Agriculture is essential to the growth of every country. Cotton and other major crops fall into the cash crops. Cotton is affected by most of the diseases that cause significant crop damage. Many diseases affect yield through the leaf. Detecting disease early saves crop from further damage. Cotton is susceptible to several diseases, including leaf spot, target spot, bacterial blight, nutrient deficiency, powdery mildew, leaf curl, etc. Accurate disease identification is important for taking effective measures. Deep learning in the identification of plant disease plays an important role. The proposed model based on meta Deep Learning is used to identify several cotton leaf diseases accurately. We gathered cotton leaf images from the field for this study. The dataset contains 2385 images of healthy and diseased leaves. The size of the dataset was increased with the help of the data augmentation approach. The dataset was trained on Custom CNN, VGG16 Transfer Learning, ResNet50, and our proposed model: the meta deep learn leaf disease identification model. A meta learning technique has been proposed and implemented to provide a good accuracy and generalization. The proposed model has outperformed the Cotton Dataset with an accuracy of 98.53%.

Vehicular ad hoc networks (VANETs) connect two or more vehicles wirelessly to enable data exchange in an Internet of Things (IoT) environment. In VANETs, location privacy is the most crucial piece of information, and its protection is the top priority. However, the location privacy threats have not been adequately addressed in positioning for IoT in VANETs. This paper provides an overview of location privacy attacks and their solutions to address the problems caused by attacks in any IoT environment. Secondly, we have analyzed specific solutions based on anonymity (pseudonym) and cryptographic solutions using a digital signature technique. This enables to improve user privacy and security of location‐based services for IoT in VANET. Moreover, we have proposed a faster 5G solution for the VANETs as it rapidly disseminates the data in fast‐moving vehicles.

Previous studies widely report the optimization of performance predictions to highlight at-risk students and advance the achievement of excellent students. They also have contributions that overlap different fields of research. On the one hand, they have insightful psychological studies, data mining discoveries, and data analysis findings. On the other hand, they produce a variety of performance prediction approaches to assess students’ performance during cognitive tasks. However, the synchronization between these studies is still a black box that increases prediction systems’ dependency on real-world datasets. It also delays the mathematical modeling of students’ emotional attributes. This review paper performs an insightful analysis and thorough literature-based survey to draw a comprehensive picture of potential challenges and prior contributions. The review consists of 1497 publications from 1990 to 2022 (32 years), which reported various opportunities for future performance prediction researchers. First, it evaluates psychological studies, data analysis results, and data mining findings to provide a general picture of the statistical association among students’ performance and various influential factors. Second, it critically evaluates new students’ performance prediction techniques, modifications in existing techniques, and comprehensive studies based on the comparative analysis. Lastly, future directions and potential pilot projects based on the assumption-based dataset are highlighted to optimize the existing performance prediction systems.

This paper presents a systematic literature review on Intelligent Process Automation (IPA), focusing on its role in modernizing business operations. It explores the integration of AI, including machine learning and robotic process automation, and their combined impact on improving efficiency and supporting human-centric tasks. The review examines the synergy between AI and human expertise, highlighting how this collaboration enhances decision-making, reduces errors, and speeds up task execution. It also investigates how digital assistants within the IPA framework redefine operational processes, supported by case studies and empirical research. The paper addresses challenges and ethical concerns related to AI integration, offering insights on risk management and ethical AI use. In conclusion, it emphasizes the paradigm shift created by AI-driven IPA, optimizing business processes and focusing on cognitive tasks, and contributing to the ongoing discussion about AI's transformative impact on human-machine collaboration.

Currently, AI and AR are the most advanced technologies inspiring innovations in numerous areas. While AR provides novel, engaging user experiences, it also creates tedious and unstructured development issues like content production and marker detection. Recently, AI has been investigated to provide solutions to those AR limitations. The results from those studies inspired this work to present DeepReality, a Unity 3D plug-in that combines Deep Learning (DL) models with AR using Barracuda inference engine and AR Foundation. The ease with which DeepReality provides DL-AR integration allows developers to deploy DL-based iOS and Android mobile apps to extract visual elements and overlay AR content onto physical objects. DeepReality enhances feature-based environmental monitoring through incongruous items and semantic processing of objects. This study also presents a smart gym device that uses AI to adjust posture in real time to ensure optimal form during workouts like the chest press and to increase effectiveness. The system analyzes the violation of postures and gives audio feedback using speech recognition by recognizing the body key points through the Mediapipe framework. In addition, while creating custom training programs, diet recommendations, and motivational music playlists, user goals will be integrated into the design, meanwhile, 3D virtual trainers demonstrate exercises. This AI-powered concept merges augmented reality with real-time feedback to create long-term exercise habits and overall wellness. Evaluation of DeepReality's execution time and memory consumption prove it to be user-friendly and menial device compatible. Extensive metrics and analysis, operational validation, and modularity for DL integration are provided by this open-source toolkit available from the Unity asset store. By making the AI AR integration available for all applications on AR, developers can leverage the transformative power of AI to enhance their AR applications. Thereby, the future of AR-AI synergy is forged with DeepReality.

Bioacoustics plays an important role in the conservation of bird species. Bio-acoustic surveys based on autonomous audio recording are both cost-effective and time-efficient. However, there are many bird species with different patterns of vocalization, and it is a challenging task to deal with them. Previous studies have revealed that many authors focus on the segmentation of bird audio without considering specific patterns of bird vocalization. Based on the existing literature, currently there is no work on the segmentation of monosyllabic and multisyllabic birds, separately. Therefore, this research addresses the aforementioned concern and also proposes a collection of audio features named ‘Perceptual, Descriptive, and Harmonic Features (PDHFs)’ that gives promising results in the classification of bird vocalization. Moreover, the classification results improved when monosyllabic and multisyllabic birds were classified separately. To analyze the performance of PDHFs, different classifiers were used in which Artificial neural network (ANN) outperformed other classifiers and demonstrated an accuracy of 98%.