Browsing by Department "Alliance University"

This research article embarks on a thorough experimental inquiry into the detection of kerosene adulteration in petrol, employing a sophisticated one-dimensional microcavity of nano-scale porous silicon (1D-PSMC) sensing device. The significance of this investigation stems from the detrimental consequences of petrol adulteration, which not only aggravates environmental pollution but also jeopardizes the functionality and durability of machinery components, thereby posing significant economic and environmental challenges. The 1D-PSMC, designed to function as an optical sensor device, has undergone extensive scrutiny and practical application. Within this study, we meticulously examine the resonance wavelength shift observed in the reflectance spectra of petrol samples containing various concentrations of kerosene adulterants. Impressively, the sensor device demonstrates outstanding efficacy, capable of detecting adulteration levels as low as 0.5% and even discerning minute variations of 0.01% [Table 1]. This remarkable sensitivity underscores the invaluable potential of the 1D-PSMC sensor in real-world applications. Moreover, the article delves into the intricate relationship between wavelength shifts in the reflectance spectra and the diverse concentrations of kerosene present in the petrol samples. By elucidating these correlations, this research contributes significantly to expanding our comprehension of the operational mechanisms of 1D-PSMC sensing devices in combatting petrol adulteration effectively. Consequently, such advancements hold promises for mitigating environmental degradation and preserving machinery efficiency on a broader scale.

Two-dimensional (2D) materials, an electrifying family of innovative materials, have recently attracted wide attention due to their remarkable characteristics, primarily their high optical transparency, exceptional metallic conductivity, high mechanical strength, carrier mobility, tunable band gap values, and optimum work function. Interestingly, 2D-nanosheets/nanolayers (2D-NLs) might be synthesized into single/multi-layers using simple processes such as chemical vapor deposition (CVD), chemical bath deposition (CBD), and mechanical and liquid-phase exfoliation processes that simply enhance optoelectronic properties. However, the stability of 2D-NLs is one of the most significant challenges that limits their commercialization. Researchers have been focusing on the stability of 2D-NLs with the aim of developing next-generation solar cells. Easily tunable distinctive 2D-NLs that are based on the synthesis process, surface functional groups, and modification with other materials/hybrid materials thereby improve the stability of the 2D-NLs and their applicability to the hole transport layer (HTL) and the electron transport layer (ETL) in solar cells. Moreover, metal/non-metal-based dopants significantly enhance band gap ability and subsequently improve the efficacy of dye-sensitized solar cells (DSSCs). In this context, research has focused on 2D-NL-based photoanodes and working electrodes that improve the photoconversion efficiency (PCE) and stability of DSSCs. Herein, we mainly focus on synthesizing 2D-NLs, challenges during synthesis, stability, and high-performing DSSCs.

Precise identification of liver region from abdominal Computed Tomography-Angiography (CTA) plays an important role in the evaluation of donor for liver transplantation surgery. Nevertheless, the issues like intensity similarity of liver with neighbouring tissues and inter-intra patient liver shape variability; left the task of liver segmentation challenging. Here, we focus on improving the accuracy and reliability of liver donor evaluation system by customising its crucial step - liver segmentation and volume measurement. For achieving this, a Bayesian classifier is iteratively trained with salient features of liver, namely Haralick texture features and spatial information computed from the individual patient dataset. The proposed method is a combination of two techniques namely, advanced region growing and Bayesian classification. The agreement between the proposed method with the manual segmentation was satisfactory with Relative Volume Difference (RVD), Dice Similarity Coefficient (DSC), False-Positive Ratio (FPR), False-Negative Ratio (FNR) with values 8.98, 94.8 ± 1.5, 3.1 ± 2.8 and 5.67 ± 1.8, respectively. Copyright © 2015 Inderscience Enterprises Ltd.

Microwave imaging utilizes low-power Near-field electromagnetic fields at microwave frequencies to detect the internal structure of an object. Sufficient resolution through the thickness is crucial in biomedical applications to detect small objects of concern. Parameters such as the frequency of microwave signals, the design, and the material of the antenna are the most important factors to consider for microwave-based biomedical sensing. The proposed antenna falls under the good health and well-being goal, which is among the sustainable development goals (SDGs) that transform the world and yields merits of: compactness in size, ease of fabrication, wider impedance bandwidth, simple design, and good RF performance. An Asymmetric-fed Coupled Stripline (ACS) antenna is 3D-printed on an FR4 substrate with return loss measurements ranging from 2 GHz to 20 GHz. The impedance bandwidth is obtained between 6 GHz to 8 GHz and 15 GHz to 17 GHz. The proposed microwave antenna was simulated using Ansys HFSS. The parameters are designed to ensure optimum radiation efficiency. The radiation patterns obtained were omnidirectional in H-plane and bidirectional in E-plane. © The Electrochemical Society

A major difficulty in 5G-based mobile communications is to guarantee robust selecting routes for adaptive packet transmission in a dynamic environment. Traditional routing protocols struggle to adapt to the fluctuating wireless channel conditions inherent in 5G networks. To address this, our study introduces a novel system that integrates Deep Q-Networks (DQN) techniques with the Zone routing protocol (ZRP). Leveraging real-time network data including channel quality, traffic load, and congestion levels, the system employs machine learning algorithms to predict route stability. This predictive capability enables dynamic identification of the most stable route for packet transmission, with continuous monitoring and adjustment in response to evolving network conditions. Our proposed system follows a multi-step flow, starting from data collection and culminating in route selection based on machine learning predictions. Extensive simulations and real-world experiments validate the efficacy of our approach, demonstrating significant improvements in packet delivery ratio, latency, and overall network stability compared to conventional methods. Notably, our system exhibits resilience against varying network conditions and maintains scalability with increasing network size and traffic load. Through the fusion of machine learning and routing protocols, our study offers a promising solution to the critical challenge of stable route selection in 5G-based mobile communications, addressing the diverse demands of emerging applications and services. © 2024, Scibulcom Ltd.. All rights reserved.

Smart grid describes a network that uses dynamic optimization technology that eliminates network losses in real-time, Retains voltage levels, Enhances reliability, And improves asset management. This grid incorporates advanced technologies and resources from the production, Transmission, And delivery to the system and equipment of end-users. In a structured, Collaborative process that makes the energy generation, Distributing, And consumption efficient, The smart grid integrates infrastructure, Processes, Devices, Information, And markets. The operational data gathered through the whole system and it will trigger system equipment to optimize the solution to make sure that different contingencies are protected from attacks, Vulnerabilities, Etc. This needs the intelligent grid to define and investigate key performance metrics, To design and evaluate suitable resources, And to establish the required educational curriculum. Provide present and future experiences, Experience, And knowledge for this innovative framework to be deployed. A necessity for greater flexibilities in energy systems, Minimizing energy costs, And reducing adverse environmental effects, For consumers in future power systems think of microgrids concept to use. This chapter addresses significant technological challenges for the architecture and implementation of the smart grid by concentrating on the technology and practices necessary to design and integrate various components with smart grids. © 2022 Scrivener Publishing LLC. All rights reserved.

In the realm of medical visual scrutiny, the accurate identification of prostate cancer holds paramount significance for early diagnosis and effective treatment. This work presents a pioneering method for prostate cancer identification, harnessing the power of deep learning and Migration Learning strategies. Leveraging the VGG-16 Convolutional Neural Network (CNN) framework as the cornerstone, the proposed approach capitalizes on its ability to extract intricate features from medical images. By incorporating Migration Learning, the model is enriched with knowledge gleaned from diverse datasets, enabling it to achieve exceptional performance even with limited medical image data. The methodology entails meticulous dataset curation and preprocessing, ensuring the quality and representativeness of the images. The VGG-16 model undergoes a meticulous finetuning process, accommodating the unique characteristics of prostate cancer images. Performance evaluation is conducted rigorously, utilizing established metrics to gauge the approach's effectiveness. Comparative analysis with contemporary methods showcases the breakthrough potential of the proposed approach. The model gave 93.97% testing accuracy. © 2024 IEEE.

Cloud computing has brought about a significant transformation in the way businesses function, presenting a wide array of advantages for enterprises regardless of their size. This technology, involving the storage and access of data and applications over the internet instead of on physical hardware, has become a fundamental element of contemporary business strategies. In this paper, we will delve into the various benefits that cloud computing offers to business enterprises. © 2024, Ismail Saritas. All rights reserved.

Software defined network (SDN) with disjoint control and data plane enables the controller to address network requirement better. A significant issue in such networks is how the network resources are allocated by the controller based on the demand, for instance, allocation of path with less delay, less error rate and so forth. The controller allocate such network resources by enabling link discovery between switches. SDN utilizes protocol like link layer discovery protocol (LLDP) for links to be identified in OpenFlow devices. The detection of links in OpenFlow devices requires a huge number of PACKET OUT (Pout) messages from the controller to be sent across every active interfaces of the switch which might limit the controller performance. This paper introduces a Broadcast based Link Discovery (BBLD) scheme which lowers messages required to obtain the link discovery in OpenFlow switches (OFS's) by dispatching a solitary Pout from the SDN controller (unlike a unicast message from the controller in case of LLDP) to any OpenFlow switch (OFS), which then circulates the same to the nodes successively. Experiments performed with Mininet reveal that messages required to obtain similar link discovery are reduced by an average of (28%-42.4%) under varied set of topologies. © 2021 IEEE.

In the proposed work, the investigation of a rural distribution network of Shivalli feeder which is in Dharwad district, Karnataka, India is the locale which is frequently undergoing with the certain issues like voltage regulation, energy loss, & peak loading conditions. Solar energy, one of the most useful and efficient renewable sources, which is used for generation and distribution, in which the optimal allocation on distribution feeders helps in reducing the energy loss and enhances the voltage profile. The methodology is evolved for determining the pertinent location of distributed generation in distribution network. The simulations are performed using Power World Simulator which indicates the reduction in energy loss and improved system performance using distributed generation. Also, the examination for climate and metrological condition of Dharwad district with respect to renewable energy resources availability is performed. The cost analysis for installation and implementation of decentralized distributed generation is achieved. Finally, the study reveals enhanced voltage profile, reduced energy loss and increased network capacity in the distribution network. The work emphasizes the impact of distributed generation on a practical system.

Wireless mesh network is an evolving next generation multi-hop broadband wireless technology. Collaborative attacks are more severe at the transport layer of such networks where the transmission control protocol's three-way handshake process is affected with the intention to bring the network down by denying its services. In this paper, we propose a novel collaborative defense protocol (CDP) which uses a handshake-based verification process and a collaborative flood detection and reaction process to effectively carry out the defense. This protocol presents a group of monitors that collaboratively entail in defending the attack; thus reduces the burden on a single monitor. Moreover, this paper proposes a novel transport layer post-connection flooding attack that occurs after establishing a TCP connection and we show that CDP can detect and mitigate this attack. The CDP protocol has been implemented in Java and its performance has been evaluated using essential metrics. We show that CDP is efficient and reliable and it can identify the attack before any major damage has occurred.

For diabetic retinopathy (DR) surgery, localization of retinal blood vessels is of paramount importance. Fundus images which are often used for DR diagnosis suffer from poor contrast (between the retinal background and the blood vessels, due to its size) limits the diagnosis. In addition to this, various pathological changes in retinal blood vessels may also be observed for different diseases such as glaucoma and diabetes. To alleviate, in this paper, an automated unsupervised retinal blood vessel segmentation technique, based on backtracking search optimization algorithm (BSA), is proposed. The BSA method is used to optimize the local search of fuzzy c-means clustering (FCM) algorithm to find micro-diameter sized vessels along with coarse vessels. The proposed technique is tested on two publicly available retinal datasets (i.e., STARE and DRIVE) and verified using the dataset collected from various hospitals in Bangalore and Mangalore, India. The results show that the performance of the proposed method is comparable to the conventional techniques in terms of sensitivity, specificity, and accuracy.

Individuals can participate in a diverse portfolio of real estate assets through reits, which are investment entities. Due to their ability to provide money and give exposure to the real estate market, they have grown to be quite popular. The performance, risk characteristics, market dynamics, and regulatory environment of reits are the main subjects of research. Financial data and market patterns are examined using quantitative analysis, while case studies provide in-depth research of particular reits or portfolios. Comparative research offers insightful comparisons between various reits or reit marketplaces. Information is gathered from a variety of sources as part of the data collection process for reit research. Economic indicators, financial statements, stock market data, real estate market data, regulatory filings, research papers, surveys, and interviews are frequently used. It is essential to guarantee the dependability and correctness of the data gathered while abiding by the rules of data privacy and confidentiality. The goals of the research on reits include performance analysis, risk assessment, benefit exploration from portfolio diversification, and market efficiency evaluation. For investors, decision-makers, and business experts interested in comprehending and using the advantages of reits, the research offers helpful insights. It emphasizes how crucial it is to keep track of performance, control risks, and keep up with market and legal developments in the reit industry.

Background: Around thirty-five percent of the population in India eat according to a vegetarian diet (Kumar and Prakash 2017). A vegetarian diet is considered healthier than a non-vegetarian diet as it has less saturated fat. However, unhealthy practices like eating packaged foods, drinking alcohol, or smoking have contributed to a growing obesity pandemic. According to current estimates, globally around 2.8 million deaths occur due to people being overweight or obese each year (WHO 2016). Anemia has been shown to be prevalent among approximately one-third of the world's population reported by WHO (Botswana Compendium, 2017). Objectives: This research aims to compare hematological, body mass index and lipid profile parameters in accordance with vegetarianism and non-vegetarianism. Its other aim is to look for effective dietary strategies for fighting lifestyle diseases. Methods: The study included a cohort of thirty vegetarian men and thirty age-matched non-vegetarian men aged 35 to 50 years who were seeking health assessments at the Master Health Check-up clinic. Demographical features were recorded along with blood tests were performed including haematology, body mass index (BMI) and lipid profile evaluations for all subj ects. Blood samples were taken after 10 hours of overnight fasting for analysing serum lipids parameters. Results: The mean±SD values of Hemoglobin, Hematocrit (PCV), Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), and Mean Corpuscular Hemoglobin Concentration (MCHC) were increased in the non-vegetarian group compared to the vegetarians. There were no significant differences in Total Red Blood Cell (RBC count), Red cell Distribution Width (RDW), Platelet count, and Erythrocyte sedimentation Rate between non-vegetarians and vegetarians. Lipid profile showed no significant differences in serum total cholesterol, triglycerides, LDL (Low-density lipoprotein) between non-vegetarians and vegetarians except for a relative increase in these parameters in non-vegetarians. The vegetarians had a relative increase in HDL (High-density lipoprotein). No significant disparity was found with BMI values in both non-vegetarians and vegetarians. Conclusion: This study shows that a vegetarian diet tends to develop nutrient deficiency anemia, especially iron deficiency anemia whereas a non-vegetarian diet has an increased risk of developing dyslipidemias. It is recommended that a non-vegetarian diet having an adequate daily intake of fruits, vegetables, and polyunsaturated fatty acids, with lifestyle modifications would prevent the development of deficiency anemias and dyslipidemias. © 2024 IEEE.

License plate recognition is one of the challenging tasks and it belongs to ITS, due to backgrounds, variation of illumination, occlusion the recognition is became the challenging task. These challenges enabled a comparison between the conventional image enhancement and the deep learning scheme for license plate recognition. We analyze and compare multiple techniques that we consider significant, including, the Edge detection, Morphological operations and template matching and also deep learning models like CNNs and R-CNNs. In this framework of experiment scenarios, we scale out a comprehensive evaluation of the methods given different datasets and compare their accuracy, speed as well as stability in conditions that may be hostile. In the experiments, we see that Naive Bayes and SVM outperform in low-resource conditions, but deep learning methods are significantly more accurate and more flexible in accommodating complicated cases and trends, which makes deep learning methods the method of choice for contemporary applications. This work provides an understanding of the strengths and weaknesses of both approaches to serve as a guideline in choosing the most effective detection techniques in relation to certain environmental conditions. © 2024 IEEE.

Access to justice is a cornerstone of a fair and equitable legal system, yet disparities in legal aid services across jurisdictions often impede this ideal. This paper undertakes a comparative analysis of legal aid frameworks in the United Kingdom, the United States, and India, focusing on their effectiveness in bridging the gap between marginalized communities and justice. The study examines the structures, funding mechanisms, and delivery models of legal aid services in these nations, highlighting their strengths and limitations. A critical component of this analysis is the role of Alternative Dispute Resolution (ADR) methods, including mediation, arbitration, and conciliation, in augmenting access to justice. While ADR has gained traction as a cost-effective and time-saving alternative to traditional litigation, its integration into legal aid programs varies significantly across the three countries. The UK demonstrates a structured approach to ADR within legal aid, supported by government initiatives. In contrast, the USA emphasizes privatized ADR systems with limited public legal aid integration. Meanwhile, India faces challenges in scaling ADR due to resource constraints and limited awareness. This paper explores the interplay between legal aid services and ADR methods, assessing their potential to enhance accessibility, reduce case backlogs, and provide culturally sensitive dispute resolution mechanisms. Drawing on comparative insights, the study identifies best practices and policy recommendations to strengthen legal aid frameworks globally. It concludes that embedding ADR within robust legal aid systems can democratize justice, ensuring it is accessible to all, regardless of socioeconomic status. This research contributes to ongoing discussions on legal reform, offering a roadmap for policymakers to create more inclusive legal systems.

This study presents a detailed analysis and framework utilizing machine learning techniques to understand the occurrences of crimes against women in India. The key methodologies include data preprocessing, feature selection, and model selection whichwould enhance the accuracy of the models. Various supervised learning algorithms such as logistic regression, naive Bayes, stochastic gradient descent, K-nearest neighbor, decision tree, random forest, and extreme gradient booster have been used and compared to understand which algorithm would be the most capable. The findings and approach discussed in this study can be used to mitigate the risks, enhance victim support systems, and strengthen preventive measures to reduce crimes against women. This study would also contribute to combating gender-based violence and foster a safer and more inclusive environment for women in India. © 2024 IEEE.

The early 1990s saw the financial sector reforms in India, which led to a rapid expansion of the economy, the opening of the Indian financial market to foreign and domestic private players, a significant influx of foreign institutional investors, increased competition, and better product options for consumers. The growth of mutual funds has been one of the key developments of this decade. Mutual Funds are a type of financial intermediary that mobilizes surplus income earners' savings and directs them towards markets where there is a need for capital. In India's financial services industry, mutual funds have become a powerful financial intermediary and the area experiencing the fastest growth. Its objectives include fostering a financially diversified, effective, and competitive sector, increasing investment returns, and fostering and accelerating economic growth. Small investors who are unable to directly invest in the stock market can use this medium of investment. This project, which is titled "A Comparative Analysis of Performance of MF between Private and Public Sectors," focuses on people's preferences for different asset management firms and their historical performance. Comparing the index with market returns is one of the study's key goals. This project also aids in determining market growth and the performance of funds based on market risk. The data gathered was analyzed using descriptive statistics. Through analysis, it was discovered that the majority of investors chose growth-oriented mutual funds as their best investment option. Any investor's primary criteria are low risk and moderate returns. They gave Reliance Mutual Fund and LIC Mutual Fund products priority when choosing the asset management firm. A conclusion was reached after the data were tabulated and properly analyzed using financial tools like the Treynor, Sharpe, and Jensen method.

This report investigates the key factors affecting the performance of Initial Public Offerings (IPOs) in India from 2022 to early 2024. The study aims to assess the impact of Oversubscription Rates, Grey Market Premiums (GMP), and financial performance indicators such as Revenue Growth and Profitability on both immediate listing day gains and longer-term post-listing outcomes across SME and Main Board IPOs. A quantitative research methodology was employed, analyzing a sample of 30 IPOs from both segments. Data on oversubscription rates, GMP values (one day prior to listing), listing day gains, and post-listing financial metrics were gathered and analyzed using descriptive statistics, correlation analysis, and regression modeling. The analysis revealed that SME IPOs tend to have significantly higher and more volatile oversubscription rates than Main Board IPOs, largely due to speculative interest from retail investors. A strong positive correlation was observed between oversubscription rates and GMP for both IPO types, with a more pronounced alignment in Main Board IPOs, reflecting institutional confidence in established companies.