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Innovative Method for Detecting Liver Cancer using Auto Encoder and Single Feed Forward Neural Network
Liver cancer ranks sixth among all cancers in frequency of incidence. A CT scan is the gold standard for diagnosis. These days, CT scan images of the liver and its tumor can be segmented using deep learning and Neural Network techniques. In this proposed approach to identifying cancer cells, it's focus on four important areas: To enhance a photo by taking out imperfections and unwanted details. An ostu method is used for this purpose. Specifically, this proposed approach to use the watershed segmentation technique for image segmentation, followed by feature extraction, in an effort to isolate the offending cancer cell. After finishing the model training with AE-ELM. To do this, Extreme Learning Machine incorporates an auto encoder. To achieve effective and supervised recognition, the network's strengths of Extreme Learning Machine (ELM) are thoroughly leveraged, including its few training parameters, quick learning speed, and robust generalization ability. The auto encoder-extreme learning machine (AE-ELM) network has been shown to have a respectable recognition impact when the sigmoid activation function is used and the number of hidden layer neurons is set to 1200. According to the results of this investigation, a method based on AE-ELM can be utilized to detect the liver tumor. As compared to the CNN and ELM models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE. -
An Intelligent Hybrid GA-PI Feature Selection Technique for Network Intrusion Detection Systems
The development of Network Intrusion Detection Systems (NIDS) has become increasingly important due to the growing threat of cyber-attacks. However, with the vast amount of data generated in networks, handling big data in NIDS has become a major challenge. To address this challenge, this research paper proposes an intelligent hybrid GA-PI algorithm for feature selection and classification tasks in NIDS using support vector machines (SVM). The proposed approach is evaluated using two sub-datasets, Analysis and Normal, and Reconnaissance and Normal, which are generated from the publicly available UNSWNB-15 dataset. In this work, instead of considering all possible attacks, the focus is on two attacks, emphasizing the importance of the feature selection agent in determining the optimal features based on the attack type. The experimental results show that the proposed hybrid feature selection approach outperforms existing methodologies in terms of accuracy and execution time. Moreover, the selection of features can be subjective and dependent on the domain knowledge of the researcher. Additionally, the proposed approach requires computational resources for feature selection and classification tasks, which can be a limitation for resource-constrained systems. To be brief, this research paper presents a promising approach for feature selection and classification tasks in NIDS using an intelligent hybrid GA-PI algorithm. While there are some challenges and limitations, the proposed approach has the potential to contribute to the development of effective and efficient NIDS. 2023, Ismail Saritas. All rights reserved. -
Intelligent machine learning approach for cidscloud intrusion detection system
In this new era of information technology world, security in cloud computing has gained more importance because of the flexible nature of the cloud. In order to maintain security in cloud computing, the importance of developing an eminent intrusion detection system also increased. Researchers have already proposed intrusion detection schemes, but most of the traditional IDS are ineffective in detecting attacks. This can be attained by developing a new ML based algorithm for intrusion detection system for cloud. In the proposed methodology, a CIDS is incorporated that uses only selected features for the identification of the attack. The complex dataset will always make the observations difficult. Feature reduction plays a vital role in CIDS through time consumption. The current literature proposes a novel faster intelligent agent for data selection and feature reduction. The data selection agent selects only the data that promotes the attack. The selected data is passed through a feature reduction technique which reduces the features by deploying SVM and LR algorithms. The reduced features which in turn are subjected to the CIDS system. Thus, the overall time will be reduced to train the model. The performance of the system was evaluated with respect to accuracy and detection rate. Then, some existing IDS is analyzed based on these performance metrics, which in turn helps to predict the expected output. For analysis, UNSW-NB15 dataset is used which contains normal and abnormal data. The present work mainly ensures confidentiality and prevents unauthorized access. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
A comprehensive review of AI based intrusion detection system
In today's digital world, the tremendous amount of data poses a significant challenge to cyber security. The complexity of cyber-attacks makes it difficult to develop efficient tools to detect them. Signature-based intrusion detection has been the common method used for detecting attacks and providing security. However, with the emergence of Artificial Intelligence (AI), particularly Machine Learning, Deep Learning and ensemble learning, promising results have been shown in detecting attacks more efficiently. This review discusses how AI-based mechanisms are being used to detect attacks effectively based on relevant research. To provide a broader view, the study presents taxonomy of the existing literature on Machine Learning (ML), Deep learning (DL), and ensemble learning. The analysis includes 72 research papers and considers factors such as the algorithm and performance metrics used for detection. The study reveals that AI-based intrusion detection methods improve accuracy, but researchers have primarily focused on improving performance for detecting attacks rather than individual attack classification. The main objective of the study is to provide an overview of different AI-based mechanisms in intrusion detection and offer deeper insights for future researchers to better understand the challenges of multi-classification of attacks. 2023 -
Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of Machines in Manufacturing Industry
Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance. 2023 IEEE. -
Framework for Controlling Interference and Power Consumption on Femto-Cells In-Wireless System
Utilization of femto-cells is one of the cost effective solution to increase the internal network connectivity and coverage. However, there are various impediment in achieving so which has caused a consistent research work evolving out with solution. Review of existing literature shows that maximum focus was given for energy problems in cellular network and not much on problems that roots out from interference. Therefore, the proposed system has presented a very simple and novel approach where the problems associated with interference and energy in using large groups of femto-cells are addressed. Adopting analytical research methodology, the proposed model offers on-demand utilization of the selective femto-cells on the basis of the traffic demands. The study outcome shows that proposed system offers better performance in contrast to existing approach. Springer Nature Switzerland AG 2019. -
Cultural Expression of Anxiety Symptoms in Kannada Language: A Qualitative Study
Background: In anxiety disorders, culture is important in symptom presentation and help-seeking. Most tools for anxiety disorders are not validated in India and thus might not capture culture-specific aspects of anxiety. This study aims to identify and generate culturally specific terms to describe symptoms of anxiety as part of the development of the Kannada version of the Panic and Anxiety National Indian Questionnaire (PANIQ). The PANIQ is a tool under development to identify anxiety and panic in Indian settings. Methods: This study used qualitative methods like focus group discussions (FGDs) and in-depth interviews (IDIs) to identify and generate items related to anxiety and panic in Kannada from stakeholders like individuals with anxiety disorders, their caregivers, healthcare workers, and mental health professionals who treat individuals with anxiety and panic disorders. Five FGDs (n = 28), one triad (n = 3), and 34 IDIs (n = 34) were conducted. Results: The mean age of the participants was 38.9 (standard deviation: 12.28) years; 57.1% were from rural areas. We generated 615 Kannada items. These were classified into 21 domains and facets. Items in domains like Somatic symptoms, Fear, and Impairment in day-to-day life were higher than those noted in existing tools for anxiety that focus more on cognitive symptoms of anxiety. Conclusions: This study generated culturally specific items of anxiety through a qualitative process of tool development incorporating subjective experiences of persons with anxiety disorders and other stakeholders. This is among the first steps toward the development of PANIQ. 2022 The Author(s). -
The Creation of Intelligent Surfaces for the Purpose of Next-Gen Wireless Networks
In preparation of the changing environment of 5th wave (5G) and prospective networks of cells, this study explores new methods to meet challenges that result from the erratic character of the communication medium. Traditionally viewed as a chance factor, the relationship between broadcast radio waves with surrounding factors lowers signal quality in modern times of wireless communications. This paper performs a full literature review on customizable autonomous surfaces (RISs) alongside their uses, stressing the chance for network managers to control radio wave features and minimize environmental spread problems. RISs allow effective control over waveform parameters, including the amplitude, phase, number, and polarization, that without needing complex encoder, decoder, or radio wave processing methods. Leveraging technical developments, metasurfaces, reflectarrays, phase shifts, and liquid crystals appear as potential options for RIS application, placing them as pioneers in the realization of 5G as well as subsequent networks. The study dives into current actions in the RIS-operated mobile phone network area and covers core research issues that deserve exploration to feed unlocking the full promise of RISs at wireless communication networks. 2024 IEEE. -
Recent developments in bandwidth improvement of dielectric resonator antennas /
International Journal of RF And Microwave Computer-Aided Engineering, Vol.29, Issue 6, pp.1-17 -
AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images
A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy. 2023 Elsevier Ltd -
Stress Management among Employees in Information Technology Sector Using Deep Learning
Information technology is one of the areas in India that is developing the quickest India's information technology (IT) administrations industry has become more merciless. The information technology area has been managing additional difficult issues like specialized development, administration enhancement, and worldwide overhauling starting from the beginning of this long period. Along these lines, it is unimaginable for everybody to adjust to the moving difficulties they experience in the field of information technology, which causes stress. Stress is something that individuals battle with for most of their lives. Albeit the information technology (IT) industry is notable for its hazardous turn of events and development, it is likewise portrayed by high worker burnout and stress levels. This theoretical proposes an original strategy for overseeing stress in the IT business that utilizes deep learning methods. This study utilizes deep learning calculations to expect, distinguish, and decrease stress makes all together location the earnest issue of stress among IT experts. The principal objective is to make a shrewd framework that can help organizations proactively recognize stress-related issues in their labor force and proposition specific cures. 2024 IEEE. -
Prioritizing Factors Affecting Customers Satisfaction in the Internet Banking Using Artificial Intelligence
Internet banking has revolutionised the way customers interact with their banks, providing them with convenient access to a wide range of financial services from the comfort of their homes or mobile devices. Customer satisfaction the success of an endeavour is contingent upon a vital component internet banking Service provision, as it pertains directly impacts customer retention and loyalty. This research explores the application of artificial intelligence (AI) techniques, specifically random forest and convolutional neural networks (CNN), to prioritise the factors that affect customer satisfaction in internet banking. The study begins with data collection from a diverse sample of internet banking customers, including demographic information, transaction history, and customer feedback. These may include the ease of navigation, the response time of the platform, and the level of trust in the bank's security measures. Furthermore, convolutional neural networks (CNN) are utilised to analyse unstructured data such as customer feedback and reviews. By applying natural language processing techniques, CNN s extract sentiment and topic information from customer comments. This approach can ultimately lead to improved customer retention and loyalty, ensuring the long-term success and competitiveness of internet banking platforms. In conclusion, this study showcases the power of AI, specifically Random Forest and CNN, in prioritising factors affecting customer satisfaction in internet banking. It highlights the significance of using both quantitative and qualitative investigations in order to attain a comprehensive comprehension of customer sentiments and preferences in the digital banking landscape. 2024 IEEE. -
Smart Product Packing and IoT Marketing: Enhancing Customer Interaction
The convergence of smart product packaging and IoT marketing has transformed commerce. This study examines the fundamental ramifications of convergence and its potential to improve customer engagement. Our research shows the transformational potential of these technologies via quantitative and qualitative analyses.Smart packaging outperforms non-smart items, giving firms an advantage, according to quantitative data. Regression and correlation analysis confirm IoT data-customer interaction. Our study also emphasizes ethical data acquisition, which supports data privacy and consumer protection.Consumers may expect personalized experiences, transparency, and real-time feedback from this technology transformation. Smart product packaging and IoT marketing enable readers to make educated decisions and influence product development to meet changing consumer expectations.This research allows academics to study the ideas and models that affect consumer engagement. Data privacy and consumer protection may inform IoT marketing and smart device packaging policy.Our research guides organizations and customers towards better customer interactions, data-driven decision-making, and ethical data practices in this changing age. The future promises revolutionary customer contact. 2023 IEEE. -
Nexus Between Interest Rate Risk and Economic Value of Equity of Banks
This analytical study looks to provide recommendations to the banking sector on different policies and regulations by examining certain aspects of the Basel III accord, which was designed to manage specific operational, capital and market risks of banks. A review of extant literature reveals that only a few papers have been written on simulation-based approaches, using basis and re-pricing risks. We look to connect this as a source while attempting to define and measure the impact of interest rate risk (IRR) on the economic value of equity (EVE) of banks. We propose to use the driverdriven method, wherein interest rate shocks are derived through prime lending rate (PLR) for the period of 20162019 in the context of India. Monte Carlo Simulation and OLS regression was performed to predict the IRR; Granger causality was used to examine the cause and effect relationship; the impulse response function (IRF) was used for sensitivity analysis; and the vector error correction model (VECM) technique was used for co-integrating relationships. Notably, the EVE movement caused due to shocks in interest rates had to be traced as it envisages probable EVE losses. Importantly, our study is among the first few to show the relationship between IRR and EVE of banks, especially after the deregulation of Indian banking sector. 2021 International Management Institute, New Delhi. -
Vimana /
Patent Number: 202241030155, Applicant: Ramesh Chandra Poonia.
Drone navigation works by building a map of its surroundings while tracking its position within the map. This allows the drone to demonstrate positional accuracy (the global average URE (User error rate) across all satellites) of < 0.643 m (2.1 ft.) 95% of the lime using the Global Positioning System (GPS). The problem with this technology is twofold. It deploys only L band communication in practice. -
Message efficient ring leader election in distributed systems
Leader Election Algorithm, not only in distributed systems but in any communication network, is an essential matter for discussion. Tremendous amount of work are happening in the research community on election as network protocols are in need of co-ordinator process for the smooth running of the system. These so called Coordinator processes are responsible for the synchronization of the system otherwise, the system loses its reliability. Furthermore, if the leader process crashes, the new leader process should take the charge as early as possible. New leader is one among the currently running processes with the highest process id. In this paper we have presented a modified version of ring algorithm. Our work involves substantial modifications of the existing ring election algorithm and the comparison of message complexity with the original algorithm. Simulation results show that our algorithm minimizes the number of messages even in worst case scenario. 2013 Springer Science+Business Media. -
Mechanisms towards enhanced quality of experience (QoE) in fog computing environments
The Fog or Edge computing emerges as one of the important paradigms for setting up and sustaining smarter environments across industry verticals. Our everyday environments are being meticulously advanced and accentuated through a bevy of edge and digital technologies and tools in order to be situation-aware and sophisticated. On the other side, we have powerful Cloud environments contributing as the one-stop IT solutions not only for business automation but also for people empowerment. Compared to the number of prospective Cloud environments, the number of Fog environments is going to be quite large with the availability of billions of connected devices. The scope of Fog environments, which are being touted as the most crucial for empowering people and in their everyday activities, is bound to escalate in the days to unfurl. The immediate challenge for Fog environments is to drastically enhance the quality of experience (QoE) for users. Academic professors and industry professionals have come out with a number of solution approaches and algorithms. This chapter is being specially prepared and presented in this book to tell all about the role and responsibility of Fog computing environments, the unique use cases and the various challenges, etc. Furthermore, the significance of QoE is described in detail and how that requirement can be attained by smartly applying various proven and potential technologies. This chapter also aims to motivate the readers and researchers to dig deep into this new critical requirement to unearth pioneering solutions towards enhanced QoE. Springer International Publishing AG, part of Springer Nature 2018. -
Comparative Study on Load Balancing Techniques in Distributed Systems
International Journal of Information Technology and Knowledge Management, Vol-6 (1), pp. 53-60. ISSN-0973-4414 -
Imidazopyridine Hydrazine Conjugates as Potent Anti-TB Agents with their Docking, SAR, and DFT Studies
Novel imidazopyridines hydrazine conjugates were designed and synthesized for their anti-tubercular (anti-TB) activity. A cytotoxicity assay was conducted with Vero cells to determine the safety profile of the most effective compounds. It was found that compound (IA3) (MIC=0.78 ?M) and (IA8) (MIC=1.12 ?M) were nearly 3.7 and 2.5 times more active than pyrazinamide. Based on Density functional theory (DFT), these molecules exhibited better charge transfer between molecular orbital's, which made them suitable for biological applications. Molecular docking on Mycobacterium tuberculosis InhA bound to NITD-916 (PDB: 4R9S) revealed that compounds possessed greater binding affinity towards proteins. In addition, the most active anti-TB compounds (IA3) and (IA8) exhibited high levels of interaction with the target protein and exceptional safety profile, suggesting they may prove to be effective leads for new drugs. 2024 Wiley-VCH GmbH. -
Imidazopyridine chalcones as potent anticancer agents: Synthesis, single-crystal X-ray, docking, DFT and SAR studies
New imidazopyridinechalcone analogs were synthesized through the ClaisenSchmidt condensation reaction. The newly synthesized imidazopyridine-chalcones (S1S12) were characterized using spectroscopic and elemental analysis. The structures of compounds S2 and S5 were confirmed by X-ray crystallography. The global chemical reactivity descriptor parameter was calculated using theoretically (DFT-B3LYP-3-211, G) estimated highest occupied molecular orbital and lowest unoccupied molecular orbital values and the results are discussed. Compounds S1S12 were screened on A-549 (lung carcinoma epithelial cells) and MDA-MB-231 (M.D. Anderson-Metastatic Breast 231) cancer cell lines. Compounds S6 and S12 displayed exceptional antiproliferative activity against lung A-549 cancer cells with IC50 values of 4.22 and 6.89 M, respectively, compared to the standard drug doxorubicin (IC50 = 3.79 ?M). In the case of the MDA-MB-231 cell line, S1 and S6 exhibited exceptionally superior antiproliferative activity with IC50 of 5.22 and 6.50 ?M, respectively, compared to doxorubicin (IC50 = 5.48 ?M). S1 was found to be more active than doxorubicin. Compounds S1S12 were tested for their cytotoxicity on human embryonic kidney 293 cells, which confirmed the nontoxic nature of the active compounds. Further molecular docking studies verified that compounds S1S12 have a higher docking score and interacted well with the target protein. The most active compound S1 interacted well with the target protein carbonic anhydrase II in complex with pyrimidine-based inhibitor, and S6 with human Topo II? ATPase/AMP-PNP. The results suggest that imidazopyridine-chalcone analogs may serve as new leads as anticancer agents. 2023 Deutsche Pharmazeutische Gesellschaft.