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An investigation of the business level strategies in Zimbabwe food manufacturing sector (2006 -2013) /
International Journal Of Science And Research, Vol.3, Issue 6, pp.1052-1063, ISSN No: 2319-7064. -
An Investigation of the Effects of Chronic Stress on Attention in Parents of Children with Neurodevelopmental Disorders
Prolonged exposure to stress can cause impairments in various brain functions including cognition. Attention is one such important cognitive function that is required for our daily life and work-related activities. Chronic stress can have an impact on attention networks such as alerting, executive control, and orienting. The effects of naturalistic, persistent psychosocial stress on several attention networks were explored in this study. Parents of children with neurodevelopmental disorders (NDD) and parents of children with typical development (TD) were given an attention network test (ANT). Overall the stressed group (M= 564.623, SD= 75.484) was found to have a quicker reaction time in all the target and cue conditions whencompared to the non-stressed group (M= 588.874, SD= 101.575). Both groups had similar accuracy in all the conditions. When comparing the three attention network scores, no significantdifference was found in either group. However, in the stressed group, there was a significant beneficial relationship between the alerting and orienting networks (p=.006) and a high negative correlation between the alerting and executive control networks (p=.028). No significant correlation was found between the attention networks in the non-stressed group. Copyright2024 by authors, all rights reserved. -
An Investigation on Machine Learning Models in Classification and Identifications Cervical Cancer Using MRI Scan
This study analyzes the effectiveness of machine learning models in the classification of cervical cancer using a dataset of 900 cancer and 200 non-cancer images gathered from online resources and hospitals. The dataset, covering both CT and MRI images, undergoes rigorous preprocessing, including standardization, normalization, and noise reduction, to enhance its quality for model training. Four machine learning models, namely VGG16, CNN, KNN, and RNN, are recruited to predict cancer and non-cancer cases. During the testing phase, VGG16 emerges as the most accurate, achieving an impressive accuracy of 95.44%, followed by CNN at 92.3%, KNN at 89.99%, and RNN at 86.233%. Performance parameters, such as precision, recall, F1 score, and accuracy, are fully analyzed, providing insights into each model's strengths and capabilities. These discoveries not only contribute to the advancement of cervical cancer diagnostic techniques but also underscore the potential of machine learning in medical imaging. The study emphasizes the relevance of model selection and provides a framework for future research endeavors seeking to enhance the accuracy and performance of cervical cancer diagnosis through the merger of advanced computational techniques with standard diagnostic practices. 2024 IEEE. -
An investigation on structural and optical properties of reduced graphene oxide-tin oxide nanocomposite
Graphene-metal oxide composites have attracted tremendous research interest in recent days due to their unique and fascinating properties. In the present study, rGO and SnO2 were synthesized separately by modified Hummers' method and nitrate-citrate gel combustion technique respectively. One step hydrothermal method was used to prepare reduced graphene oxide-tin oxide nanocomposite of various concentrations of rGO and SnO2.The obtained samples were characterized by XRD, FTIR, Raman Spectroscopy, UV-Vis spectroscopy, SEM and TEM. The results of different characterization techniques showed the successful formation of SnO2, rGO and SnO2-rGO composites. X-ray analysis pattern indicates formation of the SnO2 nanoparticles in the graphene matrix. The size of the particles prepared is in nanoscale and was found to be 10-20 nm range. TEM images reveal the incorporation of crystalline SnO2 nanoparticles in graphene layers. Upon incorporation of tin oxide to graphene matrix, one could easily tailor the energy gap of the composite matrix. 2020 World Research Association. All rights reserved. -
An investigation on structural, electrical and optical properties of GO/ZnO nanocomposite
Coupling of graphene oxide with metal oxide is an effective way to enhance the opto-electric properties of the composite. Herein, a hybrid structure of graphene oxide (GO) -Zinc oxide (ZnO) nanostructure was successfully designed and fabricated with varying concentrations of ZnO. The GO and ZnO nanoparticles were synthesized through Hummer's and simple precipitation method respectively. Structural and physiochemical properties were examined via X-ray powder diffraction, FTIR and UV-Vis spectroscopy. The XRD results of GO showed a peak at 2? of 12.02 with particles of size 6nm and inter layer spacing 0.87 nm. The XRD patterns of ZnO nanoparticles showed a hexagonal unit cell structure and the average dimension of the sample was calculated to be 15 nm. The band gap of the synthesized GO is found to be 5.1 eV and that of ZnO to be 3.07 eV with the help Tauc plot. The dependence of various concentration of ZnO on the electrical behaviour is discussed by an impedance analyzer in the frequency range 100Hz to 1MHz. The ZnO/GO composite with best results have been obtained for 20% and 60 % ratios of ZnO. The composite has high dielectric permittivity and low loss tangent values and is identified as a promising candidate for energy storage applications. 2019 The Authors. -
An Investigation on the Mechanical and Durability Properties of Concrete Structures Incorporated with Steel Slag Industrial Waste
The construction sector constantly looks for novel approaches to promote sustainability, minimize environmental impact and improve structural properties of construction materials. This work explores the incorporation of steel slag, a by-product from steel manufacturing industry, into concrete blocks. This research investigates the effects of steel slag on the mechanical strength and durability of the prepared concrete blocks, through a series of laboratory tests, including compressive, tension, flexure strength, water absorption and acid attack. This study evaluates the viability and feasibility of incorporating steel slag into concrete block production. In this study, samples of concrete mixture were set with 0% to 20% insteps of 5% steel slag as coarse aggregate. The findings show that concrete blocks consisting 20% of steel slag exhibited better compressive, tensile, flexural strength, reduction in water absorption and improved resistance to chemicals. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Investigation to the Hardness of the Cutting Tool During Machining Inconel 718 due to the Cryogenic Effect
The machining of superalloy Inconel 718 has seen a rapid demand in industries due to the superiority factor of its composition which makes it corrosion resistant, wear resistant and abrasive resistant. Due to these advanced features of this alloy, the cutting tool to be used to machine becomes a challenging one. There have been several cutting tools being used in the machine but wear of the tool and high surface roughness has been observed. Two cutting tools Tungsten Carbide RYMX 1004-ML TT3540 and Ceramic AS20 has been identified but the hardness on it is failed due to the machining conditions. The cryogenic treatment of these tools can see a remarkable change in machining and bring low surface roughness and reduce tool wear. 2023 American Institute of Physics Inc.. All rights reserved. -
An invisible race from exclusiveness to inclusiveness of queer employees at workplace
Queer theory has been a significant part of the field of queer studies. Its presence can be found in women's studies, gay and lesbian studies and feminist theory, and postmodern and poststructuralist theories. Many types of research came around during the 1990s. One of the significant studies was in 1991. Teresa de Lauret coined the term "queer theory" to characterize a school of thought that rejected heterosexuality and binary gender constructions favoring a more open view of identity. Michel Foucault and Judith Butler's study is widely regarded as the founding text of this philosophy. This study adopts the lens of gender and sexuality to challenge people's cultural norms and ideals. There is hesitation among people regarding the acceptance of the third gender that exists in society. The queer theory suggests how the rest sees the queer community of the world. While studying the conditions of the queer community in India, it is imperative to undertake the recently legalized Section 377. The Indian Penal Code says that it is no more a crime to have sexual conduct between adults of the same gender as people have no control over their sexual orientation. The study discusses the practices and protocols of transgender inclusion at the workplace and how to look beyond the labels of the LGBT community. There are various issues when the company wants to employ transgender people at the workplace and accept the community. Qualitative research methods will be used in this research by reading several databases and conducting a systematic review. This chapter will also highlight how trans people confront significant job and career-related problems and barriers in the workplace and the concessions employers should make to ensure that trans people have a safe and discrimination-free workplace. This chapter observes how queer theory can be used as a conceptual framework to advance research in organizational research on trans people's several and many times conflicting needs. Ways could be explored to reach their goals around gender transgression and congruency, work, and career, by laying out some of the crucial concepts associated with the study of trans people in the workplace. The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. -
An IoHT System Utilizing Smart Contracts for Machine Learning -Based Authentication
The Internet of Healthcare Things (IoHT) and blockchain technologies have made it feasible to share data in a secure and effective manner, but it is still challenging to ensure the data's veracity and privacy. This paper presents a blockchain authentication method that utilizes Machine Learning (ML) techniques that use smart contracts to ensure the security and privacy of IoHT data. The process utilizes smart contracts to manage access control and ensure data integrity, and deep learning algorithms to identify and validate the accuracy of user data. Furthermore, the approach improves the resilience and dependability of the authentication process and permits secure data ex-change between multiple IoHT systems. The proposed approach provides a potentially revolutionary solution to enhance the safety and confidentiality of IoHT data. It has the potential to fundamentally change how healthcare is provided in the future. 2023 IEEE. -
An Iot Application to Monitor the Variation in Pressure to Prevent the Risk of Pressure Ulcers in Elderly
Pressure sores are a common form of skin problem which occurs with patients who are bedridden or immobile. It is believed that the occurrence of ulcers due to pressure can be prevented. Making best use of resources available and providing comfort to the patient, it is very much important to identify people at risk and provide preventive measures. This work is associated with a method to analyze pressure from pressure points on bedridden patients. A system is presented in this work that continuously monitors the pressure from pressure points using force sensors and sends an alarm to the nurses or caretakers if there is a variation in the pressure exerted on a specific area. 2018 IEEE. -
An iot based wearable device for healthcare monitoring
Nowadays IoT (Internet of Things) devices are popularly used to monitor humans remotely in the healthcare sector. There are many IoT devices that are being introduced to collect data from human beings in a different scenario. These devices are embedded with sensors and controllers in them to collect data. These devices help to support many applications like a simple counting step to an advanced rehabilitation for athletes. In this research work, a mini wearable device is designed with multiple sensors and a controller. The sensors sense the environment and the controller collects data from all the sensors and sends them to the cloud in order to do the analysis related to the application. The implemented wearable device is a pair of footwear, that consists of five force sensors, one gyroscope, and one accelerometer in each leg. This prototype is built using a Wi-Fi enabled controller to send the data remotely to the cloud. The collected data can be downloaded as xlsx file from the cloud and can be used for different analyses related to the applications. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
An IoT-based agriculture maintenance using pervasive computing with machine learning technique
Purpose: In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc. In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset. Design/methodology/approach: In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis. Findings: In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like Threshold segmentation and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained. Originality/value: The implemented machine learning design is outperformance methodology, and they are proving good application detection rate. 2021, Emerald Publishing Limited. -
AN IOT-BASED COMPUTATIONAL INTELLIGENCE MODEL TO PERFORM GENE ANALYTICS IN PATERNITY TESTING AND COMPARISON FOR HEALTH 4.0
Parental comparison and parenthood testing are essential in various legal and medical scenarios. The accuracy and reliability of these tests heavily rely on the gene analysis algorithms used. However, analyzing the quality of succession data are quite challenging due to the presence of detrimental characteristics. To address this issue, we propose using machine learning-based algorithms such as clustering (Correlation-based) and Classification (Modified Naive Bayesian) to separate these characteristics from the parent-child gene array. This progression helps to identify, validate, and select tools, techniques for scrutinizing indecent sequences, leading to accurate and reliable results. In this paper, we present an IoT-based intelligence tool for parental comparison that uses a secure gene analysis algorithm. The model employs multiple sensors and devices to collect genetic data, which is then securely processed and analyzed using contemporary algorithms. The suggested model uses advanced techniques such as encryption and decryption to ensure the privacy and confidentiality of the genetic information. Our experimental consequences reveal that the proposed model is reliable, secure, and provides accurate results. The model has the potential to be used in various legal and medical contexts where the security and reliability of genetic data are critical. 2023 Little Lion Scientific. -
An iot-based fog computing approach for retrieval of patient vitals
Internet of Things (IoT) has been an interminable technology for providing real-time services to end users and has also been connected to various other technologies for an efficient use. Cloud computing has been a greater part in Internet of Things, since all the data from the sensors are stored in the cloud for later retrieval or comparison. To retrieve time-sensitive data to end users within a needed time, fog computing plays a vital role. Due to the necessity of fast retrieval of real-time data to end users, fog computing is coming into action. In this paper, a real-time data retrieval process has been done with minimal time delay using fog computing. The performance of data retrieval process using fog computing has been compared with that of cloud computing in terms of retrieval latency using parameters such as temperature, humidity, and heartbeat. With this experiment, it has been proved that fog computing performs better than cloud computing in terms of retrieval latency. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
An IoT-Based Model for Pothole Detection
Maintenance of the good roads plays a very important role in the growth of the country. Poorly maintained roads can lead to potholes which causes severe accidents. To overcome the damage caused by poor roads, the pothole detection model has been proposed in this paper. In recent days, the Internet of Things (IoT)-embedded models are developed in different applications. The main objective of the proposed work is to design the IoT prototype to collect data which can be used to detect potholes and humps. This prototype is embedded with three sensors, namely accelerometer, ultrasonic sensor, and GPS. The data from these sensors is collected by the controller and transmitted by Wi-Fi module to store in the cloud. The collected data can be downloaded as a spreadsheet from the cloud and can be used for different data analysis applications like pothole notifier application. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An IoT-Based System for Fault Detection and Diagnosis in Solar PV Panels
This abstract describes an IoT-based system for fault detection and diagnosis in solar PV panels. The proposed Fuzzy logic-based fault detection algorithms aims to improve the performance and reliability of solar PV panels, which can be affected by various faults such as shading, soiling, degradation, and electrical faults. The system includes wireless sensor nodes that are deployed on the panels to collect data on their electrical parameters and environmental conditions, such as temperature, irradiance, and humidity. The collected data is then transmitted to a central server for processing and analysis using machine learning algorithms. The system can detect and diagnose faults in real-time, and provide alerts and recommendations to maintenance personnel to take appropriate actions to prevent further damage or downtime. The system has several advantages over traditional manual inspection and maintenance methods, including reduced downtime, lower maintenance costs, and improved energy efficiency. The proposed system has been validated through experimental tests, and the results show that it can accurately detect and diagnose faults in solar PV panels with high reliability and efficiency. 2023 EDP Sciences. All rights reserved. -
An IoT-based tracking application to monitor goods carrying vehicle for public distribution system in India
Designing a secured transportation system to handover food items to various fair price shops is one of the objectives of smart city development in India. In this paper, an IoT-based tracking solution for moving goods carrying vehicle is proposed. A hardware prototype model is developed using different sensors with GPS/GPRS tracking module and is attached to the vehicle. An alarm is raised to make decision in case of trouble or malfunction. The data generated by the model during the movement of vehicle is encrypted using RSA algorithm and sent to cloud for monitoring by an application developed using PHP and analysis using MapReduce programming model. Experiments are conducted to study the feasibility of the developed model during deployment. From the experiment it is observed that, the developed hardware model and the application meet the objective of monitoring vehicle, safer recovery in case of malfunction and secured delivery of items. Copyright 2021 Inderscience Enterprises Ltd. -
An Novel Cutting Edge ANN Machine Learning Algorithm for Sepsis Early Prediction and Diagnosis
Early detection and diagnosis of sepsis can significantly improve patient outcomes, but current diagnostic methods are limited. The problem addressed in this paper is the early detection and diagnosis of sepsis using machine learning algorithms. Sepsis is a life-threatening condition that can rapidly progress and cause organ failure, leading to increased mortality rates. Early detection and treatment of sepsis are critical for improving patient outcomes and reducing healthcare costs. However, sepsis can be challenging to diagnose, and existing methods have limitations in terms of accuracy and timeliness This research proposes a new cutting-edge Optimized Artificial Neural Network machine learning algorithm for sepsis early prediction and diagnosis. The proposed algorithm combines different data sources, including patient vital signs, laboratory results, and clinical notes, to predict the likelihood of sepsis development. The algorithm was evaluated on a large dataset of patient records and achieved promising results in terms of accuracy, Precision and Recall. The proposed algorithm can potentially serve as a valuable tool for clinicians in the early detection and diagnosis of sepsis, leading to better patient outcomes. 2023 American Institute of Physics Inc.. All rights reserved. -
An Objective Evaluation of Harris Corner and FAST Feature Extraction Techniques for 3D Reconstruction of Face in Forensic Investigation
3d reconstructed face images are the volumetric data from two dimensions, it can provide geometric information, which is very helpful for different application like facial recognition, forensic analysis, animation. Reconstructed face images can provide better visualization, than a two dimensional image can provide. For a proper 3d reconstruction one of primary step is feature extraction. The objective of this study is to conduct a comprehensive evaluation of two prominent traditional feature extraction techniques, namely Harris Corner and FAST (Features from Accelerated Segment Test), for the purpose of 3D reconstruction of face images in forensic analysis. In this research paper feature extraction was carried out using the Harris corner detection and FAST Feature technique. 3D reconstruction is completed using the retrieved features. In this study a comparative analysis was conducted assessing the aspect ratio, depth resolution. The results of the assessment provide valuable insights into the strengths and limitations of both techniques, aiding researchers and practitioners in selecting the most suitable method for 3D face image reconstruction applications. 2023, Ismail Saritas. All rights reserved. -
An objective function based technique for devignetting fundus imagery using MST
Fundus photography is a powerful imaging modality that is utilized for detecting macular degeneration, retinal neoplasms, choroid disturbances, glaucoma and diabetic retinopathy. As the illumination source in fundus imaging is situated at the center of the fundus camera, the illumination at the peripheral regions of the images would be relatively less than the center, which is termed vignetting. Vignetting adversely affects the performance of computerized methods for analyzing fundus imagery. A devignetting method for fundus imagery based on the Modified Sigmoid Transform (MST) is proposed in this paper. Gain (A) and centering parameter (?) of MST have a crucial influence on its performance. For low values of the gain, local contrast is penalized, and the overall dynamic range is compressed. When the value of gain is very high, the images after the illumination correction will have a washed out appearance. The optimum value of gain is determined in this paper from an objective method based on two statistical indices, Average Gradient of Illumination Component (AGIC) and Error of Enhancement (EME). MST with gain value defined via objective methods is able to correct the uneven illumination in fundus images without penalizing the local contrast. The proposed method is compared with illumination equalization model, homomorphic filtering and Adaptive Gamma Correction (AGC) and was found to be superior in terms of naturality uniformity of background illumination, and computational speed. 2018