Research on Engineering Applications in Multidisciplinary Sectors (Part 1)

Effective key management is crucial in lightweight cryptosystems to enable secure communication and safeguard sensitive data in resource-constrained settings. This work thoroughly analyzes lightweight cryptosystem-specific key management strategies that are resource-efficient. We investigate novel key generation, distribution, updating, and revocation methods that balance cryptographic security with efficient resource use. By examining their performance, we illustrate the effectiveness of several essential management techniques in lowering computational overhead, memory, and communication costs. Our research offers essential tips for developing secure, practical, lightweight cryptosystems that can protect data while using few resources, making them suitable for use in Internet of Things (IoT) devices, wireless sensor networks, and other scenarios where resources are scarce.

For power engineers and researchers across the world, forecasting solar power is a nightmare, as the output is entirely dependent on unpredictable climatic changes. Forecasting of power from PV plants is crucial for load scheduling and electrical grid stabilisation. Forecasting using ML/DL solutions is a powerful and popular method throughout the world. Though various studies have been carried out internationally using various AI solutions for 1 hour ahead forecasting of power generation by large PV plants, in the Indian context, there is very research in this domain. This paper proposes a Deep Learning Approach for forecasting the short-term (1 hour ahead) PV output of a 10 MW solar power plant in the West Bengal state of India. The paper forecasts the power output of a 10 MW plant with LSTM. As the atmospheric variables affecting the PV output power are innumerable, relevant physical parameters are selected through correlation analysis/auto and partial correlation. Conventional machine learning techniques and Deep Learning techniques for multivariate time series analysis have been used to find the forecasting solution. The performance of the various methods is thoroughly analyzed and it was found that LSTM has an edge over other methods.

Lymphoma is a cancer of the lymphatic system. It is a heterogeneous disease, with many different subtypes. To improve patient outcomes, early diagnosis and treatment are crucial. This study proposes a new approach for predicting lymphoma using Random Forests with robust Mahalanobis distance and model ensemble for outlier handling and overfitting mitigation. The proposed approach was evaluated on a real-world dataset of lymphoma patients. The model achieved an accuracy of 92.5% on the test set, which is a significant improvement over the accuracy of other approaches. The suggested method has a number of benefits over alternative methods. First, the use of robust Mahalanobis distance for outlier handling can improve the accuracy of the model by reducing the impact of outliers. Second, the use of a model ensemble can improve the robustness of the model by reducing the risk of overfitting. Third, the use of clinical data can improve the accuracy of the model by providing additional information about the patients. The results of this study suggest that the proposed approach can be used to improve the accuracy and robustness of lymphoma prediction. This could lead to earlier diagnosis and treatment of lymphoma, which could improve patient outcomes.

Supply chain is more complex than working individually, but the results of working together are better than those of trying it alone. Better communication between supply chain members is highly needed to get the correct information about the material and to make the arrangement easy. Under this model, the supply chain consists of a manufacturer and a retailer. They work together to make things more stable. It is well known that stock out is unavoidable while running any business. The present study attempts to study the effect of supply chain models under shortages. Shortages are allowed and fully backlogged. Every firm experiences items degradation during storage, and most of the time these items cannot be recovered. Using deterministic demand rates, this study examined the Weibull distribution deterioration rate and inflation. The effect of inflation and the time value of money were examined under various inflation and discount rates. Mathematical expressions were derived to find the cycle time that is optimal for all cost structures. We demonstrate the model using sensitivity analysis with numerical examples.

An analytical technique called knowledge discovery employs computers to sort through and analyze enormous volumes of data to derive information from the data. Knowledge discovery technologies have been highly beneficial for many enterprises, including networking, marketing, sales, healthcare organizations, and financial institutions. Earlier, we used the data miner tool for analyzing huge volumes of data to derive knowledge from data and examined the uses of data mining tools, contrasting their boundaries with one another, and figuring out the performance of the data miner tool. For instance, in a dispersed network environment, there may be many network services that handle a lot of data and knowledge and provide users with applications based on Web services and Service-Oriented Architecture technologies. As a result, one of the most pressing issues is the need for a workable web service discovery technique for the data and knowledge discovery process in the complicated network environment. Knowledge discovery has several benefits across many industries. The process of knowledge discovery, which turns information into knowledge that can be applied, has improved the decision-making standard. It also makes speedy data analysis possible. The techniques for knowledge discovery and data mining are reviewed in this study. To determine the best approach, the performance of two well-known data mining classifier algorithms, ID3, J48, and Naive Bayes classifier algorithm has been examined using various parameters.

India’s development journey towards becoming a self-sustained, technology-driven, and smart city nation meets with the urgent need to address the challenges of urban development, including infrastructure, quality of life, and sustainability. The transition is akin to piecing together a complex puzzle, with each component representing a facet of technological progress and necessitates the incorporation of state-of-the-art technologies such as Artificial Intelligence (AI), Internet of Things (IoT) devices, data analytics, and advanced communication systems to digitalize and revamp city infrastructure. To materialize this vision, it is equally important to have smart governance to enforce these changes and active participation of the citizens. This paper assesses India's current smart city landscape, spanning major cities, towns, and villages, identifies existing gaps, and proposes a meticulously crafted roadmap to shape India's future. 

Cloud computing applications are becoming more popular as a result of several benefits, such as reliability, storage management, and data accessibility. Data saved in the cloud environment can be accessed at any time and from any location via network access. The enormous volume of user data sharing raises the likelihood of assaults, and unauthorized users have easy data access. Because of its distributed and cohesive nature, blockchain technology improves cloud security. The cryptographic methodologies employed in blockchain for hash generation across blocks boost data security. The blockchain-based security technologies enable strong data security in the cloud environment. As a result, the hybrid elliptic curve, Elgamal technique with blockchain, is provided for application.

Early and accurate detection of brain tumors is important for good diagnosis and treatment strategies. Leveraging advances in machine learning, researchers are using these techniques to accurately and effectively diagnose brain tumors. This study provides an investigation of the current state of machine learning for the detection of brain tumors in human subjects. We classify available data according to different machine learning algorithms, data sources, extraction methods, and measurement models. We also provide an in-depth look at the challenges faced in the field and suggest potential avenues for future research and development. This article presents a way to understand the combination of machine learning and brain tumor detection, revealing the evolving landscape of diagnostics.

Gas Metal Arc Welding (GMAW) with pulse current has gained prominence as a versatile technique for joining various materials, including high-strength low-alloy steel (HSLA) as well as stainless steel. This study aims to perform a comparative analysis of GMAW with pulse current applied to these two distinct materials in terms of weld quality and microstructural characteristics. The research methodology involved laying out of weld bead on both HSLA and stainless steel plates separately, with GMAW performed under identical welding conditions but varying pulse current parameters. Weld bead geometry and microstructural analysis were carried out to evaluate the welds' quality and performance.

Results indicate that pulse current GMAW exhibited notable differences in weld bead profiles and others, including the penetration characteristics between HSLA and stainless steel. Microstructural examination revealed distinct grain structures and phase compositions in the fusion zones of the two materials. Mechanical tests demonstrated variations in hardness properties, highlighting the influence of pulse current on the final weld properties. In conclusion, this comparative analysis sheds light on the nuanced effects of pulse current GMAW on high-strength low-alloy steel as well as stainless steel welding. The findings provide valuable insights into optimizing welding parameters for these materials, enhancing weld integrity, and tailoring mechanical properties to meet specific application requirements. 

Cellular lightweight concrete is a resourceful material that is made up of cement, fly ash, and protein-based foam. Its strength is lower than conventional concrete, so it is useful for a non-load-bearing structural element. Cellular Lightweight Concrete (CLC) is a type of concrete that contains a high volume of tiny air bubbles or cells, which give it a lower density and lighter weight compared to traditional concrete. CLC is known for its insulating properties and is used in various construction applications where lightweight and insulating materials are required. As the research shows that the LWC has a lower modulus of elasticity, lower coefficient of thermal expansion, higher inelastic strains, more voids between particles, and a more continuous contact zone between the aggregates and paste, and so on. These properties act as factors of attraction for the using LWC over traditional concrete. Lightweight concrete holds its voids between the different admixtures and stops the development of laitance layers or cement film when placed on the walls. In this experimental study, the compressive strength of lightweight concrete (LWC), as well as the compressive strength of lightweight concrete with partial replacement of laterite soil, has been investigated. After partial replacement of fine aggregates with laterite soil, the structural strength after 7-14 days was less compared to conventional LWC. But at 28 days, LWC with laterite soil had obtained its complete strength. The average reduction in its weight was around 17% as compared to conventional lightweight concrete.

In today's ever-changing communication landscape, social media platforms have emerged as powerful channels for individuals to express their thoughts and feelings. The vast and continuously expanding reservoir of social media information presents a priceless opportunity for gaining insights into public sentiment, making sentiment analysis an essential undertaking. This research paper explores the effective application of Natural Language Processing (NLP) methods to address the intricate task of sentiment analysis within the domain of social media. Crucial aspects encompass the rapid proliferation of social media data, real-time accessibility for event monitoring, the diversity of data types necessitating advanced NLP techniques, handling of sarcasm and irony, adaptability to evolving sentiment, support for multiple languages, ethical considerations, and the broader implications of sentiment analysis in various fields, such as politics, healthcare, and social sciences. Emotion detection is highlighted as a means to achieve a more nuanced understanding of these applications.

Numerous studies looking at the mechanical qualities of geo-polymer concretes have been based on the generally held belief that it has a lower potential for global warming in comparison to OPC (Ordinary Portland Cement) concrete. As per the study, geo-polymer concrete produces 80% less CO2 emissions than OPC. This concrete has emerged as a potential alternative to conventional Portland cement-based concrete because of its sustainable attributes and enhanced performance characteristics. The environmental benefit of geo-polymer concrete is a significant focus of the review, underscoring its reduced carbon footprint and lower energy consumption during production compared to conventional cement-based concrete. The utilization of industrial by-products, like fly ash and slag, as precursor material further contributes to its sustainability. This study compares geo-polymer concrete with ordinary concrete and examines its performance under various conditions, including carbonation, sulfate solution, acid corrosion, and chloride penetration. It has been found that in several cases, geo-polymer concrete durability is better than conventional concrete.

Diagrid structures are commonly used these days for tall buildings due to their structural efficiency and architectural aesthetic potential, and new grids can be studied and tested as a result. A diagrid refers specifically to the structural system of a building, involving diagonal members forming a grid for support, while an irregular grid refers to the irregular layout of buildings in an urban or architectural context. The two concepts are related to different aspects of building design and construction. The irregular grid is one of the grids chosen here. Because irregular geometry for tall buildings was used in this study, it is critical to identify relevant structural systems for improved overall performance. An irregular grid was chosen as the outer grid for this tall building. The main cases considered here are regular shape and irregular shape (L shape and C shape) buildings with irregular grids, including peripheral columns, and conventional grids with peripheral columns for the same shape. Irregular geometry was modelled in Rhino 3D with Grasshopper as a plugin, which was imported into SAP2000. Steel had been chosen as the building material in this case. According to codal provisions, analysis had been performed for both Lateral Loads, namely Earthquake and Wind Load. This study can serve as a benchmark for future infrastructure development by demonstrating how effectively irregular geometry can be used to overcome all of the flaws of conventional methods.

Without a speck of doubt, Artificial Intelligence (AI) influences practically every sector of human endeavour, and has already been the primary force behind emerging technologies. However, with its growing buzz, it has been observed that there are now concerns about the potential negative repercussions of the implementation of different AI systems, particularly with regard to the standards enshrined in the Indian Constitution. Its use cannot go beyond the rights that citizens are guaranteed, such as Freedom of Speech and Expression, Liberty, Justice, Security, and the Right to Privacy, to name a few. The Supreme Court of India defined the Right to Privacy as broadly embracing autonomy, choice, and control in the context of informational privacy in the case of Justice K.S. Puttaswamy v. Union of India ([2017] 10 SCC 1). Though not officially addressed in the Indian Constitution, Article 21—which effectively discusses the Right to existence and Personal Liberty—inherently includes the Right to Privacy as a component of a dignified existence. As it is said that every coin has two faces, Artificial Intelligence has the potential to benefit society in many ways. However, by accelerating the speed and capacity of personal information analysis, Artificial Intelligence also increases the potential for using personal information without the consent of the concerned stakeholder in ways that could violate privacy concerns. Thus, it becomes imperative to ensure that all technological applications adhere to the letter and spirit of the law. Therefore, this paper is an analysis of the concerns surrounding AI in light of the Right to Privacy, prospects, and legal and ethical challenges with special reference to Constitutional Imperatives. 

 A green supply chain approach is required for industries to remain sustainable and is critical for our planet's well-being. By improving cost effectiveness, lowering waste, and satisfying customer demand for green products, it contributes to a reduction in the carbon footprint of manufacturing operations by generating less waste and reducing expenses. This study takes into account a two-level model that considers carbon release from production, carrying products, shipping, item deterioration, and waste disposal and also helps to minimize the overall cost. The presented model was analytically evaluated and optimizes the production time and supplier’s replenishment time. The current study has been demonstrated by both a sensitivity analysis and a numerical analysis. A graphical representation is also given to determine the convexity of the proposed model.

A person is permitted to assign theirsigning rights to another individual through a proxy signature scheme. These systems have been suggested for use in various applications, particularly in distributed computing.. In the current study, a novel quantum proxy signature mechanism is proposed, by using quantum one-way function (OWF) and EPR quantum entanglement. Quantum OWF can be determined in polynomial time, but it is difficult to invert them in polynomial time. This system's unconditional protection is ensured by the quantum key distribution, OWF and onetime-pad encryption algorithm.

Speech emotion recognition (SER) is a growing branch of study that analyzes human emotion duringoral conversation. The target human can be a child, woman, or man. Using some datasets and algorithms, we recognized the human speechemotion. Some speech-emotional databases were also used. They also play a vital role in cross-cultural activities. SVM, HMM, or deep learning models like CNN) or RNN are frequently employed in speech recognition of emotional systems to achieve accurate emotion recognition. As speech emotion recognition technologies advance, it is essential to address privacy and ethical concerns. The algorithm’s performance depends on the quality and quantity of the data, feature extraction techniques, hyperparameter tuning, and some other factors.

There is a need to develop an easy-to-use system that predicts chronic disease without the need for a personal consultation with medical professionals. The goal is to identify various diseases by analyzing the symptoms of patients through various Machine-Learning Models (MLM). Machine Learning (ML) is used to predict diseases based on symptoms provided by patients or users. The proposed system takes the symptoms delivered by the patients as input and the system produces an output indicating the possibility of a specific disease. A classifier known as “Naive Bayes,” which belongs to machine learning techniques, is used to find out the possibility of a disease occurring. The increasing availability of biomedical and healthcare data has enabled more accurate analyses, facilitating early disease identification and patient care. Diabetes, malaria, jaundice, dengue, and tuberculosis are just some illnesses that can be predicted using methods like linear regression and decision trees. The study compares various Machine Learning algorithms and reveals that the Random Forest algorithm achieves a 97% accuracy rate in symptom-based predictions. 

In the present paper, we analyze K-H instability for stratified viscous fluids under the influence of a vertical magnetic field in a porous medium. Using the normal mode method on the linearized perturbation equations, we obtain a dispersion relation. Numerical solutions of the dispersion relation indicate that viscosity and porosity contribute to stability, whereas streaming motion enhances instability.

The main purpose of this research paper is to determine and evaluate the impact of Artificial Intelligence on human life. Artificial Intelligence is one of the most important and trending technologies in the current scenario. They are highly responsible for improving the quality of the interaction between humans and various technological systems. In today’s world, Artificial Intelligence is highly advanced and is continuously growing at a rapid pace. A large number of AI applications and devices which work on the concepts of AI can be seen around us. Artificial intelligence's main goal is to build systems that can perform better than humans in various fields and to provide better solutions to problems than humans. 

TerraZyme is a natural, non-toxic, non-flammable, and non-corrosive liquid enzyme mixture fermented from vegetable extracts. It improves the engineering properties, workability, and stability of the soil by catalyzing clay-organic cation interactions and accelerating the cationic exchange process, allowing for the formation of a thinner adsorbed layer. TerraZyme-treated red dirt and black cotton soil were subjected to strength tests. Counselling is made easier by compressibility and index. The unconfined compressive strength, CBR, consistency limits, compressibility, and swelling of unsterilized and stabilized soil samples were measured after air drying and desiccator curing. The air-dry curing method, which mimicked field conditions, increased the UCS and CBR of TerraZyme-treated soils, both expansive and nonexpanding. TerraZyme improves both expansive and non-expansive soil index qualities in addition to air-drying and desiccator curing. Air-dry curing outperforms desiccator curing in terms of compressibility and edoema. Air-dry curing is the most effective method for stabilizing expansive and non-expansive soils, according to research. Researchers investigated air drying and desiccator curing to economically stabilize TerraZyme-modified soil.

 Social media platforms are crucial for information and communication, but the rise of extremism and radical content has raised concerns. To ensure user safety and security, a novel approach to extremism detection is proposed using deep neural networks and a multi-language dataset. This approach considers the linguistic diversity found in social media conversations and leverages a multilingual dataset including Hindi, English, and Hindi-English code-mixed language data. Machine learning and deep learning models, including CNN, Bidirectional Long Short-Term Memory networks (Bi-LSTM), and BERT, are employed to process and analyze the multilingual data. Word embedding techniques like Word2Vec and GloVe are used to represent words and phrases in a continuous vector space. Preprocessing methods like tokenization, stemming, and top-word removal are employed to enhance the quality and consistency of the input data. The proposed approach achieves promising results in detecting extremism across different languages, surpassing traditional English-only models. The integration of diverse linguistic data and the utilization of multiple deep learning models contribute to the robustness and effectiveness of the system.

Wireless power transfer (WPT) utilizing magnetic resonance is a technology that has the potential to liberate humanity from the inconvenience of wires. In essence, WPT is based on the same fundamental principles that have been refined over the past three decades under the term Inductive Power Transfer (IPT). Recent years have witnessed significant advancements in WPT technology, with transfer distances at the kilowatt power level increasing from mere millimeters to several hundred millimeters, and grid-to-load efficiency exceeding 90%. These developments have rendered WPT highly appealing for electric vehicle (EV) charging applications, both in stationary and dynamic charging scenarios. By integrating WPT into EVs, the challenges of charging time, range, and cost can be effectively addressed, rendering battery technology irrelevant for mass market penetration of EVs. It is our hope that these state-of-the-art achievements will inspire researchers to further develop WPT and expand the EV market.

Fenton’s process has established a cost-effectively, viable process for the degradation of harmful pollutants present in wastewater. Therefore, in the work, degradation of Rhodamine B (RhB) dye in aqueous medium was carried out using Fenton (Fe2+/H2O2 ) process. The effects of different process parameters i.e., Fe2+ dosage (25–200 mg/L), H2O2 concentration (25–150 mg/L), initial dye concentration (20-100 mg/L), and solution pH (3 – 9), on the extent of degradation have been conducted in a batch mode. It has been observed that the degradation efficiency of RhB was found to be dependent on the pH of the solution. The optimum conditions were observed to be pH (3.0), H2O2 concentration (50 mg/L), and Fe2+ concentration (150 mg/L) for an initial RhB dye concentration of 20 mg/L at a temperature of 20°C. Under the optimum conditions, nearly 77% degradation of RhB in aqueous solution was found in 30 min of reaction time. In order to confirm the obtained experimental data, a digital automated program has also been used. Overall, it can be concluded that Fenton’s process may be used for the degradation of RhB dye and other similar dyes containing dye wastewater. 

 It is commonly believed that a geo-polymer concrete has a lower global warming potential than OPC concrete. Numerous studies examining the mechanical qualities of geo-polymer concretes have been based on this notion. As per a study, geopolymer concrete produces 80% less CO2 emissions than ordinary Portland cement. This concrete's improved performance qualities and sustainable qualities have made it a viable substitute for traditional Portland cement-based concrete. Polypropylene is a type of thermoplastic polymer that finds extensive usage across a diverse range of applications. These applications include, but are not limited to, strapping materials such as ropes, thermal garments, and blankets. Polymer cement is a type of cement that utilizes a polymer to serve as a coating and facilitate bonding. The various types of materials in this category comprise polymer-filled solids, polymer cement, and Portland polymer bonded concrete. The aim of the research was to achieve the highest possible concrete strength through the utilization of the most suitable weight of polypropylene fibres. The utilization of fibre reinforced concrete has become prevalent in diverse engineering applications owing to its commendable and exceptional characteristics in the domains of industry and construction.

Nowadays, biometrics is so prevalent within critical regions and other access control systems. This broad applicability of biometrics has also provided assaulters with new opportunities. As a result of the proliferation of technology, cybercriminals have also become more intelligent. Now, rather than trying to crack passwords, they are more interested in breaking into biometric-based systems. Because a human's biometric is not something that can be regenerated, it stays remarkably consistent throughout his lifetime. As a result, if such information falls into the wrong hands, a person cannot modify their features and there is no way to recover it. Although numerous researchers have developed techniques for securing such information, there are still many unfilled gaps. In order to accomplish the goal of data protection, this paper first describes the need for the protection of biometric data. It next offers a brief analysis of the strategies recommended by various researchers, along with their benefits and drawbacks.

Web scraping emerges as a computational technique capable of efficiently harvesting substantial data from websites. This paper presents a comprehensive exploration of various web scraping strategies, backed by empirical research findings. Prominent libraries like BeautifulSoup and LXML, along with external libraries like Requests and Selenium, are scrutinized in the pursuit of extracting valuable data. The primary objective of this research is to assess the efficacy of different web scraping libraries. To achieve this, each approach is rigorously tested while attempting to extract data from targeted websites. The evaluation criteria encompass process time, memory consumption, and data usage. Our experimental results demonstrate a significant performance gap between web scraping libraries. Compared to BeautifulSoup, Selenium and LXML were notably more efficient, consuming 81% less memory and utilizing 12% less processing time, which is reduced by 28%. Our findings underscore the importance of library choice in web data extraction research. The performance disparities observed among the libraries highlight the need for practitioners to carefully consider their specific requirements and select the most suitable tool for their web scraping tasks.