of E-government (E-GOV) services, however, there is limited research on such services from the perspective of bibliometric and Network analysis. Therefore, this study aims to present a bibliometric and network analysis of the E-government services literature review obtained from the Scopus database, published between 2011 to 2021. This study uses a five-step method including (1) defining keywords, (2) initializing search outcomes, (3) inclusion and exclusion of some elements of the initial result, (4) compiling initial data statistics, and (5) undertaking analysis of data. The analysis starts by identifying more than 4,880 published articles related to E-government services published between 2011 and 2021. The study findings revealed that the highest number of publications on the E-government Service was in 2019 (102 articles), the top contributing affiliation was Brunel University London, the leading influential country was the USA, and the top contributing Source was Electronic Government. Furthermore, Lu J. occupied the first rank in the list of the most influential authors in terms of citations, while Weerakkody V. occupied the list of the top authors with high publications 20 papers. Likewise, this study showed that there is a collaboration among some authors. This research identified four research clusters by which researchers could be encouraged to widen the research of E-government services in the future. The bibliometric and network analysis of E-government services helps to graphically display the publication's assessment over time and identify domains of current studies' interests and potential directions for further studies. Finally, this research draws a roadmap for future investigation into E-government services.
۲۷۶۵.

Application of Grouped MCDM Technique for Ranking and Selection of Laptops in the Current Scenario of COVID-19(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۳, ۲۰۲۳ 1 - 16

کلیدواژه‌ها: MCDM AHP BWM TOPSIS Laptop Selection

حوزه‌های تخصصی:
تعداد بازدید : ۱۴۰ تعداد دانلود : ۱۳۳
  In the modern technological age, laptops are widely used for doing various day-to-day activities and getting updates all around us. The COVID-19 situation is playing a vital role in a dynamic shift in buyer behavior with multiple personal computing devices at home. Prioritizing and selecting appropriate laptop devices is difficult because there are several options of laptops that are available in the market, and these are equipped with the latest features to do gaming, designing, attending online classes, and performing office and other everyday tasks. There are multiple selection criteria that are complex in nature. MCDM (Multiple Criteria Decision Making) approaches can handle and analyze these complicated criteria. By using MCDM techniques, decision-making can be done to select the top-ranked alternative from among the available alternatives. This paper exhibits a group of two MCDM techniques; Best Worst Method (BWM) and Analytical Hierarchy Process (AHP), which have been used to evaluate relative weights of considered conflicting criteria such as brand, price, storage capacity, RAM, processor, weight, touch screen, Bluetooth, and screen size, and these weights are used in the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for ranking and selecting the best product of laptops.
۲۷۶۶.

Efficient NetB3 for Enhanced Lung Cancer Detection: Histopathological Image Study with Augmentation(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۱, ۲۰۲۴ 98 - 117

کلیدواژه‌ها: Lung cancer Convolutional Neural Network (CNN) Histopathological Images Transfer Learning Lung Cancer Detection

حوزه‌های تخصصی:
تعداد بازدید : ۱۳۹ تعداد دانلود : ۱۳۴
Cancer is an abnormal cell growth that occurs uncontrollably within the human body and has the potential to spread to other organs. One of the primary causes of mortality and morbidity for people is cancer, particularly lung cancer. Lung cancer is one of the non-communicable diseases (NCDs), causing 71% of all deaths globally, and is the second most common cancer diagnosed worldwide. The effectiveness of treatment and the survival rate of cancer patients can be significantly increased by early and exact cancer detection. An important factor in specifying the type of cancer is the histopathological diagnosis. In this study, we present a Simple Convolutional Neural Network (CNN) and EfficientNetB3 architecture that is both straightforward and efficient for accurately classifying lung cancer from medical images. EfficientnetB3 emerged as the best-performing classifier, acquiring a trustworthy level of precision, recall, and F1 score, with a remarkable accuracy of 100%, and superior performance demonstrates EfficientnetB3’s better capacity for an accurate lung cancer detection system. Nonetheless, the accuracy ratings of 85% obtained by Simple CNN also demonstrated useful categorization. CNN models had significantly lower accuracy scores than the EfficientnetB3 model, but these determinations indicate how acceptable the classifiers are for lung cancer detection. The novelty of our research is that less work is done on histopathological images. However, the accuracy of the previous work is not very high. In this research, our model outperformed the previous result. The results are advantageous for developing systems that effectively detect lung cancer and provide crucial information about the classifier’s efficiency.
۲۷۶۷.

اولویت بندی گزینه های سرمایه نامشهود در تدوین مزیت رقابتی از طریق فرایند تحلیل شبکه ای (مطالعه موردی: شرکت های خصوصی در حوزه فناوری اطلاعات)(مقاله علمی وزارت علوم)

نویسنده:
منبع: مدیریت فناوری اطلاعات دوره ۹ پاییز ۱۳۹۶ شماره ۳ 449 - 476

کلیدواژه‌ها: سرمایه اجتماعی سرمایه فکری سرمایه معنوی فرایند تحلیل شبکه ای مزیت رقابتی

حوزه‌های تخصصی:
تعداد بازدید : ۱۳۸ تعداد دانلود : ۱۳۰
هدف از پژوهش حاضر، شناسایی و رتبه بندی مؤثرترین گزینه ها برای خلق مزیت رقابتی پایدار شرکت های فناوری اطلاعات در ایران است. در این پژوهش به کمک خبرگانِ سه شرکت مطرح فناوری اطلاعات در این حوزه، مهم ترین سرمایه های نامشهودی (سرمایه فکری، سرمایه اجتماعی و سرمایه معنوی) که در تدوین مزیت رقابتی برای شرکت های فناوری اطلاعات نقش مهمی دارند، شناسایی و رتبه بندی شدند. با مطالعه ادبیات و ایجاد پنل خبرگی، برای سه گزینه سرمایه فکری، سرمایه اجتماعی و سرمایه معنوی، در مجموع 16 زیرگزینه استخراج گردید. برای مزیت رقابتی نیز پنج معیار قیمت، پشتیبانی، طراحی، تصویر و کیفیت در نظر گرفته شد که رتبه بندی گزینه ها بر اساس این معیارها، از طریق فرایند تحلیل شبکه ای صورت پذیرفت. نتایج فرایند تحلیل شبکه ای نشان داد سرمایه فکری در مقایسه با سرمایه اجتماعی و سرمایه معنوی در خلق مزیت رقابتی در شرکت های فناوری اطلاعات، نقش مهم تری دارد. همچنین در رتبه بندی زیرگزینه ها، سرمایه انسانی بالاترین رتبه را در کسب مزیت رقابتی در این حوزه به دست آورد. عنصر رابطه ای، چشم انداز محوری/ ارزش محوری و سرمایه رابطه ای/ مشتری، در رتبه های بعدی اهمیت در تدوین مزیت رقابتی پایدار قرار گرفتند.
۲۷۶۸.

AI-WSN: Direction of Arrival Estimation Based on Bee Swarm Optimization for Wireless Sensor Networks(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۴, Issue ۴, ۲۰۲۲ 69 - 86

کلیدواژه‌ها: Wireless Sensor Network Direction of arrival Bees Swarm Optimization Energy estimation

حوزه‌های تخصصی:
تعداد بازدید : ۱۳۸ تعداد دانلود : ۱۳۵
An Artificial Intelligence (AI) technique plays the most crucial factor to consider in energy utilization in a wireless sensor network (WSN). AI transforms industrial operations by optimizing the energy consumption in sensor nodes. As a result, it is crucial for improving sensor node location accuracy, particularly in unbalanced or Adhoc environments. Because of this, the purpose of this research is to improve the accuracy of the localization process in locations where sensor nodes encounter barriers or obstacles on a regular basis. The Bees Swarm Optimization (BSO) algorithm is used to segment sensor nodes in order to increase the accuracy of the Direction of Arrival (DoA) estimate between the anchor and unknown node pairs. Even in the presence of unbalanced conditions, the proposed DoA- BSO involving three separate bee colonies can identify plausible anchor nodes as well as segment nodes arranged in clusters. In order to obtain the intended result, the objective function is designed to take into consideration the hops, energy, and transmission distance of the anchor and unknown node pairs, among other factors. The studies are carried out in a large-scale WSN using sensor node pairs in order to determine the precision with which the DoA-BSO can be located. When comparing DoA-BSO to conventional approaches, the findings of the meta-heuristic algorithm show that it improves the accuracy and segmentation of nodes significantly
۲۷۶۹.

Design of A Fuzzy-controlled Energy - Efficient Multicast Scheduler (FEMS) For SDWSN(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۳, Special Issue: Big Data Analytics and Management in Internet of Things, ۲۰۲۱ 111 - 132

کلیدواژه‌ها: Energy Efficiency Fuzzy scheduler Multicasting Software-defined wireless sensor networks urgency

حوزه‌های تخصصی:
تعداد بازدید : ۱۳۹ تعداد دانلود : ۱۱۹
Multicasting is an important operation in software-defined wireless sensor networks (SDWSNs). In this operation, a group of nodes specified by their unique node identification numbers is supposed to receive the same multicast message at the approximately same time, if possible. These nodes are termed as multicast members or multicast destinations. They need not be physically close to one another to form a group. The present article proposes an energy-efficient scheduler exclusively for multicast operation in the SDWSN environment. Based on the advantages provided by underlying network architecture, a router can efficiently schedule multicast packets belonging to various multicast sessions. This promotes greenery in the network and significantly increases the packet delivery ratio. These claims are supported and justified by the experimental results presented in this paper. As far as the authors know, there is no multicast packet scheduler in the literature of wireless sensor networks or WSN. SDWSN is a more advanced version, and no multicast protocol has yet been proposed for these kinds of networks. Therefore, while designing the present fuzzy scheduler, we kept in mind all standard multicast protocols in the WSN environment.
۲۷۷۰.

Net Asset Value (NAV) Prediction using Dense Residual Models(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Special Issue: EIntelligent and Security for Communication, Computing Application (ISCCA-۲۰۲۲), ۲۰۲۳ 111 - 138

کلیدواژه‌ها: Net Asset Value NAV prediction Mutual Funds N-BEATS FLANN LSTM

حوزه‌های تخصصی:
تعداد بازدید : ۱۳۷ تعداد دانلود : ۹۲
Net Asset Value (NAV) has long been a key performance metric for mutual fund investors. Due to the considerable fluctuation in the NAV value, it is risky for investors to make investment decisions. As a result, accurate and reliable NAV forecasts can help investors make better decisions and profit. In this research, we have analysed and compared the NAV prediction performance of our proposed deep learning models, such as N-BEATS and NBSL, with the FLANN model in both univariate and multivariate settings for five Indian mutual funds for forecast periods of 15, 20, 45, 63, 126, and 252 days using RMSE, MAPE, and R2 as evaluation metrics. A large forecast horizon was chosen to assess the model's consistency, reliability, and accuracy. The result reveals that the N-BEATS model outperforms the FLANN and NBSL models in the univariate setting for all datasets and all prediction horizons. In a multivariate setting, the outcome demonstrates that the N-BEATS model outperforms the FLANN model across all datasets and prediction horizons. The result also shows that, as the number of forecast days grew, our suggested models, notably N-BEATS, maintained consistency and attained the highest R2 value throughout the longest forecast duration.
۲۷۷۱.

In-Depth Analysis of Various Artificial Intelligence Techniques in Software Engineering: Experimental Study(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۳, ۲۰۲۳ 162 - 181

کلیدواژه‌ها: Software Engineering Defects Prediction Artificial Intelligence ML ANN DNN CNN

حوزه‌های تخصصی:
تعداد بازدید : ۱۳۶ تعداد دانلود : ۱۳۴
In this paper, we have extended our literature survey with experimental implementation. Analyzing numerous Artificial Intelligence (AI) techniques in software engineering (SE) can help understand the field better; the outcomes will be more effective when used with it. Our manuscript shows various AI-based algorithms that include Machine learning techniques (ML), Artificial Neural Networks (ANN), Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Genetic Algorithms (GA) applications. Software testing using Ant Colony Optimization (ACO) approach, predicting software maintainability with Group Method of Data Handling (GMDH), Probabilistic Neural Network (PNN), and Software production with time series analysis technique. Furthermore, data is the fuel for AI-based model testing and validation techniques. We have also used NASA dataset promise repository in our script. There are various applications of AI in SE, and we have experimentally demonstrated one among them, i.e., software defect prediction using AI-based techniques. Moreover, the expected future trends have also been mentioned; these are some significant contributions to the research
۲۷۷۲.

Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۳, ۲۰۲۳ 99 - 112

کلیدواژه‌ها: feature selection Classification Cloud Computing Metaheuristic algorithm Convolution neural network

حوزه‌های تخصصی:
تعداد بازدید : ۱۳۷ تعداد دانلود : ۱۳۶
Cloud Computing has drastically simplified the management of IT resources by introducing the concept of resource pooling. It has led to a tremendous improvement in infrastructure planning. The major goals of cloud computing include maximization of computing resources with minimization of cost. But the truth is that everything has a price and cloud computing is no different. With Cloud computing there comes a number of security concerns which need to be addressed. Cloud forensics plays a vital role to address the security issues related to cloud computing by identifying, collecting and studying digital evidence in cloud environment. The aim of the research paper is to explore the concept of cloud forensic by applying optimization for feature selection before classification of data on cloud side. The data is classified as malicious and non-malicious using convolutional neural network. The proposed system makes a comparison of models with and without feature selection algorithms before applying the data to CNN. A comparison of different metaheuristics algorithms- Particle Swarm Optimization, Shuffled Frog Leap Optimization and Fire fly algorithm for feature optimization is done based on convergence rate and efficiency.
۲۷۷۳.

Multi- Objective Fuzzy Software Release Problem with learning capacities for fault detection and correction processes(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۳, ۲۰۲۳ 17 - 30

کلیدواژه‌ها: Software Reliability Software Reliability Growth Models Fuzzy Release Time Problem Software Development Life Cycle

حوزه‌های تخصصی:
تعداد بازدید : ۱۳۴ تعداد دانلود : ۹۳
Without utilization of computer and its related technology, modern day’s life cannot be headway. It has also transformed into an incredibly troublesome task. The genuine challenges included are shorter life cycles, cost effective and higher software quality goals. Despite these challenges the software developers have started to give cautious thought on to the procedure to develop software, testing and reliability investigation of software and to reinforce the method. Developer most fundamental decisions related to the perfect release time of Software. Software development method incorporates a piece of vulnerabilities and ambiguities. We have proposed a multi objective software release time issue under fuzzy environment using a software reliability growth model to overcome such vulnerabilities and ambiguities. Further we have discussed the fuzzy environment framework to deal with the issue. Considering model and issue, we can especially address the issue of when to release software under these conditions. Results are illustrated numerically.
۲۷۷۴.

The Pandemic Benefits Reaped by Online Teaching Platforms: A Case study of Whitehat Junior(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۳, ۲۰۲۳ 69 - 84

کلیدواژه‌ها: pandemic Online Education teaching Platforms parents Perspectiv COVID -19

حوزه‌های تخصصی:
تعداد بازدید : ۱۳۱ تعداد دانلود : ۱۲۹
Pandemic has brought all together a new environment of working and compelled all the off line educational institutions to become online educational platforms and strengthen their online resources. We need to understand online platforms as universities, institutes, schools, colleges or any educational institute which are working online and providing degrees, certificates, diplomas for several courses and programs. In different researches related to online education and Covid -19, investigations addressed student’s perspective or teachers perspective. Literature review has showed the gap in exploring the turnaround strategies inspired by the parent’s perspective for online education especially with respect to young children (Age group 8 to 12 years). Apart from literature review and analysis of secondary data from websites and search engines, qualitative research was undertaken to know about parent’s views in general about the online platforms and particularly about WHJ (White Hat Junior). The focused group discussion and the indepth interviews revealed very useful information with regard to Online educational platforms and especially WHJ in relation to Covid -19 times. Findings relate to awareness, acceptability, perception change, costs, safety issues, etc. It has brought out elaborately in this case based research, how parents expectation may impact the turnaround strategies of their wards’ online educational platforms. In different researches related to online education and Covid -19, investigations addressed student’s perspective or teacher’s perspective.
۲۷۷۵.

Cucumber Leaf Disease Detection and Classification Using a Deep Convolutional Neural Network(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Special Issue: EIntelligent and Security for Communication, Computing Application (ISCCA-۲۰۲۲), ۲۰۲۳ 94 - 110

کلیدواژه‌ها: DCNNs (Deep Convolution Neural Network) CNNs (Convolution Neural Network) Classification

حوزه‌های تخصصی:
تعداد بازدید : ۱۳۰ تعداد دانلود : ۸۱
Due to obstruction in photosynthesis, the leaves of the plants get affected by the disease. Powdery mildew is the main disease in cucumber plants which generally occurs in the middle and late stages. Cucumber plant leaves are affected by various diseases, such as powdery mildew, downy mildew and Alternaria leaf spot, which ultimately affect the photosynthesis process; that’s why it is necessary to detect diseases at the right time to prevent the loss of plants. This paper aims to identify and classify diseases of cucumber leaves at the right time using a deep convolutional neural network (DCNN). In this work, the Deep-CNN model based on disease classification is used to enhance the performance of the ResNet50 model. The proposed model generates the most accurate results for cucumber disease detection using data enhancement based on a different data set. The data augmentation method plays an important role in enhancing the characteristics of cucumber leaves. Due to the requirements of the large number of parameters and the expensive computations required to modify standard CNNs, the pytorch library was used in this work which provides a wide range of deep learning algorithms. To assess the model accuracy large quantity of four types of healthy and diseased leaves and specific parameters such as batch size and epochs were compared with various machine learning algorithms such as support vector machine method, self-organizing map, convolutional neural network and proposed method in which the proposed DCNN model gave better results.
۲۷۷۶.

Networking to learn by learning to network: Social networking among students(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۶, Issue ۳, ۲۰۲۴ 92 - 114

کلیدواژه‌ها: Social networking Networked Learning Higher education Personal Learning System

حوزه‌های تخصصی:
تعداد بازدید : ۱۲۹ تعداد دانلود : ۹۷
The positive effect of social networking, particularly social networking sites (SNSs), on improving the process of learning has been acknowledged by many recent types of research. The relationship between features and characteristics of SNSs and the development of students' social networking was of interest to past researchers. As social networking is primarily perceived as intelligent thought and action in both real and virtual environments, there seems to be a need for a qualitative exploration of the influential factors of students' social networking. The study has been conducted using the case study method to look at the identified factors retrieved from previous research. A semi-structured in-depth interview was used to investigate the viewpoints and experiences of socially proactive and successful students at Iranian universities. Findings explain students' social networking due to three factors categorized as central, causal, and contextual. The personal learning system has a critical position among the various factors affecting students' social networking. Therefore, despite the facilitating role of social networking in promoting the learning process, students' social networking would be useless without utilizing a personal learning system. We can see a dynamic and interactive cycle of learning and social networking in the university context. The research has been founded on critical consideration of previously studied factors affecting social networking that were mainly limited to online technologies according to qualitative exploration. As a result of this research, different learning and social networking levels regarding diverse meaning, function, and complexity were identified.
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Analyzing Hybrid C4.5 Algorithm for Sentiment Extraction over Lexical and Semantic Interpretation(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Special Issue: EIntelligent and Security for Communication, Computing Application (ISCCA-۲۰۲۲), ۲۰۲۳ 57 - 79

کلیدواژه‌ها: Hybrid C4.5 Lexical Analysis Machine Learning Semantic Analysis Sentiment Analysis Social Media Data

حوزه‌های تخصصی:
تعداد بازدید : ۱۲۷ تعداد دانلود : ۸۷
Internet-based social channels have turned into an important information repository for many people to get an idea about current trends and events happening around the world. As a result of Abundance of raw information on these social media platforms, it has become a crucial platform for businesses and individuals to make decisions based on social media analytics. The ever-expanding volume of online data available on the global network necessitates the use of specialized techniques and methods to effectively analyse and utilize this vast amount of information. This study's objective is to comprehend the textual information at the Lexical and Semantic level and to extract sentiments from this information in the most accurate way possible. To achieve this, the paper proposes to cluster semantically related words by evaluating their lexical similarity with respect to feature and sequence vectors. The proposed method utilizes Natural Language Processing, semantic and lexical clustering and hybrid C4.5 algorithm to extract six subcategories of emotions over three classes of sentiments based on word-based analysis of text. The proposed approach has yielded superior results with seven existing approaches in terms of parametric values, with an accuracy of 0.96, precision of 0.92, sensitivity of 0.94, and an f1-score of 0.92.
۲۷۷۸.

Analysis of Diabetes disease using Machine Learning Techniques: A Review(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۴, ۲۰۲۳ 139 - 159

کلیدواژه‌ها: Machine Learning diabetes Classifiers Prediction Classification

حوزه‌های تخصصی:
تعداد بازدید : ۱۲۷ تعداد دانلود : ۱۱۲
Diabetes is a type of metabolic disorder with a high level of blood glucose. Due to the high blood sugar, the risk of heart-related diseases like heart attack and stroke got increased. The number of diabetic patients worldwide has increased significantly, and it is considered to be a major life-threatening disease worldwide. The diabetic disease cannot be cured but it can be controlled and managed by timely detection. Artificial Intelligence (AI) with Machine Learning (ML) empowers automatic early diabetes detection which is found to be much better than a manual method of diagnosis. At present, there are many research papers available on diabetes detection using ML techniques. This article aims to outline most of the literature related to ML techniques applied for diabetes prediction and summarize the related challenges. It also talks about the conclusions of the existing model and the benefits of the AI model. After a thorough screening method, 74 articles from the Scopus and Web of Science databases are selected for this study. This review article presents a clear outlook of diabetes detection which helps the researchers work in the area of automated diabetes prediction.
۲۷۷۹.

Impact of Review, Reviewer and Hotel Characteristics on Ewom Helpfulness: An Empirical Study(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۵, Issue ۳, ۲۰۲۳ 31 - 46

کلیدواژه‌ها: eWOM helpfulness review parameters reviewer parameters hotel parameters Econometric Modelling

حوزه‌های تخصصی:
تعداد بازدید : ۱۲۸ تعداد دانلود : ۲۳۵
Electronic word of mouth (eWOM) has been gaining popularity pertaining to its numerous benefits and ability to be applied in various fields. It helps consumers in making informed decisions and aids service providers in delivering an enhanced service or product. Despite all these benefits, dealing with the huge amounts of eWOM is a consistent problem. eWOM helpfulness comes handy in order to address this issue. In this study, we utilize 16699 hotels related eWOM written by 1099 reviewers which are collected from TripAdvisor.com. Our main objective is to analyze which factors impact eWOM helpfulness and how. For this purpose, eight unique variables belonging to three different categories are selected (eWOM length, eWOM subjectivity, eWOM polarity, eWOM readability, eWOM recency, hotel rating, reviewer badge and reviewer helpfulness) and are analyzed using econometric modelling. Our findings show that hotel rating as well as reviewer badge and helpfulness enjoy a positive significant relationship with eWOM helpfulness. It also suggests that eWOM length, readability and subjectivity positively influences eWOM helpfulness though eWOM polarity and recency are found to have an inverse relationship with the helpfulness of eWOM. Thus, our study reports that review, hotel and reviewer characteristics impact eWOM helpfulness in different ways. This study is summarized with the discussion of theoretical and practical implications.
۲۷۸۰.

Towards Supporting Exploratory Search over the Arabic Web Content: The Case of ArabXplore(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۲, Issue ۴, ۲۰۲۰ 160 - 179

کلیدواژه‌ها: Exploratory Search Arabic Wikipedia PageRank Entity Ranking

حوزه‌های تخصصی:
تعداد بازدید : ۱۲۷ تعداد دانلود : ۱۰۴
Due to the huge amount of data published on the Web, the Web search process has become more difficult, and it is sometimes hard to get the expected results, especially when the users are less certain about their information needs. Several efforts have been proposed to support exploratory search on the web by using query expansion, faceted search, or supplementary information extracted from external knowledge resources. However, these solutions are not well explored for the general web search in an open-domain setting. In addition, they mostly focus on supporting search in content expressed in English and Latin based languages. In this research, we propose a fully automated approach that aims to support exploratory search over the Arabic web content. It exploits the Arabic version of Wikipedia to extract complementary information that supports visual representation and deeper exploration of the search engine's results. Key Wikipedia entities are extracted from the text snippets produced by the search engine in response to the user's query. Entities are then filtered and ranked by using a novel ranking algorithm that extends the conventional PageRank algorithm. Finally, a graph is built and presented to the user to visually represent highly ranked topics and their relationships. The proposed approach was realized by developing ArabXplore, a system that integrates with the web browser to support the web search process by executing our approach in query time. It was assessed over a dataset of 100 Arabic search queries covering different domains, and results were assessed and rated by human subjects. The underlying ranking algorithm was also compared with the conventional PageRank.

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