Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments.

Aerial image-based target object detection has several glitches such as low accuracy in multi-scale target detection locations, slow detection, missed targets, and misprediction of targets. To solve this problem, this paper proposes an improved You Only Look Once (YOLO) algorithm from the viewpoint of model efficiency using target box dimension clustering, classification of the pre-trained network, multi-scale detection training, and changing the screening rules of the candidate box. This modified approach has the potential to be better adapted to the positioning task. The aerial image of the unmanned aerial vehicle (UAV) can be positioned to the target area in real-time, and the projection relation can convert the latitude and longitude of the UAV. The results proved to be more effective; notably, the average accuracy of the detection network in the aerial image of the target area detection tasks increased to 79.5%. The aerial images containing the target area are considered to experiment with the flight simulation to verify its network positioning accuracy rate and were found to be greater than 84%. This proposed model can be effectively used for real-time target detection for multi-scale targets with reduced misprediction rate due to its superior accuracy.

Day by day, biometric-based systems play a vital role in our daily lives. This paper proposed an intelligent assistant intended to identify emotions via voice message. A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions. This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes (LED) alert signals and it also keep track of the places like households, hospitals and remote areas, etc. The proposed approach is able to detect seven emotions: worry, surprise, neutral, sadness, happiness, hate and love. The key elements for the implementation of speech emotion recognition are voice processing, and once the emotion is recognized, the machine interface automatically detects the actions by buzzer and LED. The proposed system is trained and tested on various benchmark datasets, i.e., Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) database, Acoustic-Phonetic Continuous Speech Corpus (TIMIT) database, Emotional Speech database (Emo-DB) database and evaluated based on various parameters, i.e., accuracy, error rate, and time. While comparing with existing technologies, the proposed algorithm gave a better error rate and less time. Error rate and time is decreased by 19.79%, 5.13 s. for the RAVDEES dataset, 15.77%, 0.01 s for the Emo-DB dataset and 14.88%, 3.62 for the TIMIT database. The proposed model shows better accuracy of 81.02% for the RAVDEES dataset, 84.23% for the TIMIT dataset and 85.12% for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM) and Support Vector Machine (SVM) Model.

Recently, the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the economy. World Health Organization (WHO) and many others advised controlling Corona Virus Disease in 2019. The limited treatment resources, medical resources, and unawareness of immunity is an essential horizon to unfold. Among all resources, wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) droplets. All countries made masks mandatory to prevent infection. For such enforcement, automatic and effective face detection systems are crucial. This study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without masks. To contribute to society, we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or organization. The paper aims to get detection accuracy using transfer learning from Residual Neural Network 50 (ResNet-50) architecture and works on detection localization. The experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks (AlexNet), Residual Neural Networks (ResNet), and Visual Geometry Group Networks (VGG-Net) advanced architecture. The proposed system generates an accuracy of 98.4% when modeled using Residual Neural Network 50 (ResNet-50). Also, the precision and recall values are proved as better when compared to the existing models. This outstanding work also can be used in video surveillance applications.

An accurate identification of objects from the acquisition system depends on the clear segmentation and classification of remote sensing images. With the limited financial resources and the high intra-class variations, the earlier proposed algorithms failed to handle the sub-optimal dataset. The building of an efficient training set iteratively in active learning (AL) approaches improves classification performance. The heuristics-based AL provides better results with the inheritance of contextual information and the robustness to noise variations. The uncertainty exists pixel variations make the heuristics-based AL fail to handle the remote sensing image classification. Previously, we focused on the extraction of clear textural pattern information by using the extended differential pattern-based relevance vector machine (EDP-AL). This paper extends that work into the novel pixel-certainty activity learning (PCAL) based on the information about textural patterns obtained from the extended differential pattern (EDP). Initially, distributed intensity filtering (DIF) is used to eliminate noise from the image, and then histogram equalization (HE) is used to improve the image quality. The EDP is used to merge and classify different labels for each image sample, and this algorithm expresses the textural information. The PCAL technique is used to classify the HSI patterns that are important in remote sensing applications using this pattern collection. Pavia University and Indian Pines (IP) are the datasets used to validate the performance of the proposed PCAL (PU). The ability of PCAL to accurately categorize land cover types is demonstrated by a comparison of the proposed PCAL with existing algorithms in terms of classification accuracy and the Kappa coefficient.

Internet of Things (IoT) assisted application in agriculture shows tremendous success to improve productivity in agriculture. Agriculture is grappling with issues such as depleted soil fertility, climate-related hazards like intensified pest attacks and diseases. Accurate forecasting of pest outbreaks can play a vital role in improving agricultural yield. Utilizing IoT technology for environmental monitoring in crop fields to forecast pest attacks. The important parameters for pest predictions are temperature, humidity, rainfall, wind speed and sunshine duration. Directly sensed environmental conditions are utilized as input to a deep learning model, which makes binary decisions about the presence of pest populations based on the prevailing environmental conditions. The accuracy and precision of the deep learning model in making predictions are assessed through evaluation with test data. Five year data 2028 to 2022 have been used for making prediction. The model of pest prediction generates weekly predictions. The overall accuracy of the weekly predictions is 94% and high F-measure, Precision, Recall, Cohens kappa, and ROC AUC for making to optimize the prediction. The accuracy of the pest prediction improves gradually with time. Weekly predictions are generated from the means of all environment conditions from the last seven days. The weekly predictions are important for the short-term measures against the pest attacks.