As e-commerce has grown gradually online item assessments have emerged as a key source of consumer data. That stated, there are problems with the consistency and fictitiousness of the evaluations because there are numerous fake or fraudulent ones. These misleading assessments are generated during the investigation in an attempt to mislead customers about the nature of a real advantage, compromising their ability to make a predetermined decision and damaging the reputations of businesses. A cutting-edge interrogation department revealed that quantum machine learning (QML) could manage a huge amount of machine-trained data and could convey almost emotional choices in the context of inaccurate checks. It is truly beneficial in obtaining reviews for things that are incorrect. Opinion, generating trends, interpersonal relationships, and assessing fatigue is merely a few of the attributes. Tests conducted utilizing the Amazon fraudulent review. The dataset demonstrates that QML tactics outperform conventional knowledge acquisition procedures in errands, including the place of fraudulent reviews. The delicacy and tolerance of incorrect review distinguishing evidence can be significantly advanced, although QML is still in its early stages of development. Both our proposed model and model pass rigorous conventional machine learning algorithms testing with a remarkable level of accuracy. An article introduces a unique approach to fraudulent review detection and brings in the QNN algorithm as a solution. A deep learning model, such as this one, has an 86% accuracy rate in quantum computer implementation, which is an impressive level of innovation, especially if it comes with successful results. Involvement in these cutting-edge technologies promises significant benefits in battling the problem of false identities on the Web. In our case, the experimental results demonstrate that our QNN algorithm, which can accurately identify fake reviews, will become a key weapon for suppressing various forms of fraudulence on emerging digital technology platforms.

Deep learning and computer vision algorithms will be applied to find the breed of the dog from an image. The goal is to have the user submit an image of a dog, and the model will choose one of the 120 breeds stated in the dataset to determine the dog’s breed. The proposed work uses various deep learning algorithms like Xception, VGG19, NASNetMobile, EfficientNetV2M, ResNet152V2, Hybrid of Inception &Xception, and Hybrid of EfficientNetV2M, NASNetMobile, Inception & Exception to predict dog breeds. ResNet101, ResNet50, InceptionResNetV2, and Inception-v3 on the Stanford Dogs Standard Datasetswere used in the existing system. They used a transfer learning algorithm with data augmentation to increase their accuracy. They achieved a validation accuracy score of 71.63% for ResNet101, 63.78% for ResNet50, 40.72% for InceptionResNetV2, and 34.84% for InceptionV3. This paper compares the proposed algorithms with existing ones like ResNet101, ResNet50, InceptionResNetV2, and InceptionV3. In the existing system, ResNet101 gave the highest accuracy of 71.63%. The proposed algorithms give a validation accuracy score of 91.9% for Xception, 55% for VGG19, 83.47% for NASNetMobile, 89.05% for EfficientNetV2M, 87.38% for ResNet152V2, 92.4% for Hybrid of Inception-v3 &Xception, and 89.00% for Hybrid of EfficientNetV2M, NASNetMobile, Inception & Exception. Among these algorithms, the Hybrid of Inception-v3 &Xception gives the highest accuracy of 92.4%.

Objectives: This paper proposes three iterative methods of order three, six and seven respectively for solving non-linear equations using the modified homotopy perturbation technique coupled with system of equations. This paper also discusses the analysis of convergence of the proposed iterative methods. Results: Several numerical examples are presented to illustrate and validation of the proposed methods. Implementation of the proposed methods in Maple is discussed with sample computations.

This research explores the intricate dynamics of job displacement resulting from artificial intelligence (AI) using a sophisticated non-linear dynamical system modeled through the Lotka-Volterra equations, commonly employed in ecology to elucidate predator–prey dynamics. In this study, we interpret human jobs as the “prey” and AI technology as the “predator,” identifying two equilibrium points: E₁ ≡ (0, 0) signifies a state with no jobs and no AI technology, while E₂ ≡ (s/β, r/α) denotes a balanced coexistence where job growth and AI development are in equilibrium. Qualitative analysis reveals four regions characterized by different trends in job expansion and AI development, with Region IV indicating a co-evolutionary phase marked by positive feedback. Stability analysis demonstrates that while E₁ remains consistently unstable, E₂ remains stable, providing valuable insights into system dynamics. Scenarios presented suggest a promising future where balanced growth fosters sustainable coexistence between human workers and AI, although challenges arise when AI outpaces human job growth, emphasizing the necessity of effective policy responses to mitigate adverse effects and maximize the benefits of technological advancement. Understanding these dynamics is crucial for policymakers to navigate the complexities of AI-induced job displacement and ensure equitable societal outcomes.

This article introduces a novel approach called Digital Weighted Multi Criteria Decision Making (DWMCDM) that employs interval valued fuzzy sets to select the best contractor for building projects. The contractor is chosen based on the pre-qualification and bid evaluation phases. In the first phase, the distance between the actual and required skills of the significant criteria is determined, and it is then converted into digital weighted distances to identify the maximum number of criteria related to the specific project of each contractor. The second step ranks the best contractor based on the bid price and digital weighted distances. The suggested technique integrates the pre-qualification and bid review phases to address project award delays and other restrictions. Finally, a real-world application is addressed to demonstrate the applicability of the proposed approach to any type of interval valued fuzzy inputs.

In this study, an autonomous type deterministic nonlinear mathematical model that explains the transmission dynamics of COVID‐19 is proposed and analyzed by considering awareness campaign between humans and infectives of COVID‐19 asymptomatic human immigrants. Unlike some of other previous model studies about this disease, we have taken into account the impact of awareness c between humans and infectives of COVID‐19 asymptomatic human immigrants on COVID‐19 transmission. The existence and uniqueness of model solutions are proved using the fundamental existence and uniqueness theorem. We also showed positivity and the invariant region of the model system with initial conditions in a certain meaningful set. The model exhibits two equilibria: disease (COVID‐19) free and COVID‐19 persistent equilibrium points and also the basic reproduction number, R₀ which is derived via the help of next generation approach. Our analytical analysis showed that disease‐free equilibrium point is obtained only in the absence of asymptomatic COVID‐19 human immigrants and disease (COVID‐19) in the population. Moreover, local stability of disease‐free equilibrium point is verified via the help of Jacobian and Hurwitz criteria, and the global stability is verified using Castillo‐Chavez and Song approach. The disease‐free equilibrium point is both locally and globally asymptotically stable whenever R₀ < 1, so that disease dies out in the population. If R₀ > 1, then disease‐free equilibrium point is unstable while the endemic equilibrium point exists and stable, which implies the disease persist and reinvasion will occur within a population. Furthermore, sensitivity analysis of the basic reproduction number, R₀ with respect to model parameters, is computed to identify the most influential parameters in transmission as well as in the control of COVID‐19. Finally, some numerical simulations are illustrated to verify the theoretical results of the model.