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Authors:Paul; Victer, Paul, Bivek Benoy, Raju, R. Pages: 1 - 19 Abstract: Diabetic retinopathy is one of the leading causes of visual loss and with timely diagnosis, this condition can be prevented. This research proposes a transfer learning-based model that is trained using retinal fundus images of patients whose severity is graded by trained ophthalmologists into five different classifications. The research uses transfer learning based on a pre-trained model that is ResNet 50, thus it is possible to train the model with the limited amount of labeled training data. The model has been trained and its accuracy has been analyzed using different metrics namely accuracy score, loss graph and confusion matrix. Such deep learning models need to be transparent for approval by the regulatory authorities for clinical use. The clinical practitioner also needs to have information about the working of the classification method to make sure that he/she understands the decision making process of the model. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Intelligent Information Technologies (IJIIT), Volume: 19, Issue: 1 (2023) Pages: 1-19 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/IJIIT.329929 Issue No:Vol. 19, No. 1 (2023)
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Authors:Menaouer; Brahami, Islem, Abdallah El Hadj Mohamed, Nada, Matta Pages: 1 - 22 Abstract: In the past decade, Android has become a standard smartphone operating system. The mobile devices running on the Android operating system are particularly interesting to malware developers, as the users often keep personal information on their mobile devices. This paper proposes a deep learning model for mobile malware detection and classification. It is based on SAE for reducing the data dimensionality. Then, a CNN is utilized to detect and classify malware apps in Android devices through binary visualization. Tests were carried out with an original Android application (Drebin-215) dataset consisting of 15,036 applications. The conducted experiments prove that the classification performance achieves high accuracy of about 98.50%. Other performance measures used in the study are precision, recall, and F1-score. Finally, the accuracy and results of these techniques are analyzed by comparing the effectiveness with previous works. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Intelligent Information Technologies (IJIIT), Volume: 19, Issue: 1 (2023) Pages: 1-22 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/IJIIT.329956 Issue No:Vol. 19, No. 1 (2023)
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Authors:Dwivedi; Rajendra Kumar, Kumar, Devesh Pages: 1 - 20 Abstract: Face recognition is an emerging field of research in recent days. With the rise of deep learning, face recognition has become efficient and precise, creating new milestones. The performance, accuracy, and computational time of the existing schemes can be enhanced by devising a new scheme. In this context, a multiclass classification framework for face recognition using residual network (ResNet) and principal component analysis (PCA) schemes of deep learning with Dlib library is proposed in this paper. The proposed framework produces face recognition accuracy of 99.6% and a reduction of computational time with 68.03% using principal component analysis. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Intelligent Information Technologies (IJIIT), Volume: 19, Issue: 1 (2023) Pages: 1-20 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/IJIIT.329957 Issue No:Vol. 19, No. 1 (2023)
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Authors:Chatterjee; Moumita, Kumar, Piyush, Sarkar, Dhrubasish Pages: 1 - 25 Abstract: The coronavirus pandemic has led to a dramatic increase in depression cases worldwide. Several people are utilizing social media to share their depression or suicidal thoughts. Thus, the major goal of the proposed study is to examine Twitter posts by users and identify features that may indicate depressed symptoms among online users. A numerical metric for each user is proposed based on the sentiment value of their tweets, and it is demonstrated that this feature can detect depression with good accuracy by using several machine learning classifiers. The paper proposes a novel method for measuring the mental health index of an individual by combining the sentiment score with multimodal features extracted from his online activities. A real-time curve is generated using this index that can monitor a person's mental health in real time and offer real-time information about his state. The proposed model shows an accuracy of 89% using SVM, and proper feature selection is very essential for obtaining good performance. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Intelligent Information Technologies (IJIIT), Volume: 19, Issue: 1 (2023) Pages: 1-25 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/IJIIT.324600 Issue No:Vol. 19, No. 1 (2023)
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Authors:Marques; Sara, Gonçalves, Rui, Lopes da Costa, Renato, Pereira, Leandro Ferreira, Dias, Alvaro Lopes Pages: 1 - 32 Abstract: In today's competitive and changing business environment, the concern about technologies and intelligent systems has gained more notoriety. However, companies still have many tasks performed by humans; in the medium-term, intelligent systems will become more present in companies and will perform tasks that are currently done by humans much more efficiently. There is a need for companies to adapt and to start thinking about combining human and intelligent systems capabilities. This research was focused specifically in the management accounting profession, as these professionals spend a lot of time collecting and organizing data, doing repetitive tasks that can be easily and quickly accomplished by intelligent systems. This research studied the impact that artificial intelligence, big data, and internet of things can have in this profession. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Intelligent Information Technologies (IJIIT), Volume: 19, Issue: 1 (2023) Pages: 1-32 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/IJIIT.324601 Issue No:Vol. 19, No. 1 (2023)
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Authors:Sujatha T., Wilfred Blessing N. R., Palarimath; Suresh Pages: 1 - 11 Abstract: For a business to succeed, it is very important to make things speaking more to clients than to rivals. It is more critical to decide on the significant feature of an item which influences its competency. In spite of the works that have been done already, a few algorithms gained efficient solution. This paper proposes the CMiner++ Algorithm to assess the competitive relationship among items in unstructured dataset and finding the Top-K competitors of a given item. Definitively, the nature of the outcomes and the versatility of this methodology utilizing numerous datasets from various areas are assessed, and the efficiency and adaptability of this algorithm on various data sets are improved when compared to existing algorithms. In today's busy world, automatic recommendation systems are emerging because people are looking for the products best suited for them. So, it is very important to analyse the behaviour of people, make a review on large and large unstructured data sets, and make the fully automated deep learning system to ensure the accurate outcome. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Intelligent Information Technologies (IJIIT), Volume: 19, Issue: 1 (2023) Pages: 1-11 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/IJIIT.318670 Issue No:Vol. 19, No. 1 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Manmadhan; Sruthy, Kovoor, Binsu C. Pages: 1 - 19 Abstract: Visual question answering (VQA) demands a meticulous and concurrent proficiency in image interpretation and natural language understanding to correctly answer the question about an image. The existing VQA solutions either focus only on improving the joint multi-modal embedding or on the fine-tuning of visual understanding through attention. This research, in contrast to the current trend, investigates the feasibility of an object-assisted language understanding strategy titled semantic object ranking (SOR) framework for VQA. The proposed system refines the natural language question representation with the help of detected visual objects. For multi-CNN image representation, the system employs canonical correlation analysis (CCA). The suggested model is assessed using accuracy and WUPS measures on the DAQUAR dataset. On the DAQUAR dataset, the analytical outcomes reveal that the presented system outperforms the prior state-of-the-art by a significant factor. In addition to the quantitative analysis, proper illustrations are supplied to observe the reasons for performance improvement. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Intelligent Information Technologies (IJIIT), Volume: 19, Issue: 1 (2023) Pages: 1-19 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/IJIIT.318671 Issue No:Vol. 19, No. 1 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Owusu; Ebenezer, Quainoo, Richard, Mensah, Solomon, Appati, Justice Kwame Pages: 1 - 16 Abstract: Lending institutions face key challenges in making accurate predictions of loan defaults. Large sums of money given as loans are defaulted and this causes a substantial loss in business. This study addresses loan default in online peer-to-peer lending activities. Data for the study was obtained from the online lending club on the Kaggle platform. The loan status was chosen as the dependent variable and was classified discretely into “default” and “fully paid” loans. The dataset is preprocessed to eliminate all irrelevant instances. Due to the imbalanced nature of the dataset, the adaptive synthetic (ADASYN) oversampling algorithm is used to balance the data by oversampling the minority class with synthetic data instances. Deep neural network (DNN) is used for prediction. A prediction accuracy of 94.1% is realized and this emerged as the highest score from several trials with variations in batch sizes and epochs. The result of the study clearly shows that the proposed procedure is very promising. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Intelligent Information Technologies (IJIIT), Volume: 19, Issue: 1 (2023) Pages: 1-16 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/IJIIT.318672 Issue No:Vol. 19, No. 1 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Jeya Mala D., Prabha; Ramalakshmi Pages: 1 - 23 Abstract: Software testing plays a vital role during the software development process, as it ensures quality software deployment. Success of software testing depends on the design of effective test cases. To achieve the optimization of generated test cases, the proposed approach combines both global and local searches by means of intelligent agents which exhibit the behaviour of employed bees, onlooker bees, and scout bees in the qABC algorithm. The proposed qABC algorithm has key improvements over the basic artificial bee colony algorithm (ABC) in test optimization by reducing redundancy, filtering of test cases in each iteration and parallel working of the bees. Further, the fitness evaluation of the test cases is done by employing two test adequacy metrics namely path coverage and mutation score. Further, the experimental evaluation of qABC, GA, and the basic ABC based test cases is done using several case study applications. The result shows that qABC outperforms the other algorithms in terms of effectiveness of test cases in revealing the faults with less time and a smaller number of test cases. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Intelligent Information Technologies (IJIIT), Volume: 19, Issue: 1 (2023) Pages: 1-23 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/IJIIT.318673 Issue No:Vol. 19, No. 1 (2023)
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