Abstract Video Analytics is widely used by the internet‐based platforms to govern the mass consumption of videos. Traditionally, it is carried out from the decoded format of the videos. This requires the analytics server to perform both decoding and analytics computation. This process can be made fast and efficient if performed over the compressed format of the videos as it reduces the decoding stress over the analytics server. The field of video analytics from the binarized formats using modern deep learning techniques is still emerging and needs further exploration. This proposed work is based on the same notion. In this work, two analytics tasks that is, classification and object detection are carried out from the binarized videos. The binarized formats are produced by using an already‐designed end‐to‐end video compression network. The experiments have been carried out over standard datasets. The proposed MobileNetv2‐based classification network shows an accuracy of 66% over the YouTube UGC dataset and the YOLOX‐S‐based detection network shows mAP of 45% over IMAGENet datasets. The proposed work shows competitiveness and improvement in the detection outcomes on compressed data and also provides further motivation for the adoption of deep learning‐based video compression in practical analytics domains.

Present MLL (Multimodal Large Language) models do exceptionally well in computer vision tasks, such as answering virtual questions and captioning images. However, they are not up to par in important perceptual tasks like object detection. To overcome this constraint, an entirely novel research work is presented for contextual object detection, which aims to comprehend observable items in various human-AI interaction scenarios. This study investigates two widely used scenarios: captioning the images and virtual question answering. For human-machine interaction, a brand-new CODNet (Context-based Object Detection Network) is presented that can locate, recognize, and connect visual objects with spoken inputs. The proposed network is capable of end-to-end modeling of visual language contexts. This network unifies language and vision functions by interpreting images in a foreign language and coordinating language functions with vision-centric jobs that may be managed and created flexibly using language instructions. The network comprises three sub-modules: an encoder that extracts the required features from the context, a pre-trained large language model meta AI (Llama) for decoding the context, and a decoder for predicting anchor boxes around the contextually recognized objects in images. The features are extracted with the help of a convolutional neural network merged with multiplicative LSTM. A fine-tuned YOLOv6 model detects and locates the required objects in the image provided. The network is trained on the MS-COCO dataset, evaluated on real-time images, and achieved over 95% average weighted accuracy. • Context-based object detection refers to identifying objects within an image or a sequence of images by considering the surrounding context in which these objects appear. For example, a keyboard is usually found near a computer. Some objects are more likely to appear together. For example, a fork and a knife are often found together on a dining table. • This study presents a new context-based object detection network that defines a new direction in visual object detection and improves LLMs. • A novel CODNet network is proposed that helps in first generating the object words and then classifying the objects in the images. The network comprises three sub-modules: encoder, Llama, and decoder. The network combines CNN-mLSTM (Convolutional Neural Network merged with multiplicative LSTM) to help extract features from provided images. Llama is a pre-trained large language model for decoding the context and finetuned YOLOv6 for object detection. • A new set of object words, namely, ERCODE (Easy Readable Context-Based Object DEtection), is also proposed in this work to help classify required objects in images.

Abstract The transport system manages traffic between cities worldwide. In addition to cities, highways and other locations can also experience traffic congestion. The area’s existing transportation system is unsatisfactory without supervision. The constraints of the present traffic monitoring system in collecting road data and expanding its visual range are improved by using remote sensing data to identify congestion. Since some remote sensing data must be kept private, this issue must be resolved to safeguard the security of remote sensing data while deep learning training is underway. In contrast to the traditional deep learning training method, this work provides a federated learning methodology to detect automobile objects in remote sensing pictures, addressing the data privacy problem in the training stage of remote sensing data. This study uses a finetuned YOLOv6 model to detect different vehicles along with federated learning. This experiment uses real-time remote sensing data as training samples. The training results reach an accuracy of roughly 95.89%, and the estimated processing time is as short as 0.047 s. A method that effectively controls traffic and improves the passing-vehicle ratio at the intersection is also introduced. Using a mathematical process, the dependence of fuel use on trip time and fuel consumption is also determined, which helps reduce vehicle idle time and fuel consumption. To detect congestion, the system will automatically recognize automobiles as objects in a traffic scenario based on the final experimental findings.

The Internet of Things (IoT) is being prominently used in smart cities and a wide range of applications in society. The benefits of IoT are evident, but cyber terrorism and security concerns inhibit many organizations and users from deploying it. Cyber-physical systems that are IoT-enabled might be difficult to secure since security solutions designed for general information/operational technology systems may not work as well in an environment. Thus, deep learning (DL) can assist as a powerful tool for building IoT-enabled cyber-physical systems with automatic anomaly detection. In this paper, two distinct DL models have been employed i.e., Deep Belief Network (DBN) and Convolutional Neural Network (CNN), considered hybrid classifiers, to create a framework for detecting attacks in IoT-enabled cyber-physical systems. However, DL models need to be trained in such a way that will increase their classification accuracy. Therefore, this paper also aims to present a new hybrid optimization algorithm called “Seagull Adapted Elephant Herding Optimization” (SAEHO) to tune the weights of the hybrid classifier. The “Hybrid Classifier + SAEHO” framework takes the feature extracted dataset as an input and classifies the network as either attack or benign. Using sensitivity, precision, accuracy, and specificity, two datasets were compared. In every performance metric, the proposed framework outperforms conventional methods.

Abstract In agriculture, crops are severely affected by illnesses, which reduce their production every year. The detection of plant diseases during their initial stages is critical and thus needs to be addressed. Researchers have been making significant progress in the development of automatic plant disease recognition techniques through the utilization of machine learning (ML), image processing, and deep learning (DL). This study analyses the recent advancements made by researchers in the field of ML techniques for identifying plant diseases. This study also examines various methods used by researchers to produce ML solutions, such as image preprocessing, segmentation, and feature extraction. This study highlights the challenges encountered while creating plant disease identification systems, such as small datasets, image capture conditions, and the generalizability of the models, and discusses possible solutions to cater to these problems. Still, the development of a solution that automatically detects various plant diseases for various plant species remains a big challenge. To address these challenges, there is a need to create a system that is trained on an extensive dataset that contains images of various types of diseases a plant can suffer from, and plant images should be taken at various stages of the disease's development. This study further presents an analysis of various methods used at different stages of plant disease identification.

Effective traffic congestion prediction is need of the hour in a modern smart city to save time and improve the quality of life for citizens. In this study, AB_AO (ARIMA Bi-LSTM using Aquila optimizer), a hybrid predictive model, is proposed using the most effective time-series data prediction statistical model ARIMA (Autoregressive Integrated Moving Average) and sequential predictive Deep Learning (DL) technique LSTM (Long Short-Term Memory) which helps in traffic congestion prediction with a minimum error rate. Also, the Aquila optimizer (AO) is used to elevate the adequacy of the AB_AO model. Three road traffic datasets of different cities from the “CityPulse EU FP7 project” are used to implement the proposed hybrid model. In a time-series dataset, two components need to be handled with care, i.e., linear and nonlinear. In this study, the ARIMA model has been used to manage linear components and Bi-LSTM is used to handle nonlinear components of the time-series dataset. The Aquila Optimizer (AO) is used for hyperparametric tuning to enhance the performance of Bi-LSTM. Error measurement parameters like the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) are used to validate the results. A detailed mathematical and empirical analysis is given to justify the performance of the AB_AO model using an ablation study and comparative analysis. The AB_AO model acquires more stable and precise results with MSE as 18.78, MAE as 3.18, and MAPE as 0.21 than other models. It may further help to predict the vehicle count on the road, which may be of great help in reducing wastage of time in traffic congestion.