This paper presents a brief survey of the approaches under the DRL umbrella for game playing and a discussion of the problems and the advances that have been witnessed in this promising research line. It is well understandable that the inputs are highly dimensional and the game environment is dynamic and hence the model described above incorporated both CNNs and RNNs. Other components of the methodology were the HRL that started by deconstructing a task into sub-tasks and then the sub-tasks were directed by a master policy. The DRL agents were tested on different platform environments: Specific and steady-build games and platforms and then to evaluate its performance, learning effect, and or its ability to generalize. It was found that the proposed methods were reasonably close to the other effective existing methods; in addition, the time required to train the model was less and the percentage of positive results in the games were also good showing an acceptable generality. The study has also has also extended the prior work on transfer learning as well as the efficient rewarding exploration approach to counter negative impacts of limited rewards and computational time. The data generated by this work contributes to the Doctoral study assessment and is relevant to various DRL themes and can be applied for Robotics and AS fields based on AI. In conclusion, the authors repeat their imperative for future work on expanding DRL approaches that are effective on a large scale, the limitations of application to multi-agent systems, and the transferability of learnt behaviour to improve the AI elements of game playing that is not limited to.

This study aims to provide a wide-ranging investigation of the ML-based drug discovery approach by discussing ML methods, results, and outcomes at the drug development process. The methodology covers the steps of data acquisition and preprocessing, feature engineering and selection, model selection and optimization, virtual screening of drugs, prediction of response to drugs, toxicity prediction, integration of multi-omics data, and validation and deployment of an ML model. The study shows superiority of ML-driven methods in terms of speeding up the candidate selection process, prediction of drug target relationships, toxicity prognosis, and integration of multiple molecular datasets for the discovery of new therapeutic targets and biomarkers. While the models might perform seriously in metrics and such, without the model interpretability, generalization, and ethics, there comes necessity to research more and to work together with various experts. A talk is given where a clear link between accurate delivering results, ethical management and regulations as well as a fully transparent reporting as can be done is given for responsible and fair ML technology development in drug discovery is made. If we cast our eye on the future, constant improvements and capital increases in ML based methods are set to achieve a fundamental breakthrough in drug development and individualized medicine and thus ultimately turn patients lives upside down for the better also global scale.

This paper introduces a systematic methodology for enhancing cloud security in information systems by applying mathematical algorithms in cybersecurity. Using the most sophisticated computational methods, our approach strives to boost the security posture of cloud-based platforms by not only identifying but also blocking, and rectifying various cyber threats. We start with a detailed review and analysis of various algorithms, which contribute to the identification of gaps in the current research landscape and solutions already existing. We proceed by performing the requirement analysis during which we set up security targets for the cloud environment to be optimized. The second step consists of deliberation on what the best algorithms are and is involved in a process that will meet the identified requirements. We did the development work for a prototype of the security solutions described beforehand. It was integrated and tested on a cloud environment for the best KPI result. Performance metrics, including detection accuracy (98.5%), false positive rate (0.2%), response time (25 milliseconds), and resource utilization (CPU: The JSON-LD, Microformats, and MAP-trees (10%, 30%, and 40%) algorithms were assessed to determine the functionality of the algorithms. Thus, the security effectiveness values will be obtained in the following way: malware detection (99%) – intrusion detection (97%) – data breach prevention (95%) and insider threat detection (98%) will be determined through comprehensive testing with numerous cyber threats. One more thing I would like to mention is the scalability parameter of the security solutions they function both for 1,000 concurrent users and handle 1TB of data per day and 1,000 requests per second. The team calculated the scalability which was found out to be 95% which is over 90% thus highly scalable to meet the growing demand. Besides, user satisfaction ratings were obtained which were; (4.5), (4.3), (4.7) and (4.6) for usability, reliability, performance and effectiveness respectively on a scale of one to five using the feedback from stakeholder and end-users as a rating tool.

Due to the heightened expenses associated with maintaining extensive data storage infrastructure on-premises, many organizations face challenges in accommodating large volumes of data within their facilities. Data outsourcing proves beneficial for users as it alleviates the responsibility of storing and managing the data. While efforts have been made to establish a secure and reliable cloud platform for data storage, persistent concerns linger regarding the confidentiality and integrity of data and applications stored in the cloud. Consequently, there exists a critical necessity to establish a robust security framework for cloud-based data storage. In response to this, a proposal is presented for a secure block-level cloud storage system, leveraging the efficient randomized round encryption protocol for encryption and decryption, ensuring the storage and management of sensitive data in a secure manner.

The distributed denial of service (DDoS) assault was a kind of intrusion in the cloud computing environment that severely affects the end user by injecting illegitimate packets. To obtain performance, a hybrid improved wolf optimizer with asymmetric key Goldwasser cryptography (IWO-AKGC) algorithm was proposed based on combining the exploitation ability of security and exploration capability of machine learning. In addition to the selection of parameters, a proposed hybrid IWO-AKGC technique is used for weighting and bias coefficients in neural network models. This has led to an immediate improvement in communication security for the delivery of different types of data services via clouds, thanks to the proposed IWO-AKGC method. The recommended hybrid optimizer successfully addresses the drawbacks of conventional methods, such as local stagnation problems, delayed convergence problems, and local and global optimal trapping problems. Thus, secured data communication is obtained for cloud service provisioning. The proposed model proved to be a better model for DDoS intrusion detection.

Fault-tolerant quantum error correction (FTQEC) is pivotal for ensuring the reliability and scalability of quantum computers, which harness quantum mechanics principles. This paper presents a comprehensive analysis of FTQEC methodologies, integrating theoretical investigations, experimental validations, error correction capabilities, and simulation comparisons. Theoretical frameworks reveal high error correction capabilities, with success rates ranging from 0.88 to 0.95 and significant error rate reductions of up to 98%. Experimental validations across diverse quantum hardware platforms demonstrate promising results, with high success rates and notable error correction efficiency, albeit challenges in scalability. Analysis of error correction capabilities highlights robustness against errors and preservation of qubits integrity during error correction procedures. Simulation comparisons further validate the efficacy of FTQEC protocols, showing significant improvements in error rate reduction and gate fidelity. However, scalability and resource overhead remain concerns, necessitating further research for efficient and scalable FTQEC protocols. Overall, this study contributes to the understanding and advancement of fault-tolerant quantum error correction, crucial for realizing the transformative potential of quantum computing technology.

The advancement and innovations in the field of science and technology paved way for various advanced treatments in the field of medicine. They are implemented using sensors, and computer-aided designs with artificial intelligence techniques. This helps in the detection of serious health constraints at an earlier stage with appropriate treatments using decision-making techniques. One of the important health concerns that are increasing rapidly is cardiovascular disorders. This includes Arrhythmia and Myocardial Infarction. Earlier prediction and classification can protect them from serious constraints. They are diagnosed using the Electrocardiogram (ECG). To obtain accurate results, artificial intelligence techniques are implemented to extract the optimum output. The proposed system includes the detection and classification using deep learning techniques with the Internet of Things (IoT). The existing heartbeat detection system is overcome using a deep convolutional neural network. This helps in the implementation of automatic heartbeat detection and identification of abnormalities. The ECG signals are pre-processed with segmentation and feature extraction techniques. The classification and identification of constraints in the functioning of the heart are identified using optimization algorithms. The proposed system is trained, tested, and evaluated using the MIT-BIH arrhythmia database. The accuracy and efficiency of the proposed system are 99.98% using the MIT-BIH dataset.

Autonomous electric vehicle safety is crucially dependent on the accurate recognition of pedestrians in diverse situations. Current pedestrian detection techniques, however, face significant limitations due to reduced visibility and poor-quality images under low-lighting scenarios. With the aim of overcoming these challenges, this article proposes a novel, sustainable method for pedestrian detection and classification in electric vehicles using machine learning techniques. The approach processes video frame-based images as input, removing noise and smoothing the images for improved detection. A Bayesian component network analysis is employed to refine the features of the filtering-based boundary box detection, further enhancing the detection process. The selected features are then classified using a fully connected kernel operation based on the region with reward Q-Reinforcement architecture, resulting in a secure and efficient pedestrian detection system. The proposed method was evaluated on multiple image datasets using average precision, an area under the curve (AUC), log-average miss rate (MR), and root-mean-square error (RMSE) as performance measures. The experimental results demonstrated an average precision of 92%, MR of 48%, AUC of 56%, and RMSE of 61%. These findings indicate that the proposed technique effectively enhances pedestrian detection and classification for autonomous electric vehicles, contributing to increased safety and reliability in real-world applications.

Student’s attendance is important factor all the time in educational institution because a single absent is big difference in performance and disciplinary related activities. Making attendance with high accuracy is important factor even though there are various ways to mark student’s attendance in modern era. Face recognition-based attendance tracking system is a popular way nowadays introduced in colleges and schools. There are two problems associated with the automated attendance management system is it requires an administrator to monitor the attendance of students. Generally, it’s very difficult to handle with large number of students. Another problem with the system is record need to be maintained from forgery. Blockchain technology is decentralized management useful for protect sensitive data. The goal of the proposed work is to provide a web-based application with a notification system that allows mentors to keep track of their mentees’ attendance while sitting in their place. The work is divided into two parts: first, automatic face detection and analysis using the CNN model; and second, notification systems and the production of logs to consider. The generated log is maintained in block chain network.

Cloud storage systems may be used by users and businesses to transfer their enormous volumes of data for storage, processing, and analysis. Cloud storage may be used efficiently by employing the deduplication technique to prevent duplicate copies. Users may save any type of material, including audio, video, images, text, and more. The handling of deduplication techniques for these distinct data types may vary depending on their characteristics, sizes, etc. Video deduplication is one of these, and it's essential for reducing memory waste in the cloud storage system. There are many levels of video deduplication that may be done. In this research work, we proposed a GOP-level deduplication system using an adaptive GOP structure. Since GOP is the level of deduplication, it is inefficient to build GOP with a fixed size. It can fail to discover a pair of identical frames. The adaptive GOP structure will yield GOPs with closer relation among their frames than with a fixed-size GOP structure. The proposed technique is compared with the fixed-size GOP structure with the GOP sizes of 8, 10, 12, and 15 and the proposed technique achieved a 2.18% PSNR gain which is relatively higher than other fixed-size GOP structures.

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 C. Raghavendra, K. Shyam Sundar Reddy, M. Shanmugathai, A. Devipriya; Electron microscopy images for automatic bacterial trichomoniasis diagnostic classification separating and sorting of overlapping microbes. AIP Conference Proceedings 30 January 2023; 2523 (1): 020089. https://doi.org/10.1063/5.0110989 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

This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are somany research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual-atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRESASPP- Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage. © 2022 Tech Science Press. All rights reserved.

Our modern societies cannot function without transportation system. Individual freedom of movement, business, and the expansion of economies around the world are all directly impacted by the efficiency of transportation infrastructure. An intelligent transportation system, the internet of vehicles is a component of the overall system. Many technologies and algorithms have been developed to optimize traffic management. In this paper, the optimal route on a map is discovered using an ant colony algorithm. The suggested route selection mechanism based on IOV was found to function well in tests. Segmented maps are used instead of the complete map to identify important routes. This paper was used to make the roads safer, the transportation system more efficient, and the environment better in our system.

Recently, humans are lost their life due to respiratory failure. At present in India, the coronary scene passing percentage will be 47%. This happened with a delayed consequence of people eating habit, age factor, turn of events and different parts. The essential driver of death during respiratory disappointment isn't giving hope to the patient. With continually noticing this beat rate & sweat of a patient, respiratory disappointment will be recognized. Aim of the recommendation is used to build up a very high-benefit & insignificant exertion contraption which is used to calculate the amount of beat pounds each patient snapshot & perceiving a cardiovascular failure by placing the sensors on any one of the finger (excluding the thumb) or any point in the human body that the heart beat can be identified or surveyed from, and along these lines demonstrating the output of the persistent screen of the Arduino IDE. In order to provide the performance accuracy of the given system, a sensor will be used to identify the sweat. Suppose any abnormalities occurred, the GSM (Global System for Mobile Communications) module can be impelled & it will send an alarm message to the adaptable device which can be given to Arduino code. The nearest centre of the patient is identified with the Google Maps API then the details are send to the family members. In addition to this, Electronic Health Records (EHR) will be shared to the nearest hospital for tracking or monitoring the patient health history. This implementation performs careful distinctive evidence & track messages. This design will help the people to save their life in a short period of time.

Industrial Air pollution that acts on our everyday activities and general well being. This thing constitutes a warning via the system and therefore the general well being on the earth. The terrible need to be compelled to detect the quality of ambient air is incredibly obtrusive, due to the rise in factory-made pursuit over the latest years. Folks have to be compelled to grasp the extent on which their pursuit has an effect on quality of ambient air. This paper came up with the Associate in Industrial Air pollution observance approach. This approach is being expanded to exploit the Arduino microcontroller. This Industrial pollution observance approach is sketched to analyze quality of ambient air in period. The clean air is estimated by the designed approach to be precise. The consequence is shown on the created apparatus exhibit port along with accessed cloud on any sensible portable accessory. This paper offers the improvement of pollution tracking systems with deployment of intelligent sensors. The proposed approach finds application in industry and additionally in monitoring of causes like a fan, motors.

A subject of extensive research interest in the Brain Computer Interfaces (BCIs) niche is motor imagery (MI), where users imagine limb movements to control the system. This interest is owed to the immense potential for its applicability in gaming, neuro-prosthetics and neuro-rehabilitation, where the user's thoughts of imagined movements need to be decoded. Electroencephalography (EEG) equipment is commonly used for keeping track of cerebrum movement in BCI systems. The EEG signals are recognized by feature extraction and classification. The current research proposes a Hybrid-KELM (Kernel Extreme Learning Machine) method based on PCA (Principal Component Analysis) and FLD (Fisher's Linear Discriminant) for MI BCI classification of EEG data. The performance and results of the method are demonstrated using BCI competition dataset III, and compared with those of contemporary methods. The proposed method generated an accuracy of 96.54%.

Eye state ID is a sort of basic time-arrangement grouping issue in which it is additionally a problem area in the late exploration. Electroencephalography (EEG) is broadly utilized in a vision state in order to recognize people perception form. Past examination was approved possibility of AI & measurable methodologies of EEG vision state arrangement. This research means to propose novel methodology for EEG vision state distinguishing proof utilizing Gradual Characteristic Learning (GCL) in light of neural organizations. GCL is a novel AI methodology which bit by bit imports and prepares includes individually. Past examinations have confirmed that such a methodology is appropriate for settling various example acknowledgment issues. Nonetheless, in these past works, little examination on GCL zeroed in its application to temporal-arrangement issues. Thusly, it is as yet unclear if GCL will be utilized for adapting the temporal-arrangement issues like EEG vision state characterization. Trial brings about this examination shows that, with appropriate element extraction and highlight requesting, GCL cannot just productively adapt to time-arrangement order issues, yet additionally display better grouping execution as far as characterization mistake rates in correlation with ordinary and some different methodologies. Vision state classification is performed and discussed with KNN classification and accuracy is enriched finally discussed the vision state classification with ensemble machine learning model.

As per Health Insurance Portability and Accountability Act (HIPAA)the patient's protection and protection are significant in assurance of medical care protection. Simultaneously, the quantity of maturing populace is developing essentially. Purpose Of-care in medical clinics utilized generally around the globe. The Security Regulations are represented in order to provide data veracity, privacy, and accessibility. Consequently, patient’s ECG along with other physiological signals, for example, temperature, pulse, glucose reading, position, and so forth, were gathered by utilizing Body Sensor Networks (BSNs) and transmitted. At a similar cost, understanding protection is ensured against stalkers while information direct in vulnerable organization and placed in medical clinic workers. Likewise, the accompanying system was consolidated in this venture: (1) encryption and decryption for information classification and trustworthiness (2) ECG based Steganography to trade information. Our plan additionally guarantees security, efficiency, and scalability.