Alzheimer’s Disease (AD) is the most prevalent cause of dementia, a neurodegenerative\ndisorder primarily affecting the brain. Mild cases of dementia gradually worsen, resulting in a\ndecline in cognitive abilities, including memory, thinking, reasoning, and behavior. Currently,\nthere is no cure for AD; however, early diagnosis of mild cases can help slow the disease\'s\nprogression, improve its management, and enhance overall quality of life. Existing models for\nearly AD diagnosis encounter challenges, such as low accuracy and precision, poor\ngeneralization and overfitting due to data imbalances in datasets. To address these limitations, we\npropose a novel Deep Learning (DL) based model for early AD diagnosis. Our model comprises\nthree main components: image processing and data augmentation, feature extraction based on\nVGG19 (Visual Geometry Group 19), and a Multi-Layer Perceptron (MLP) based classifier\ncategorizing input Magnetic Resonance Imaging (MRI) scans into four classes representing AD\'s\ndevelopmental stages: Very-Mild-Demented, Mild-Demented, Moderate-Demented, and NonDemented. We optimized our model using the ADAM Algorithm and applied it to images selected\nfrom the Open Access Series of Imaging Studies (OASIS) dataset. Our model yielded impressive\nresults, achieving an accuracy of 92.58%, an AUC of 99%, and precision, recall, and F-measure\nvalues of 0.907, 0.894, and 0.890, respectively. Furthermore, we conducted a comparative\nanalysis with several state-of-the-art models, consistently demonstrating that our proposed model\noutperforms them.

Over the past few years, the smart city concept has become a prominent solution to address urban challenges, including economic, social, and environmental issues. These cities strive to offer superior services in healthcare, energy management, transportation, and education by leveraging IoT devices. These devices are pivotal in data collection, analysis, and facilitating citizen interactions. Incorporating IoT applications into our surroundings augments automation, efficiency, and user comfort. To achieve this vision, it is imperative to ensure robust security, privacy, effective deployment, authentication, and resilience against cyber-attacks. Several recent studies have shown promising results regarding the use of blockchain for addressing IoT security concerns, especially in access control and authentication domains. The smart city concept, while beneficial, still faces numerous challenges regarding information security and privacy. With its unique characteristics like auditability, immutability, transparency, and decentralization, blockchain can significantly boost the growth of smart cities and rectify communication issues among IoT devices. This research explores blockchain\'s potential to resolve access control and authentication challenges in IoT when merged with it. It will also offer an in-depth analysis and evaluation of recently proposed solutions in the literature focusing on integrating IoT with blockchain.

In this paper, a new type of set-valued mapping whose range set lies in a family of soft sets is examined. To this effect, we introduce a few fixed point theorems which are generalizations of several significant fixed point results of point-to-point and point-to-set valued mappings in the comparable literature. Some of these particular cases are noted and analyzed. Moreover, nontrivial examples are provided to support the assumptions of our results.

With the daily rapid growth in the number of newly confirmed and suspected COVID-19 cases, COVID-19 extremely threatens public health, countries\' economic and social life, and international relations worldwide. There are different medical methods to detect and diagnose this disease, such as viral nucleic acid screening, using the lower respiratory tract\'s specimens. However, the non-availability of sufficient laboratory screening in the infected counties represents a critical challenge, especially with the fast-spreading of COVID-19. Therefore, alternative diagnostic procedures that depend on Artificial Intelligence (AI) techniques are required in the meantime to fight against this epidemic. This paper focuses on using chest CT to diagnose COVID-19, as an alternative or assistive method to the reverse-transcription polymerase chain reaction (RT-PCR) tests. Motivated by this, this paper introduces a new model based on deep learning for detecting patients infected with COVID-19 using chest CT. In this paper, a new model is proposed to diagnose COVID-19 based on Convolutional Neural Networks (CNN) and Particle Swarm Optimization (PSO) algorithm to classify the CT chest images of patients into infected or not infected categories. In this paper, the CNN\'s network hyper-parameters are optimized by using the PSO algorithm to eliminate the requirement of manual search, thereby enhancing the network performance. This paper\'s used chest radiography dataset is described which leveraged to train COVID-Net and includes more than 16,500 chest radiography images across more than 13,500 patient cases from two open access data repositories. This work\'s experimental results exhibited that the proposed system accuracy ratio of 98.04% is competitive to other models.

In this paper, we present a new approach, named FA-MOLS, based on integrating the firefly algorithm (FA) with mutually orthogonal Latin squares (MOLS) to solve the quadratic assignment problem (QAP). In the proposed FA-MOLS algorithm, the neighbourhood solutions are searched by introducing MOLS as a local search strategy to enhance the FA\'s solution\'s quality. Additionally, an opposition-based learning scheme is provided to enhance the exploration ability and preserve the population diversity. Also, a 2-exchange neighbourhood search strategy is introduced and performed randomly. Accordingly, we eliminate the trapping of fireflies in local optima and improve the convergence speed. A mapping of the largest real values is developed to convert real values into discrete values to cope with the combinatorial nature of the QAP. The performance of the proposed FA-MOLS is validated on different test instances from the literature. Additionally, two real-world QAPs are simulated and investigated involving other office buildings of a single company and hospital departments\' layout. These comprehensive experimental simulations show that FA-MOLS can provide a highly competitive performance compared with several literature algorithms.

In certain environments the system may not fail completely, but undergoes degradation and the system productivity might decrease. Meanwhile at the same time the system may be vulnerable to shocks. A single-unit system prone to degradation and shocks is proposed in this study. The system is expected to have three states viz. normal, degraded and failed. As the system enters degraded state it is said to be partially failed. The degraded state incurs higher degradation than normal state and is more prone to shocks. Inspections are used to determine the state and failure-type of the system. Inspections are pre-determined to be carried out sequentially at time I,I+aI,I+aI+a^2 I,… where 0

Computer science education lags in researching instruments to identify programming misconceptions appropriate for all taught programming languages and all levels of education. Such a tool could monitor phenomena of misconceptions across educational vertical. Identifying commonalities could reduce the amount of work required to identify misconceptions from the beginning for each educational level and develop strategies to effectively address them. \nThe purpose of this study is to investigate the effectiveness of the open-ended programming language-independent test for challenging misconceptions of novice programmers at different educational levels. A 30 item test was developed to identify misconceptions of commonly taught programming concepts and was administered to 412 students. Quantitative means were used to determine the validity and usefulness of the test. Item analysis was performed to determine discrimination ability and difficulty level. \nResults also show that students at different educational levels hold the same misconceptions. Most of the participants’ characteristics did not show statistically significant correlations with results. The most influential are attitude towards programming with its positive effect on correct answers and usage of Scratch with its negative effect on correct answers.\nThe study shows that holding of misconceptions begins with the youngest students. It suggests that teachers should apply this test to tackle misconceptions in the classroom as early as possible. In addition, findings in this study may also improve the methodology of developing multiple choice item techniques to identify misconceptions.

Recent research evidence indicates that the powerful testing tools, even though generate test inputs automatically for coverage measures, but not up to satisfaction. These tools sometimes achieve high structural coverage, which does not guarantee to have high fault detection ability. These findings lead us to a decisive point that code coverage is merely one factor towards effective test data generation. Thus, we discuss our findings and proposed work on MC/DC based test case generation and prioritization techniques. This work aims to generate, minimize, and prioritize MC/DC test cases obtained through concolic testing process. This work presents three technical contributions. The first contribution is to propose a greedy algorithm to increase the number of effective test cases for improving MC/DC scores. The second contribution is to minimize the updated test suite size to have only the optimal number of contributing test cases towards forming MC/DC pairs. The third contribution is to prioritize these test cases by considering both their Contribution Index (CI) values and Fault Exposing Potential (FEP) values. The proposed approach is validated by experimenting on fifteen Java programs and achieved 1.29 times increase in the number of effective test cases that lead to an increase in MC/DC score by 37.82%. We achieve 47.83% reduction rate to minimize the test suite size and finally prioritized the test cases based on their prioritization index values.

In this paper, we have defined a new power sequence in integer-valued polynomial module unlike other power sequences. We have called the sequence as the power generalized k-Horadam sequence in integer-valued polynomial module. By using these sequences, we have rebuilt ElGamal cryptosystem and then we have obtained a new cryptographic method by combining this system rebuilt and symmetric systems. We have made an encryption example with the new cryptographic method. Finally, we have obtained the advantages of the new cryptographic method by comparing with the some of the asymmetric cryptographic systems such as RSA, ElGamal. And we have achieved that the new cryptographic method is more advantageous.