Global Academy of Technology

The Internet of Things (IoT) is a technology that extends the reach of the Internet to items in the real world, encompassing both living and non-living objects. The Internet serves as a platform for linking people and computers. Things are real-world items such as automobiles, schools, animals, heart rate monitors, and other household equipment, amongst many more examples. Since the previous several decades, scientists have been working on developing devices, networks, and protocols that may be used to monitor, control, and track real-world objects and events. Sensors, actuators, and Radio Frequency Identification Devices (RFID) are the devices that are employed for these reasons, and they are collectively referred to as IoT devices in this thesis. Wireless sensor and actuator networks (WSAN), machine-to-machine networks, and other networks established with the help of these devices are examples of networks built with the help of these devices. Communication between machines (M2M), SCADA, and RFID tracking system, among other things. The collaborative load balancing across fog nodes is proposed in this work, and the load balancing approach is used to disperse the load.

Spherical nanoparticles of zinc oxide (ZnO NPs) were synthesized by an eco-friendly green combustion method using citrate containing Artocarpus gomezianus fruit extract as a fuel. The morphology, compositions and structure of the product were characterized by Powder X-ray Diffraction (PXRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Fourier Transform Infra-red (FTIR), UV–Visible (UV–Vis) and Raman Spectroscopy. Highly uniform spherical zinc oxide NPs were subjected to cytotoxicity, antifungal and antibacterial activities. PXRD patterns show that the product formed belongs to a hexagonal wurtzite system. SEM micrographs reveal that the particles are agglomerated. The TEM images demonstrate that the particles are highly uniform spherical in shape and loosely agglomerated. Scherrer's method and WH plots were used to calculate the average crystallite sizes, yielding 39, 35, 31 and 40, 37, 32 nm for ZnO NPs prepared with 5, 10 and 15 mL of 10% Artocarpus gomezianus fruit extract, respectively. These results were confirmed by the TEM observations. Breast cancer cell lines (MCF-7) were subjected to in vitro anticancer activity. MTT assay revealed a good anticancer activity of ZnO NPs against MCF-7. Zone of the inhibition method shows that the spherical ZnO NPs also exhibit significant antibacterial activity against staphylococcus aureus and antifungal activity against Aspergillus niger. The synthesized ZnO NPs can find plausible biological applications.

Polycystic ovarian syndrome (PCOS) is a hormonal disorder found in women of reproductive age. There are different methods used for the detection of PCOS, but these methods limitedly support the integration of PCOS and mental health issues. To address these issues, in this paper we present an automated early detection and prediction model which can accurately estimate the likelihood of having PCOS and associated mental health issues. In real-life applications, we often see that people are prompted to answer in linguistic terminologies to express their well-being in response to questions asked by the clinician. To model the inherent linguistic nature of the mapping between symptoms and diagnosis of PCOS a fuzzy approach is used. Therefore, in the present study, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is evaluated for its performance. Using the local yet specific dataset collected on a spectrum of women, the Fuzzy TOPSIS is compared with the widely used support vector machines (SVM) algorithm. Both the methods are evaluated on the same dataset. An accuracy of 98.20% using the Fuzzy TOPSIS method and 94.01% using SVM was obtained. Along with the improvement in the performance and methodological contribution, the early detection and treatment of PCOS and mental health issues can together aid in taking preventive measures in advance. The psychological well-being of the women was also objectively evaluated and can be brought into the PCOS treatment protocol.

Due to the advancements in the lifestyle, stress builds enormously among individuals. A few recent studies have indicated that stress is a major contributor for infertility and subsequent ovarian cancer among women of reproductive age. In view of this, the present study proposes a two-stage computational methodology to identify and segment the ovarian tumour and classify it as benign or malignant. Using computerized tomography images, the first stage involves image segmentation using inverted fuzzy c-Means clustering, and second stage consists of deep quantum convolutional neural network in order to detect the tumours. The efficacy of the proposed method is demonstrated using in-house clinically collected dataset by comparing the results with the state-of-the-art methods. The experimental results confirm that the proposed approach outperforms the existing fuzzy C means algorithm by achieving the average Jaccard score of (0.65, 0.84, 0.79) (min, max, avg) and Dice score of (0.70, 0.83, 0.77) (min, max, avg), classification result of 78% for benign and 70.03% for malignant tumours. The classification results using the variant of convolutional neural network (CNN) model ResNet16 are compared with the quantum convolutional neural networks (QCNN) and obtained the classification performance of 87.02% for benign and 79.4% for malignant tumours and 84.4% for benign and 77.03% for malignant tumours respectively.

Research is considering security of big data and retaining the performance during its transmission over network. It has been observed that there have been several researches that have considered the concept of big data. Moreover, a lot of those researches also provided security against data but failed to retain the performance. Use of several encryption mechanisms such as RSA [43] and AES [44] has been used in previous researches. But, if these encryption mechanisms are applied, then the performance of network system gets degraded. In order to resolve those issues, the proposed work is making using of compression mechanism to reduce the size before implementing encryption. Moreover, data is spitted in order to make the transmission more reliable. After splitting the data contents data has been transferred from multiple route. If some hackers opt to capture that data in unauthentic manner, then they would be unable to get complete and meaning full information. Thus, the proposed model has improved the security of big data in network environment by integration of compression and splitting mechanism with big data encryption. Moreover, the use of user-defined port and use of multiple paths during transmission of big data in split manner increases the reliability and security of big data over network environment.

Effective and low-cost CuO/TiO₂ nanocomposites were prepared at room temperature by a surfactant-assisted sol–gel method for photocatalytic activities under UV-visible and visible light irradiations.

Effective research has been aimed at increasing the distributed compute dependent Software Define Network (SDN) with high-level Intelligent - Internet of Things (I-IoT). Wireless sensor networks come with a set of resource restrictions. Still, only a few functions are often configured such as energy restraint and the concerted demands that are vital for IoT application routing performance. A major technique for solving the expansion of network scalability by applying Mobile Sink (MS). The construction of data transmission optimal path, the detection of an optimal set data-gathering points ODG and MS scheduled with dynamic networks for energy-efficient techniques, that the network’s lifetime in enormous complications, principally in large-scale IoT networks. The research work proposes an Research Objective: i) Develop an energy-efficient routing technique for large-scale I-IoT networks within a cloud-based SDN system. ii) Optimize network scalability, lower-level routing, and load balancing using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The prime aim of cloud-based SDN with AI is to determine: a lower level routing in the perception layer, a load-balanced Cluster Table (CT), an optimal ODG points, and MS optimal paths OMSpath. The main contribution of proposed routing is i) Energy Minimization (EM): The proposed routing minimizes energy dissemination by the Cluster Head (CH) in critical conditions (EM-CH). ii) Enhanced Energy Balance (EEB): The EC-based SDN, considering both Optimal Data-Gathering (ODG) and Mobile Sink (MS) advancements, achieves enhanced energy balance during network routing (EEB-SDN). Research results validate the proposed model stability that improves the network lifetime up to 63%, the energy usage in the network is reduced up to 78%, the high volume data loaded to the MS up to 95%, and the delay of the OMSpath by 69% when compared with various model.

Abstract The classification of tumours into benign and malignant continues to date to be a very relevant and significant research topic in the cancer research domain. With the advent of Computer Vision and rapid developments in the fields of deep learning, as well as medical devices and instruments, researchers are therefore utilizing the state‐of‐the‐art deep learning architectures to discover patterns in the medical image data and thereby use this information to detect tumours and classify them as benign or malignant. In this paper, we propose a custom state‐of‐the‐art deep learning architecture, the Inception‐ResNet v2 for the classification of ovarian tumours into the two categories of benign and malignant based on a custom dataset with a validation accuracy of 97.5% and a test accuracy of 67%. Furthermore, a quantum convolutional neural network (QCNN) was also implemented with an accuracy of 92% on the validation dataset.

Au/TiO₂ NPs have been successfully prepared <italic>via</italic> ionic liquid assisted hydrothermal method and utilizing Au/TiO₂ NPs for photocatalytic hydrogen production and photochemical and electrochemical reduction of Cr⁶⁺ to Cr³⁺.

Abstract Medical image analysis and disease diagnosis have significantly improved with the use of AI and Machine Learning algorithms. Automated systems for medical image analysis will help the doctors and radiologists understand the anomaly in a short span of time and with better visualization. Such automated systems will help to reduce the time taken for diagnosis by experts. Recently, Computer Vision is industrialized with the advancements in algorithms and hardware. The proposed study aims to develop a computer vision solution for automatic segmentation and classification of ovarian tumours in discriminating between benign and malignant tumours by image‐to‐image translation approach using Conditional Generative Adversarial Network (cGAN). Our method uses a novel algorithm which segments and classifies the images in a single pipeline which makes the algorithm unique and useful. This research also aims to compare its diagnostic accuracy with that of an expert radiologist. The dataset used by in the present study is formulated with images obtained from a hospital and annotated by doctors from the hospital. The obtained results show the proposed study is promising for ovarian cancer segmentation and classification with an average segmentation score of 0.825 for benign and 0.765 for malignant and classification accuracy of 83% for benign and 79% for malignant, precision score of 85% for benign and 80% for malignant and F1 score of 81% for benign and 80.1% for malignant images. The proposed methodology is evaluated on the existing MRI images to perform segmentation and classification. The results obtained shows that the proposed methodology can perform well on other MRI images. In this study, proposed methodology is convenient as separate segmentation need not be done and is giving good result. The same MRI images are segmented using UNet and classified using RESNET 101 and results are compared with the proposed methodology.

Abstract The utilization of distributed computing has expanded quickly in numerous associations. Distributed computing gives numerous advantages regarding the ease and availability of information. Guaranteeing the security of distributed computing is a the central point in the distributed computing condition, as clients regularly store delicate data with cloud capacity suppliers, however, these suppliers might be untrusted. Managing ”single cloud” suppliers is anticipated to turn out to be less famous with clients due to dangers of administration accessibility disappointment and the probability of vindictive insiders in the single cloud. A the development towards ”multi-mists”, or at the end of the day, ”inter clouds” has developed as of late. High-Availability and Integrity Layer (HAIL), a conveyed cryptographic framework that allows a set of workers to demonstrate to a customer that a put-away record is flawless what’s more, retrievable. HAIL fortifies, officially brings together, what’s more, smoothes out unmistakable methodologies from the cryptographic furthermore, dispersed frameworks networks. Evidence in HAIL are proficiently process able by workers and profoundly reduced regularly tens or many bytes, regardless of record size. This paper reviews the ongoing examination identified with single what’s more, multi-cloud security and addresses conceivable arrangements. It is discovered that the examination into the utilization of multi-cloud suppliers to keep up security has gotten less consideration from the examination network than has the utilization of single mists. This work means to advance the utilization of multi-mists because of its capacity to decrease security dangers that influence the distributed computing client.

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model's effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model's decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools.

Various types of electrical connectors are available globally. Electrical connectors are one of the critical components in many applications, including the automotive sector. They are primarily used to supply high power (or high currents) to other connected devices, such as batteries, electronics, and motors. in both conventional and hybrid/electric vehicles. Finite element modeling (FEM) is one of the tools through which better electrical connector designs can be created, and thus, enhanced performance of these connectors can be achieved. This article presents a comprehensive review of the FEM of electrical connectors performed until now in both the bulk regions as well as the contact regions by researchers worldwide.

In this chapter, the Internet of Things (IoT) system is required for automating irrigation systems and monitoring real-time data from sensors. IoT systems may easily and affordably integrate the long-range wide-area network (LoRaWAN). Four irrigation strategies, including ET (ETc), MP60 (watermark 200SS-5 soil matric potential sensors, (-70 kPa), MP50 (at -50 kPa)), and GesCoN (a decision support system), were developed and put to the test. According to the findings, treatment MP70 had a marketable yield that was greater by 16 percent and 24 percent than that of ET and MP50. Due to improper installation and positioning of the soil moisture sensors, MP40 received relatively little water during irrigation. The GesCoN and ET results were not significantly different from the MP70 results. It has been demonstrated that using sensors and precision irrigation can help farmers conserve water when growing crops. The LoRaWAN-based IoT system nevertheless performed admirably in terms of power usage, connectivity, sensor reading, and valve management.

The physical identification of tumors may be a laborious and time-consuming process for medical professionals because of the complex nature of the tumor and the noise involution that can occur in magnetic resonance (MR) imaging information. Therefore, determining the location of the tumor at an earlier stage is quite important. The medical scan may track and prognosticate the uncontrolled proliferation of cancer pretentious regions at different levels in order to deliver a felicitous diagnosis at an early time. This is accomplished via the utilisation of segmentation in conjunction with relegation procedures. In order to recognize the tissues of a brain tumor, segmentation of the picture obtained from the MRI is a crucial and challenging step. So, the proposed work includes the tumor segmentation process using Region Growth Algorithm with Gray-Level-Run-Length-Matrix and Centre-Symmetric-Local-Binary-Patterns texture feature extraction process. The segmented images undergo feature extraction process with higher level of accuracy. The performance metrics are measured using accuracy, sensitivity and specificity. The proposed work has 0.97% sensitivity, 0.85% specificity and 99.80% accuracy.

Image Processing nowadays has been commonly used in different medical fields and includes many types of techniques such as storage, communication, presentation, extraction, detection, image gaining, and surgical planning which helps in improving early detection and treatment stage. Early detection is easier in the treatment especially in the case of ovarian cancer. Ovarian cancer has become one among the frequently occuring diseases in 1 in every 8 women these days, this increase in the number of women getting this cancer has drawn the attention of many medical communities. Statistics indicate that ovarian cancer is one of the 10 usual cancers in women. It is the deadliest gynecologic cancer and takes many lives. Early identification of this cancer is very much important for an efficacious diagnosis. There have been many algorithms used to detect ovarian cancer. Here are two algorithms active contour and random walker that are used to detect ovarian cancer and to find the better approach out of them.

The application of HVAC in the present age has affected our lives in much far-reaching range with an extensive variety of applications. HVAC is slowly becoming the pillar of our lifestyle and performs a crucial role in future sustainable growth. Vapour compression refrigeration system is predominantly used in an HVAC sector, which make use of the high-grade energy and causes depletion of ozone layer and global warming due to environmental unfriendly refrigerant. In this regard, ASHRAE has made available a list of low GWP refrigerant for various applications. This paper reviews thermodynamic, flammability properties of these low GWP refrigerants. The compatibility of the low GWP refrigerant with construction material and lubricant is presented.

In the recent era, Image processing has been one of the most commonly used domain in the field of medical science that includes different kinds of procedures namely extraction, image gaining, detection, surgical planning and presentation which indeed helps in giving effective treatment to the patient and using all those techniques, the disease could also be detected at a early stage. When it comes to the case of ovarian cancer, it could be treated successfully when detected at an early stage.As per the following statistics, it is very much important to identify the cancer in the ovaries at the starting stage. Today, there are many algorithms that are being implemented in order to detect the cancer i.e. by segmentation. Here are the three algorithms Random Walker, Active Contour and Watershed that are being implemented in order to segment the ovarian cancer. Finally, a comparative analysis is being performed to identify which gives a better result among the three algorithms.

Abstract Heart disease, known interchangeably as “Cardio Vascular Disease,” blocks the blood vessels in the heart and causes heart attack, chest pain, and stroke. Heart disease is one of the leading causes of morbidity and mortality worldwide and it is one of the major causes of morbidity and mortality globally and a trending topic in clinical data analysis. Assessing risk factors related to heart disease is considered as an important step in diagnosing the disease at an early stage. Clinical data present in the form of electronic health records (EHR) can be extracted with the aid of machine learning (ML) algorithms to provide valuable decisions and predictions. ML approaches also play a vital role in early diagnosis and therapeutic monitoring of heart disease. Several research works have been carried out recently to predict heart disease. To this end, we propose a novel hybrid recurrent neural network (RNN)‐logistic chaos‐based whale optimization (LCBWO) structured hybrid framework for predicting heart disease within 5 years using EHR data. Meanwhile, in the hybrid model established multilayer bidirectional LSTM is used for feature selection, LCBWO algorithm for structural improvement and fast convergence, and LSTM for disease prediction. This research used 10 cross‐validations to obtain generalized accuracy and error values. The findings and observations provided here are focused on the knowledge obtained from the EHR report. The results show that the proposed novel hybrid RNN‐LCBWO framework achieves a higher accuracy of 98%, a specificity of 99%, precision of 96%, Mathews correlation coefficient of 91%, F‐measure of 0.9892, an area under the curve value of 98%, and a prediction time of 9.23 seconds. The accurate predictions obtained from the comparative analysis shows the significant performance of our proposed framework.

The use of the third-generation feedstock for biodiesel production has become increasingly popular over the past decade. Among the various third-generation feedstock identified, biodiesel synthesised from the microalgae attracted the attention of researchers throughout the world. The present research includes a study on the suitability of Schizochytrium microalgae biodiesel as an alternative fuel for the diesel engine. The investigation was carried out on the production, characterisation of Schizochytrium microalgae biodiesel through the transesterification process followed by performance, combustion and emission characteristics of a diesel engine fuelled with Schizochytrium microalgae biodiesel and its blends. The study revealed that the properties of biodiesel were obtained to meet the specified ASTM D6751 standards. The engine performance, combustion and emission characteristics were found to be satisfactory than those of fossil diesel.