The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation’s economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2’s pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.

With the proliferation of electronic commerce, the facial data used for identity authentication and mobile payment are potentially subject to data analytics and mining attacks by third-party platforms, which has raised public privacy concerns. To tackle the issue, a novel Multi-Level Face Anonymization Scheme (ML-FAS) based on deep learning technology is proposed in this paper. First, a 4-D chaotic system is employed to construct different levels of keys with initial parameters securely distributed and managed using the Semiconductor SuperLattice Physical Unclonable Function (SSL-PUF). Secondly, under the guidance of the known prior distribution and adversarial training strategy, a noise-like cipher image is generated by the encryption network to withstand the known-plaintext attacks. Besides, different levels of recipients can leverage the identical decryption network to reconstruct the facial images with varying visual content. Compared with the existing manually designed anonymization schemes, the ML-FAS possesses several significant merits. Finally, extensive simulation experiments verified the effectiveness of the proposed scheme, including its security and robustness. The code is available at https://github.com/DonghuaJiang/ML-FAS.

The growth in academic and scientific publications has increased very rapidly. Researchers must choose a representative and significant literature for their research, which has become challenging worldwide. Usually, the paper citation number indicates this paper’s potential influence and importance. However, this standard metric of citation numbers is not suitable to assess the popularity and significance of recently published papers. To address this challenge, this study presents an effective prediction method called FutureCite to predict the future citation level of research articles. FutureCite integrates machine learning with text and graph mining techniques, leveraging their abilities in classification, datasets in-depth analysis, and feature extraction. FutureCite aims to predict future citation levels of research articles applying a multilabel classification approach. FutureCite can extract significant semantic features and capture the interconnection relationships found in scientific articles during feature extraction using textual content, citation networks, and metadata as feature resources. This study’s objective is to contribute to the advancement of effective approaches impacting the citation counts in scientific publications by enhancing the precision of future citations. We conducted several experiments using a comprehensive publication dataset to evaluate our method and determine the impact of using a variety of machine learning algorithms. FutureCite demonstrated its robustness and efficiency and showed promising results based on different evaluation metrics. Using the FutureCite model has significant implications for improving the researchers’ ability to determine targeted literature for their research and better understand the potential impact of research publications.

This study evaluates the semantic alignment of ChatGPT’s responses with human-selected best answers in an open-domain question answering (QA) setting, using data from the Yahoo! Answers platform. Unlike prior research focused on domain-specific or educational datasets, this work investigates ChatGPT’s general-purpose QA capabilities across a diverse topical landscape. We apply BERTopic to extract latent themes from 500 full-question samples and use BERTScore metrics (precision, recall, F1) to quantify semantic similarity between ChatGPT-generated answers and top-rated community responses. Results show that ChatGPT achieves a strong average F1 score of 0.827, indicating high overall alignment with human judgments. Nonetheless, topic level analysis revealed important performance differences: the model was strong when asked factual and encyclopedia type questions, but was less capable of responding to subjective, ambiguous, or advice related questions. In this research, we proposed a topic-sensitive evaluation framework, that can be used to assess large language models in open-domain QA situations, that will add to the understanding of current benchmarking, interpreting performance, and building successful conversational AI systems.

The increasing prevalence of affordable smart hand-held devices and the expanding access to Internet connectivity in both urban and rural areas have fostered the rapid advancement of Video-on-Demand (VoD) streaming. However, accurately assessing the Quality of User Experience (QoUX) attributes for a VoD system remains a complex challenge. The dynamic and non-uniform nature of the Internet introduces significant unpredictability, compounded by the stochastic behavior of VCR actions, such as jumps, rewinds, stops, and pauses. To address these complexities, this paper presents a formal model for analyzing the QoUX performance of a multimedia e-commerce-focused VoD system. Employing the Continuous Time Markov Chain (CTMC) methodology and the PRISM model checker, we evaluate critical metrics such as throughput, latency, jitter, error rate, and the probability of user video rejection to ensure an enhanced user experience. Moreover, this study compares our findings with industry benchmarks, predicting thresholds for 5% packet loss and 5-second latency within the VoD system. We demonstrate that formal methods can effectively analyze complex VoD systems at the desired abstraction levels, akin to other measurement approaches in e-commerce, without the need for elaborate simulations, prototypes, and proofs-of-concept. By emphasizing trusted computing principles, this research underscores the potential for enhancing personalization and contextualization in e-commerce, ensuring an immersive and seamless user experience in the realm of multimedia commerce.

The growth rate in academic and scientific publications increased very fast. Researchers must choose a good representative and significant literature in publications, which has become challenging worldwide. Usually, the paper citation number indicates this paper's potential influence and importance. However, this standard metric of citation numbers cannot be used as a measurement to judge the popularity and significance of the recently published papers. To address this challenge, this paper presents an effective prediction method called FutureCite to predict the future citation level of research articles. FutureCite combines machine learning with text and graph mining techniques, leveraging their abilities in classification, datasets in-depth analysis, and feature extraction. FutureCite aims to predict future citation levels of research articles applying a multilabel classification approach. FutureCite can extract significant semantic features and capture the interconnection relationships found in scientific articles during feature extraction using textual content, citation networks, and metadata as feature resources. Our goal is to contribute to the advancement of substantial effective approaches impacting the citation counts in scientific publications by enhancing the precision of future citations. We conducted several experiments using a comprehensive publication dataset to evaluate our method and determine the impact of using a variety of machine learning algorithms. FutureCite demonstrated its robustness and efficiency and showed promising results based on different evaluation metrics. Using the FutureCite model has significant implications in improving the researchers' ability to determine targeted literature for their research and better understand the potential impact of research publications.