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An introductory illustration of medical image analysis
The medical imaging field has evolved into an enormous scientific discipline since the last decade of the 19th century. The analysis of medical data obtained by current image modalities such as positron emission tomography, magnetic resonance imaging, computed tomography, and ultrasound comes to the aid of the fruitful diagnosis, appropriate planning, and assessment of therapy for patients treatment and much more. Medical image analysis is crucial to grip this huge amount of data and to investigate and present the appropriate information for any particular medical task. In this chapter, different aspects with regard to medical image analysis are exhaustively explored. In particular, issues and challenges in connection with this task are investigated and described. In addition, a brief summary of the contributory chapters is presented to trace the challenges and findings of each. 2020 Elsevier Inc. All rights reserved. -
An Intrusion Detection Model Based on Hybridization of S-ROA in Deep Learning Model for MANET
A kind of wireless network called a mobile ad hoc network (MANET) can transfer data without the aid of any infrastructure. Due to its short battery life, limited bandwidth, reliance on intermediaries or other nodes, distributed architecture, and self-organisation, the MANET node is vulnerable to many security-related attacks. The Internet of Things (IoT), a more modern networking pattern that can be seen as a superset of the paradigms discussed above, has recently come into existence. It is extremely difficult to secure these networks due to their scattered design and the few resources they have. A key function of intrusion detection systems (IDS) is the identification of hostile actions that impair network performance. It is extremely important that an IDS be able to adapt to such difficulties. As a result, the research creates a deep learning-based feature extraction to increase the machine learning technique's classification accuracy. The suggested model uses outstanding network-constructed feature extraction (RNBFE), which pulls structures from a deep residual network's many convolutional layers. Additionally, RNBFE's numerous parameters cause a lot of configuration issues because they require manual parameter adjustment. Therefore, the integration of the Rider Optimization Algorithm (ROA) and the Spotted Hyena Optimizer (SHO) to frame the new algorithm, Spotted Hyena-based Rider Optimization (S-ROA), is used to adjust the RNBFEs settings. Attack classification is performed on the resulting feature vectors using fuzzy neural classifiers (FNC). The experimental analysis uses two datasets that are publicly accessible. The Author(s), under exclusive licence to Shiraz University 2024. -
An Intuitionistic Fuzzy-Rough Attribute Selection Using Representative Samples
Selecting relevant features is an important tool for extracting knowledge from datasets with many attributes and objects. The traditional theory of rough set is a fundamental and successful tool for dealing with vagueness and inconsistency. Combining the rough set with the fuzzy set handles the information loss problem arising from the discretisation process. Still, it fails to consider the hesitancy part of any information system. A generalisation of fuzzy set known as an intuitionistic fuzzy (IF) set has more real-world applications to confront uncertainty and ambiguity than the fuzzy set. So, the combination of rough set and IF set not only deals with vagueness but also able to consider the hesitancy available in any real-world data. In this work, we propose an IF rough set model based on representative samples and its application in the area of attribute reduction of high-dimensional datasets. First, we defined the representative sample-based intuitionistic fuzzy rough set and then presented an algorithm to calculate the reduction of a dataset using the degree of dependency method. Mathematical theorems are applied to validate the presented model theoretically. Experimental analysis is also discussed to validate the proposed technique. Finally, we applied our proposed method to improve the prediction of antifungal peptides. 2025 Old City Publishing, Inc. -
An Inventory Model for Growing Items with Deterioration and Trade Credit
Growing items industry plays a vital role in the economy of most of the countries. Growing item industries consists of live stocks like sheep, fishes, pigs, chickens etc. In this paper, we developed a mathematical model for growing items by considering various operational constraints. The aim of the present model is to optimize the net profit by optimizing decision variables like time after growing period and shortages. Also, the delay in payment policy has been used to maximize the profit. A numerical example is provided in support of the solution procedure. Sensitivity analysis provides some important insights. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An investigation and analysis on automatic speech recognition systems
A crucial part of a Speech Recognition System (SRS) is working on its most fundamental modules with the latest technology. While the fundamentals provide basic insights into the system, the recent technologies used on it would provide more ways of exploring and exploiting the fundamentals to upgrade the system itself. These upgrades end up in finding more specific ways to enhance the scope of SRS. Algorithms like the Hidden Markov Model (HMM), Artificial Neural Network (ANN), the hybrid versions of HMM and ANN, Recurrent Neural Networks (RNN), and many similar are used in accomplishing high performance in SRS systems. Considering the domain of application of SRS, the algorithm selection criteria play a critical role in enhancing the performance of SRS. The algorithm chosen for SRS should finally work in hand with the language model conformed to the natural language constraints. Each language model follows a variety of methods according to the application domain. Hybrid constraints are considered in the case of geography-specific dialects. 2024 by author(s). -
An Investigation into the Effects of Varying Seasons, Indole-3-Butyric Acid (IBA) and Rooting Media on the Rooting and Longevity of Air-layered Water Apples (Syzygium samarangense L.)
Background: The variation observed in sexual propagation often results in slow growth and fruit development, attributed partly to insufficient photosynthates during early growth stages. To address this challenge, vegetative propagation methods such as air layering offer promising solutions. This technique not only accelerates productivity but also enhances the quality of water apples. Recognizing the importance of air layering in water apple cultivation, this experiment was conducted to standardize this technique by examining the impact of different seasons and planting media in the western tropical wet and dry climate of Tamil Nadu. Methods: The study was conducted to evaluate the variables affecting air layering in water apples in Coimbatore district. The experiment was carried out with three different factors like seasons [August (S1), September (S2) and October (S3)] and IBA [applied at different concentrations of 2000 mg/L (I1), 3000 mg/L (I2) and 4000 mg/L (I3)] along with different rooting media: Cocopeat (M1) and Sphagnum moss (M2). The factorial randomized complete block design (FRCBD) was laid out for statistical analysis. Result: Sphagnum moss performed as a better rooting medium due to its high water holding capacity and good aeration. Increase in the number and early formation of roots were due to more rain fall and high relative humidity in August, that resulted in more nutrient uptake and structural stability. The discovery aids in determining the optimal conditions, including season, dosage and combination (4000 mg/L IBA with sphagnum moss applied in August), for successful air layering in water apple. 2025, Agricultural Research Communication Centre. All rights reserved. -
An Investigation into the Irrigation Capability of Treated Wastewater by Monitoring the Growth of Cicer arietinum (Chickpea)
This study compared the effects of treated wastewater and Hoagland nutrient media on chickpea growth. Results showed that Hoagland media created a more favourable environment, with higher protein levels and carbohydrate content. Antioxidant enzyme activity varied, with SOD activity increasing in both samples. Heavy metal analysis revealed higher lead concentrations in treated wastewater. The findings have implications for sustainable irrigation practices and environmental protection. Treated wastewater can be a viable irrigation option, but careful management is necessary to prevent adverse effects on plant growth and human health. Cautious use of treated wastewater in agriculture is essential for safe and sustainable crop production. 2025 - Kalpana Corporation. -
An Investigation into the Role of AI-Based Innovation in Supporting the Next Generation of Startup Entrepreneurs
The advent of Artificial Intelligence (AI) has revolutionized various industries, offering unprecedented opportunities for innovation and entrepreneurship. This investigation delves into the pivotal role of AI-based innovation in nurturing and empowering the next generation of startup entrepreneurs.AI technologies, including machine learning, natural language processing, and computer vision, have significantly augmented the capabilities of startups across diverse sectors. This study aims to elucidate the multifaceted ways in which AI fosters entrepreneurial endeavors, from ideation to market penetration.AI algorithms enable startups to analyze vast datasets swiftly, extracting valuable insights that inform strategic decision-making and product development. Through predictive analytics and trend forecasting, entrepreneurs can anticipate market demands, optimize resource allocation, and mitigate risks, thereby enhancing the viability and competitiveness of their ventures.AI facilitates personalized customer experiences, driving customer engagement and retention for startups. By leveraging AI algorithms to analyze user behavior and preferences, entrepreneurs can deliver tailored products, services, and marketing campaigns, fostering brand loyalty and customer satisfaction.The integration of AI into startup ecosystems also presents various challenges, including ethical considerations, data privacy concerns, and regulatory complexities. Therefore, this investigation also explores the ethical implications and regulatory frameworks surrounding AI-based entrepreneurship, advocating for responsible innovation practices and stakeholder collaboration. 2024, Collegium Basilea. All rights reserved. -
An Investigation of Complex Interactions Between Genetically Determined Protein Expression and the Metabolic Phenotype of Human Islet Cells Using Deep Learning
The relationship between gene modules and several genome-scale metrics was examined, including heterozygosity that caused type 2 diabetes due to insulin deuteration, differential expression, genotyping association, methylation, and copy number changes. This work investigates the complex relationships between protein expression, genetic polymorphisms, and metabolic properties of human islet cells using expression quantitative trait loci (eQTL) detection. We looked at the genomic, transcriptomic, and proteomic information from islet cells in persons with type 2 diabetes. From the information from different levels, we noticed novel eQTLs that regulate crucial metabolic and signaling pathways in islet cells. Our study highlights the importance of a systems-level approach in understanding the complicated biological processes by highlighting the complexity of the link between genetic variants, protein expression, and metabolic abnormalities using the PIMA Indian dataset. Our findings provide novel insights into the molecular mechanisms behind islet cell failure in type 2 diabetes, potential targets for emerging treatment strategies, and the genomic implications of variations in gene expression, mutations, and other factors. To accomplish this purpose, we proposed a novel BLB model and obtained 99.89%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
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 electrochemical performance of Mn3O4-based aqueous symmetric supercapacitor devices
Manganese (ii, iii) oxide (Mn3O4) is one of the promising materials in the realm of high-performance supercapacitors. The high theoretical specific capacitance, low cost, non-toxicity, environmental compatibility, and natural abundance made it significant in the research field. A low-temperature hydrothermal synthesis method was adopted to prepare Mn3O4 (hausmannite) nanoparticles with a tetragonal spinel structure. The as-prepared nanoparticles were assessed for the structural, elemental, electrical, optical and nitrogen adsorptiondesorption studies through XRD, FTIR, Raman spectroscopy, XPS, DC conductivity, UV-vis absorption and BET analyses. Morphological studies were done using FESEM and TEM and a mixture of nanorods and nanocubes were observed. The electrochemical performances of the as-prepared Mn3O4 nanoparticles were investigated by cyclic voltammetry (CV), galvanostatic charge/discharge (GCD) method and electrochemical impedance spectroscopy (EIS) in a three-electrode system. The present work reports the fabrication of a prototype aqueous symmetric supercapacitor device for the first time. The electrochemical studies were performed in 0.5 M Na2SO4 electrolyte on the separator with a potential window of 0 V to 1 V. A specific capacitance of 68 Fg?1 at a current density of 1 Ag?1 was observed from the constant charge/discharge method. It exhibited a cyclic stability of 72% with a coulombic efficiency of 100% after 1000 cycles. This underscores the noteworthy role of manganese oxide nanoparticles as electrode materials in supercapacitors. 2026 The Author(s). Published by the Royal Society of Chemistry -
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.