[This retracts the article DOI: 10.1016/j.heliyon.2023.e21947.].

Weightlifting is a popular sport and a hobby of millions; however, the risk of injury is high, primarily when performed with incorrect form/posture due to voluntary or involuntary reasons. It results in a significant loss of strength and hinders progress. The gradual strain on tendons and ligaments causes wear and tear. This paper aims to develop a deep learning model that detects incorrect posture and provides insight into remedial steps that need to be taken to correct the same, thereby providing increased safety and efficiency. Our Form Check model detects a user's form, vector geometry of posture is evaluated and corrected. A graphical representation of the exercise is provided as feedback to the user. Object detection, pose estimation, and action recognition algorithms infer the human skeleton and recognize the exercise. The inconsistency in the exercise performed is determined and mapped to specific imperfections in the stance of users during the exercise through Graph

Digital networks present a number of interesting research problems, one of which is the secure image transfer through various channels of communication. In these circumstances, cryptography algorithms are commonly employed to securely encrypt and decode data at both the sending and receiving ends. To determine the most effective method of image encryption, a wide range of modern algorithms are analysed and compared. In this study, we describe several fuzzy modes of operation for image encryption. It guarantees the safety of digital image transfer. The work's goal is to provide the most trustworthy image encryption possible; This is achieved by integrating image encryption methods into a hybrid fuzzy based architecture. The proposed composite fuzzy-based encrypted systems (CFES) are recommended for use in order to ensure the security of data on various communications media. The composite fuzzy-based encrypted systems provide strong security while yet protecting users' privacy while viewing image content.

As wireless communication grows, so does the need for smart, simple, affordable solutions. The need prompted academics to develop appropriate network solutions ranging from wireless sensor networks (WSNs) to the Internet of Things (IoT). With the innovations of researchers, the necessity for enhancements in existing researchers has increased. Initially, network protocols were the focus of study and development. Regardless, IoT devices are already being employed in different industries and collecting massive amounts of data through complicated applications. This necessitates IoT load-balancing research. Several studies tried to address the communication overheads produced by significant IoT network traffic. These studies intended to control network loads by evenly spreading them across IoT nodes. Eventually, the practitioners decided to migrate the IoT node data and the apps processing it to the cloud. So, the difficulty is to design a cloud-based load balancer algorithm that meets the criteria of IoT network protocols. Defined as a unique method for controlling loads on cloud-integrated IoT networks. The suggested method analyses actual and virtual host machine needs in cloud computing environments. The purpose of the proposed model is to design a load balancer that improves network response time while reducing energy consumption. The proposed load balancer algorithm may be easily integrated with peer-existing IoT frameworks. Handling the load for cloud-based IoT architectures with the above-described methods. Significantly boosts response time for the IoT network by 60 %. The proposed scheme has less energy consumption (31 %), less execution time (24\%), decreased node shutdown time (45 %), and less infrastructure cost (48\%) in comparison to existing frameworks. Based on the simulation results, it is concluded that the proposed framework offers an improved solution for IoT-based cloud load-balancing issues.

Gaming has become a highly popular mode of entertainment, with games often eliciting strong emotional responses from players. As such, there is a need to develop new approaches that can enhance the overall gaming experience. Given that humans frequently undergo changes in emotional states without being fully conscious of them, proper emotion control is imperative. To address this, we propose the development of a game that changes difficulty with player’s emotions. This game can record user emotions in real-time and modify the game’s difficulty accordingly. This emotion-adaptive game operates by adapting to the emotional states of the player, with the difficulty level changing based on these emotions. For the game, a desktop-based space shooter game has been selected, where the player must shoot down enemy ships. Players who exhibit negative emotions are subjected to a penalty, resulting in an increase in game difficulty. The machine learning algorithm has been chosen for emotion prediction, which has been trained on a dataset of labeled facial expressions and their corresponding emotions. To predict emotions in real-time during gameplay, a machine learning model has been integrated into the game, and the game and emotion predictor are run concurrently using threads. The emotion information obtained from the predictor is then used by the game to dynamically adjust the difficulty level. By employing this approach, emotion-adaptive gaming can not only improve the overall gaming experience but can also enhance emotion control and self-awareness.

Diabetic Retinopathy (DR) is a dangerous disease nowadays, which may cause vision loss. Current deep learning models are successful in classifying different stages of DR effectively. Still, there is a memory bottleneck to deploy these models into mobile-like devices. The computational cost of existing deep learning models needs to reduce for commercial medical applications. Existing lightweight models facing challenges with parameter reduction, minimizing quantization loss, and gradient error. To combat these challenges, we proposed a lightweight CNN model that can occupy less space and improve the model’s performance. Our model experimental results prove the storage and execution efficient compared to competitive models.

On a global scale, at least 2.2 billion people are affected by a near or distance vision impairment. Vision is an essential aspect of human life, and vision loss is a problem many unfortunate people experience. Many blind people rely on white cane sticks and guide dogs for navigation, but they come with their own set of drawbacks. Assistive technologies are those utilities that aid visually impaired people in carrying out some specific tasks. This paper presents a novel assistive device that overcomes the issue of low visual coverage of the surroundings by using multiple cameras. The blind user can wear the cameras in the front, right, back, and left directions surrounding the neck, getting wider visual coverage. A raspberry pi microcomputer captures images from the cameras and uses them to find objects and faces without using the internet. The microcomputer provides voice feedback on the detections and the camera direction through earphones. An Arduino microcontroller connected to an ultrasonic sensor senses the obstacles and alerts the user with a vibration motor. A smartphone can connect to these Arduino and Raspberry pi modules via Bluetooth, and the user can send commands with the mobile application by touching or by voice. The system could get a broad picture of the surroundings and provide output in a reasonable amount of time.

In this paper, an autonomous brain tumor segmentation and detection model is developed utilizing a convolutional neural network technique that included a local binary pattern and a multilayered support vector machine. The detection and classification of brain tumors are a key feature in order to aid physicians; an intelligent system must be designed with less manual work and more automated operations in mind. The collected images are then processed using image filtering techniques, followed by image intensity normalization, before proceeding to the patch extraction stage, which results in patch extracted images. During feature extraction, the RGB image is converted to a binary image by grayscale conversion via the colormap process, and this process is then completed by the local binary pattern (LBP). To extract feature information, a convolutional network can be utilized, while to detect objects, a multilayered support vector machine (ML-SVM) can be employed. CNN is a popular deep learning algorithm that is utilized in a wide variety of engineering applications. Finally, the classification approach used in this work aids in determining the presence or absence of a brain tumor. To conduct the comparison, the entire work is tested against existing procedures and the proposed approach using critical metrics such as dice similarity coefficient (DSC), Jaccard similarity index (JSI), sensitivity (SE), accuracy (ACC), specificity (SP), and precision (PR).

Neuroimaging is critical in the diagnosis and treatment of brain cancers; however, the first detection of tumors is a challenge. Detection techniques like image segmentation are heavily reliant on the segmented image's resolution. Magnetic resonance imaging (MRI) tumor segmentation has emerged as a new study area in the medical imaging field. This spongy and delicate mass of tissue is the brain. Stable conditions allow for patterns to enter and interact with each other. To put it simply, a tumor is a mass of tissue that has grown unchecked by the natural mechanisms that keep it under control. When cells divide uncontrollably, they create a cancerous tumor. Brain tumors can be detected and segmented using a variety of methods. A new method for detecting brain tumors using MRI images is presented in this research. An innovative Woelfel filter is used for enhancement, and morphological segmentation approaches combined with anisotropic diffusion are used for segmentation. Segmentation of brain tumors can be accomplished using thresholding and morphological techniques, which are both effective. The tumor will be located and identified using morphological image processing. Image denoising refers to the process of removing artefacts such as noise and aliasing from digital images. Here MATLAB programming language is utilised as it incorporates all the toolboxes required for the application involved in the work.

Diabetic Retinopathy (DR) is a rapidly spreading disease that can lead to blindness. Early detection can help to limit disease progression and minimize treatment costs. The process of finding a real DR is very much dependent on the clinical experts. The computer-aided software approach in solving this problem gain attention with large scale datasets. Modern techniques of Deep Learning can achieve extraordinary results in DR classification. However, with the current models, memory usage and increased runtime is a significant problem. To solve the problem, we recommend Binary Convolutional Neural Networks (BCNN), which significantly reduce memory usage and speed up the execution process. We augmented the dataset in preprocessing to train our model effectively. Our hardware friendly model outperforms in restricted memory environments. Our experiments using the Kaggle dataset reduced the memory usage and increased execution speed compared to the base model.

Credit card usage has increased a lot nowadays. Due to online banking services and mobile banking applications, payments done through credit cards are growing year by year. As usage increased, frauds on them increased, which are resulting in severe consequences. Nowadays, most of the authors are using Machine Learning models to detect credit card frauds and produce noticeable results. In this paper, we gave a detailed study of those models with exciting insights. These are useful for researchers in finding the gaps in this domain and proposing novel solutions for their contributions.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Morarjee Kolla, H. V. Ramana Rao, N. Vijay Kumar; COVID-19 image classification techniques in medical analysis using deep representations. AIP Conf. Proc. 30 July 2021; 2358 (1): 070008. https://doi.org/10.1063/5.0057943 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Diabetic Retinopathy (DR) is a fastly spreading disease that may lead to loss of vision if not quickly detected and treated. Early-stage detection is beneficial to restrict the progress of disease and reduces the recovery expenditure. The current detection process of DR heavily depends on domain experts. Machine-dependent approaches are gain attention with large-scale fundus image repositories to overcome this difficulty. Recent techniques with deep learning are successful in getting noticeable results with pre-trained networks. However, the increase of memory occupancy and runtime with existing models is the bottleneck. We propose Binary Convolutional Neural Networks (BCNN), which drastically reduces memory consumption and faster the execution process to combat this problem. Our model is hardware friendly and efficient in DR classification with large scale fundus images. Experiments conducted using the Kaggle dataset reduce memory consumption by 37% and increase runtime by 49% compared to the base model.

The development of Image Processing techniques in classification using conventional features has not yet achieved the expected results. The medical analysis is still beginning with these traditional imaging functions. The use of Deep representations can attain improvements in medical analysis instead of conventional features. Researchers explored several techniques using Deep Classification in the Medical field. Dealing with the leading cause of vision impairment and blindness of Diabetic Retinopathy using the existing methods suffers with memory bottleneck. The purpose of this paper is to describe the various deep classification techniques used in Diabetic Retinopathy and discuss novel ideas that may be useful for advanced medical analysis and research.

Alerts are tasks that continually monitor active queries to look for and report on specific events or conditions like system performance, security incidents, and threats for a system or network. Companies with an extensive IT infrastructure often deal with many alerts per day, varying from a routine host or network performance notifications to security incidents raised by Network Security Devices. With an increase in cyberattacks, Network Security Devices play a vital role in detecting critical incidents and threats. However, more often than not, these security incidents are frequently occurring low threat alerts. As the number of alerts skyrockets, it becomes increasingly tedious to sift through all the alerts generated and identify critical one. This may result in longer response time or overlooking important alerts, which is referred to as alert fatigue. Aiming to tackle this problem, our paper proposes a solution to reduce alert fatigue by identifying and highlighting anomalous alerts using Extended Isolation Forest, an isolation-based anomaly detection technique. Our model reduces the number of alerts received at the Security Operations Center (SOC) by 82.15%. The security analyst needs to monitor only 17.85% of the 50,000 total alerts received from the IDS.

Start in today’s time; e-commerce has been growing steadily over the years. Due to the variation of online choices, small prices, and fast sourcing organizations, it has become fashionable to buy products, for most people want to shop online. Meanwhile, the quantity and distribution of goods are unequal, and counterfeit branded products are distributed. We review the product as a dataset at Amazon retailers for customer reviews and rank the review text from Objectivity or Fairness and Negative or Positive buyer perspective. These reviews help somewhat, and they promise buyers and manufacturers of products. In this study, we will analyze the basics of specific, positive, and negative production approach.

Deep Clustering leans feature representations in embedded space suitable for clustering. In Deep Convolutional Embedded Clustering (DCEC) algorithm, the last convolution layer feature map of encoder is used to build the embedded space. This considers spatial information retains in the last convolution layer of encoder, which unable to identify the discriminative parts or the image. To address this issue, we propose a solution using Global Average Pooling (GAP) of the last convolution layer feature maps in the encoder. This Will encourage the network to identify all discriminative regions and an extent or an object to formulate semantic image clusters (SIC). Our experimental results prove the efficiency of proposed Global Average Pooled Deep Convolutional Embedded Clustering (GAPDCEC) for simultaneous feature learning and clustering.

Discovering various interest of users from massive image databases is a strenuous and rapid impel expedition region. Understanding the needs of users and representing them meaningfully is a challenging task. Region-based image retrieval (RBIR) is a method that incorporates the meaningful description of objects and an intuitive specification of spatial relationships. Our proposed model introduces a novel technique of semantic clustering in two stages. Initial semantic clusters are constructed in the first stage from the database log file by focusing on user interested query regions. These clusters are further refined by relevance feedback in the second stage based on probabilistic feature weight using positive and negative examples. Our results show that the proposed system enhances the performance of semantic clusters.

Extracting Semantic images from the large amount of heterogeneous image data is a quiet challenge in Content Based Image Retrieval (CBIR). Search space and Semantic gap reduction are two major issues in extracting semantic images. The proposed method of Region based semantic image retrieval considers both Search space and Semantic gap reduction. The proposed methodology first does the region based clustering as it reduces retrieval search space. Later it reduces the semantic gap with the support of ontology framework. The ontology framework shares the information among image seekers and domains. Our experimental results reveal the efficacy of the proposed method.