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Synthesis and Nuclear Magnetic Resonance Studies of 2-Thiophenecarboxaldehyde Nicotinic Hydrazone and 2-Thiophenecarboxaldehyde Benzhydrazone
Synthesis and NMR spectral studies of bidentate N and S heterocycles of 2-thiophenecarboxaldehyde nicotinic hydrazone and 2-thiophenecarboxaldehyde benzhydrazone have been carried out. The compounds, 2-thiophenecarboxaldehyde nicotinic hydrazone and 2-thiophenecarboxaldehyde benzhydrazone were synthesized by reacting stoichiometric quantities of nicotinic hydrazide and benzhydrazide with 2-thiophene carboxaldehyde in methanol in the presence of glacial acetic acid at refluxing temperature. Upon cooling the reaction mixture, the products were obtained as colorless solids. 1H, 13C, 1H-1H COSY, and 1H-13C HSQC experiments have been conducted to characterize the compounds. 2020 Malaysian Institute of Chemistry. All rights reserved. -
Advances in Carbon-Element Bond Construction under Chan-Lam Cross-Coupling Conditions: A Second Decade
Copper-mediated carbon-heteroatom bond-forming reactions involving a wide range of substrates have been in the spotlight for many organic chemists. This review highlights developments between 2010 and 2019 in both stoichiometric and catalytic copper-mediated reactions, and also examples of nickel-mediated reactions, under modified Chan-Lam cross-coupling conditions using various nucleophiles; examples include chemo- and regioselective N-arylations or O-arylations. The utilization of various nucleophiles as coupling partners together with reaction optimization (including the choice of copper source, ligands, base, and other additives), limitations, scope, and mechanisms are examined; these have benefitted the development of efficient and milder methods. The synthesis of medicinally valuable or pharmaceutically important nitrogen heterocycles, including isotope-labeled compounds, is also included. Chan-Lam coupling reaction can now form twelve different C-element bonds, making it one of the most diverse and mild reactions known in organic chemistry. 1 Introduction 2 Construction of C-N and C-O Bonds 2.1 C-N Bond Formation 2.1.1 Original Discovery via Stoichiometric Copper-Mediated C-N Bond Formation 2.1.2 Copper-Catalyzed C-N Bond Formation 2.1.3 Coupling with Azides, Sulfoximines, and Sulfonediimines as Nitrogen Nucleophiles 2.1.4 Coupling with N, N -Dialkylhydroxylamines 2.1.5 Enolate Coupling with sp 3-Carbon Nucleophiles 2.1.6 Nickel-Catalyzed Chan-Lam Coupling 2.1.7 Coupling with Amino Acids 2.1.8 Coupling with Alkylboron Reagents 2.1.9 Coupling with Electron-Deficient Heteroarylamines 2.1.10 Selective C-N Bond Formation for the Synthesis of Heterocycle-Containing Compounds 2.1.11 Using Sulfonato-imino Copper(II) Complexes 2.2 C-O Bond Formation 2.2.1 Coupling with (Hetero)arylboron Reagents 2.2.2 Coupling with Alkyl- and Alkenylboron Reagents 3 C-Element (Element = S, P, C, F, Cl, Br, I, Se, Te, At) Bond Forma tion under Modified Chan-Lam Conditions 4 Conclusions. 2021 Georg Thieme Verlag. All rights reserved. -
Smart Systems for Disease Prediction: Advancements, Applications and Challenges
Smart Systems for Disease Prediction: Advancements, Applications and Challenges is a comprehensive book that explores the use of intelligent technologies to predict diseases accurately and efficiently. It covers a wide range of topics, including image and signal processing, behavioural analysis, and the integration of multimodal data in healthcare. The book examines the application of artificial intelligence, machine learning, and data analytics in creating predictive models for diseases. It also addresses the challenges, ethical considerations, and future directions in the field. This work emphasises the significant impact of intelligent systems on enabling early diagnosis, personalised medicine, and improving patient outcomes. 2026 S. Vijayalakshmi, Alwin Joseph, Naived George Eapen, Balamurugan Balusamy, Jagjit Singh Dhatterwal, and Kuldeep Singh Kaswan. -
An Enhanced RFM Customer Value-Based Customer Segmentation and Evaluation
Machine Learning Algorithms are widely used in the contemporary era of highly compatible technical improvements to provide answers to the challenges of business environment, yet crucial services for a firm to run successfully in this intensely competitive E-commerce sector. Recently, strategies like clustering and classification mechanisms that allow for the classification of both existing and new clients into clusters have also produced positive outcomes. Recency, Frequency, and Monetary (RFM) measures are hugely being used these days to perform these kinds of tasks. In this study, individual one-dimensional clustering on the Recency, Frequency, and Monetary columns was performed, and a weighted average or preferred linear combination of the three features was then used to calculate an overall score. Summing up the result of three individual clusters. Finally, all of the distinct clients were divided into these three segments based on the overall score, which was divided into three categories. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Smart Intelligence Aided Power and Energy Management
The Artificial Intelligence (AI) has become a revolutionary technology in power and energy management, providing exceptional prospects for improving efficiency, reliability, and sustainability. This study delves into the incorporation of AI methodologies into smart intelligence-driven systems for power and energy management. It delves into how AI algorithms, encompassing machine learning and optimization approaches, are utilized to enhance energy generation, distribution, and consumption across a range of environments, including smart grids, microgrids, and intelligent buildings. The abstract examines the primary challenges and factors to consider when implementing AI-driven solutions for power and energy management, which encompass issues such as data quality, privacy, security, and scalability. It emphasizes the crucial role of transparency and interpretability in AI algorithms to cultivate trust among stakeholders and secure user acceptance. Additionally, it addresses the importance of upholding ethical standards and regulatory requirements to address societal apprehensions and mitigate potential risks linked to the deployment of AI in energy systems. Moreover, the abstract highlights AIs contribution to advancing energy efficiency and sustainability through dynamic demand response, incorporating renewable resources, and the optimization of grid operations. It underscores the importance of on-going monitoring and evaluation of AI-driven energy management systems to pinpoint areas for enhancement and mitigate unintended repercussions. In summary, this paper offers perspectives on AIs potential to transform power and energy management methodologies, leading to more intelligent, robust, and eco-friendly energy systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Effective Models for Computing Optimized Storage Systems for Energy
This chapter investigates effective modeling techniques for designing optimized storage systems that minimize energy consumption. We explore various models capturing the interplay between storage performance, capacity, and energy efficiency, focusing on computational methods to enhance effectiveness. As the demand for renewable energy sources continues to increase, the need for reliable and efficient storage solutions becomes increasingly crucial. We discuss the design and implementation of optimized storage systems for energy, highlighting computational models role in improving efficiency. Starting with an overview of the energy storage system, we examine different modeling approaches such as mathematical optimization, machine learning, and simulation techniques. Each approach offers a unique approach to addressing the complexities of energy storage. Additionally, we discuss optimization models, ensuring that energy storage solutions are both technically efficient and economically viable. In summary, this section emphasizes the importance of computational modeling in developing efficient energy storage systems, which are crucial for meeting energy integration demands and ensuring stability and sustainability. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Easy and swift cane juicer with augmented bagasse brick maker /
Patent Number: 202121042785, Applicant: Thomas K T.
Sugarcane being one of the largest agriculture-based industry in India, provides subsistence to a huge number of farmers whose life is completely dependent upon sugarcane cultivation and its related products. One major source of income is extraction of Juice from sugarcane by road side vendors and farmers through a traditional machinery which turns out to be old and tedious job to perform. This also results into increased waiting time for customers as the task requires repetition of similar activity of by vendors in order to extract satisfactorily amount of juice from sugarcane by obsolescence machinery. -
Easy and swift cane juicer with augmented bassage brick maker /
"Patent Number: 202121042785, Applicant: Thomas K T.
Sugarcane being one of the largest agriculture-based industry in India, provides subsistence to a huge number of farmers whose life is completely dependent upon sugarcane cultivation and its related products. One major source of income is extraction of Juice from sugarcane by road side vendors and farmers through a traditional machinery which turns out to be old and tedious job to perform. This also results into increased waiting time for customers as the task requires repetition of similar activity of by vendors in order to extract satisfactorily amount of juice from sugarcane by obsolescence machinery. -
Smart assistive device for visually impaired /
Patent Number: 202121042792, Applicant: Dr. S. Vijayalakshmi.
The most difficult problem for blind people is to navigate the outside world. Smart devices make it much easier for visual impaired to complete daily tasks like smart speakers, smart bulbs, household smart devices, smart sticks, and many more are easy to control. As a whole, smart devices have the potential to significantly improve the lives of visual impaired people. All apps and devices are used individually for different purposes but for visually impaired person it is very difficult to handle all devices and apps at a particular time. -
Audio Recognition of Animals Using Optimized Deep Learning Techniques for the Conservation of Wildlife
The classification of animal sounds has emerged as a vital tool in contemporary research, offering numerous benefits for animal occurrence records, taxonomic research, and behavioral studies. However, the problem of accurately identifying animal species based on their vocalizations remains a significant challenge, particularly in real-world environments where background noise and variability in sound patterns can hinder classification accuracy. In this paper addressed this challenge by proposing a CNN-optimized approach for classifying animal sounds. In order to enhance the number of sound samples, utilized augmentation techniques to extract animal sounds from the Kaggle animal sounds dataset. The animal sounds totally 600 audio samples are used. To improve performance, this model was developed using feature extractions from the MFCC, ZCR, and Mel-Spectrogram. The seamless deployment of forest department workers is ensured by the interpretability of our model for real-world applications related to wildlife conservation and monitoring. The main goal is to successfully identify animals using auditory properties, such as tiger, leopard, elephant, and otter noises, based on their vocalizations. Additionally, The optimized CNN and LSTM for sound classification. The Optimized CNN outperformed all other models, achieving an outstanding 98.32 % training accuracy rate. 2025 IEEE. -
A Comprehensive Meta-Analysis on Animal Identification Using Machine Learning and Deep Learning
Artificial Intelligence (AI)-based models have shown promising results in the identification of animals breeds. The surge in the development of new models has opened up new avenues for computer vision. The growing need to achieve cent percent accuracy in the prediction, identification and classification of data/images has motivated researchers to develop innovative strategies seamlessly. The results of various AI models are analyzed in terms of their classification accuracy. EfficientNet-B0 provided an accuracy of 95% in cat breed identification. InceptionV3 deep learning model reached the maximum accuracy of 96.75%, 96.57%, and 100% on dog, goat, and pig breed identification, respectively. ResNet attained an accuracy of 85.77% on snake species identification. This article provides an in-depth analysis of animal classification/species identification models. The inferences drawn out of this literature review would help the researchers in the selection of an ideal AI model to develop an automated animal classification model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A refined mechanism for human face recongnition from video footages
Undoubtedly homeland security is a noteworthy concern in today's increasingly connected world and there is a bevy of IT-based security solutions and services emerging and evolving to guarantee the safety and security of people and properties. Security and surveillance cameras are the prominent security solutions. People movements, gestures, and activities are being minutely monitored, captured as images and videos, and subjected to a variety of investigations in order to extract anything uncommon. As videos capture the movement, there is no requirement for the user to coordinate with the camera; video arrangements need not contain face images alone despite the fact that human appearances are vital articles in video sequences. Henceforth recognising a face from the sequence of a video turns into an essential undertaking in PC vision applications. As the user is not totally coordinating with the camera, it is not necessary that the face captured in the video sequence is the frontal face. At times, it need not be a human face. Hence, it is necessary to detect the human face region in a frame. If a human face is detected in a video sequence, there are chances that the detected face can be masked. It is also possible that the faces can be captured in various poses. There is also a difficulty in identifying a face subjected to various illuminations. The above natural possibility of not capturing the frontal face of a human in a frame makes the identification task difficult. This problem prompts researchers working under face recognition technology to design an improved framework that increases the recognition rate in the above situations. From the above-mentioned possibilities that deteriorate the recognition rate of a face, in this thesis work, two major problems namely face with varying pose and partially occluded faces are considered for recognition from a captured video. -
Multilayer classification based Alzheimer's disease detection
Hippocampus, a small brain region plays a role in the initiation of the neurodegenerative pathways that leadto Alzheimer's. Humans with MCI are probable to develop Alzheimer's disorder. Hippocampal volume has been proven to indicate which patients with MCI will later develop Alzheimer's. Brain degeneration in MCI progresses over time and varies from person - to - person, making early detection difficult. Magnetic resonance imaging is a tool in diagnosing clinically suspected Alzheimer's disease. Information about the historical development of structural changes as the disease progresses from preclinical to overt stages is shaping understanding of the disease, and also guides diagnosis and treatment decisions in the future. In this study, we developed a new multilayer classification method to identify Alzheimer's disease from brain MRI using contour model and multilayer classifier. This method is evaluated on 436 samples of OASIS dataset and achieved accuracy of method is 93.75 %. 2024 Author(s). -
Prediction of heart disease using XGB classifier
Predicting heart disease in advance could be a significant medical breakthrough because it is widespread. A reliable strategy that can be utilized to do this is machine learning. Decision tree classifiers, random forests, and multilayer perceptron have all been used in studies to predict heart disease. However, several of these techniques could be improved, like poor precision. In our research, we have taken the South African heart Disease dataset and implemented a few models, which include Support Vector Machine (SVM), K Neighbors (KNN), Artificial neural network and XG Boost Classifier. We have used different methods for measuring performance. SVM with 69.0 accuracy, KNN with 86.0 accuracy, and ANN with 80.0 accuracy. However, the XGB classifier has shown some promising results in predicting heart disease with an accuracy of 90%. Further, when the hyperparameters were tuned using the random search method, the accuracy increased to 92.8%. The benefit of this work is that it uses machine-learning approaches to enhance the performance of coronary heart disease prediction. 2024 Author(s). -
Portfolio Management Decision Support System Using Cryptocurrencies and Traditional Assets in Indian Context
The paper attempts to develop a portfolio management decision support system (PMDSS) to help the investors to ensure portfolio optimization in Indian context. For this, the study conducts a comparative analysis between portfolios with equities from Indian market and portfolios that includes cryptocurrencies along with equities. Considering the huge hype received by cryptocurrencies in the current scenario, we attempt to diversify portfolios by including risky assets like cryptocurrencies, specially focusing from Indian perspective. Till now, Portfolio Optimization with Monte Carlo Simulation and Hierarchical Risk Parity has not been implemented by combining portfolios of cryptocurrencies and Indian stocks together. Traditional assets for the study are selected upon their market capitalization, Earnings Per Share, Profit margin, Operating profit etc. and cryptocurrencies are chosen according to their market capitalization. Data on the daily prices of these assets are collected from 201920 to 202122. An attempt is made to optimize the portfolios by minimizing the portfolio standard deviation and maximizing the portfolio expected returns. This helps to minimize risk and maximize the possible returns that might arise from the portfolio after adjusting for the volatility of the asset classes respectively. Based on the results, we suggest not to incorporate cryptocurrencies in portfolios with Indian stocks. This is because the risk adjusted returns of cryptocurrencies are comparatively lower as compared to the other under study. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
The Role of Artifcial Intelligence in Renewable Energy
Technology is evolving at an unbelievable pace, to the extent where many of us cant keep up effectively. With increasing Artifcial Intelligence (AI) complexity, our environment will be transformed in amazing ways over the years and decades that follow. The renewable energy (RE) sector is no different. AI can observe patterns and beneft from large amounts of knowledge. Consequently, AI is able to make improvements to enhance energy production, conversion, and even delivery. These systems allow precise forecasting of, for example, weather and loads, mitigating, among countless other uses, the possibility of electrical surges. AI systems would signifcantly improve the productivity of renewable systems by automation over the next 10 years. For solar and wind energy, this will become particularly prevalent. Independent power producers would have the latitude required to deliver ever-more sustainable business models and services by integrating increased generation coupled with low-cost savings provided by automation. We are all aware of the requirements of RE, including solar power. However, how can AI help to increase the availability of RE? The demand for global energy is growing day by day, but fossil fuels cannot fulfll our future needs for energy. Because of increased energy consumption, fossil fuel carbon emissions have reached very high levels over time. RE, however, is emerging as a good replacement for fossil fuels. It is safer and also very clean in comparison to traditional sources. The RE industry has made tremendous strides during the preceding decade with developments in technology. AI and machine learning technologies can analyze data to predict the future. So, the use of AI can solve the problems and challenges of RE. In this chapter we discuss RE, its sources and challenges, and how AI can address these challenges. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Blockchain security for artificial intelligence-based clinical decision support tool
For a healthcare organization, it is very difficult to satisfy the growing challenges and cost and provide good quality care. But nowadays clinical decision support system becomes an essential tool for a healthcare organization to help healthcare experts enhance the treatment process and advance healthcare services. Clinical decision support system supports collaborative treatment to enhance medical services. In a collaborative treatment service, the patient's health records are shared by different healthcare experts. All the patient health data are maintained by an electronic health records system. Electronic health records have very sensitive and patient's private information so sharing electronic health records is a very challenging task. Some downsides of collaborative treatment are privacy and lack of confidence among contributors like a patient, doctors, radiologist, hospitals, and insurance organization. Blockchain which is known as distributed ledger technology and has a secured architecture framework can be used to enhance the healthcare organization. Blockchain with artificial intelligence has a great potential in helping healthcare traders tackle major healthcare issues and challenges. In this chapter, we discussed how artificial intelligence and blockchain as a powerful pair can transform the healthcare sector. We also discussed the model, challenges, and application in clinical decision support tools. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Big Data for Intelligence and Security
The name Big Data for Security and Intelligence is a method of analysis that focuses on huge data (ranging from petabytes to zettabytes) that includes all sources (such as log files, IP addresses, and emails). Various companies use big data technology for security and intelligence in order to identify suspicious tasks, threats, and security tasks. They are able to use this information to combat cyber-attacks. One of the limitations of big data security is the inability to cover both current and past data in order to be able to uncover identified threats, anomalies, and fraud to keep the n/wsafe from attacks. A number of organizations are addressing rising problems like APTs, attacks, and fraud by focusing on them. More is better than less! The easier it will be to determine. Nevertheless, organizations which utilize big data techniques make sure that privacy and security issues have been resolved before putting their data to use. Because there are so many different types of data stored in so many different systems, the infrastructure needed to analyze big data should be able to handle and support more advanced analytics like statistics and data mining. The one side of the coin is the collection and storing of lots of information; the other side is protecting massive amounts of information from uncertified access, which is very difficult. Big data is commonly used extensively in the improvement of security and the facilitation of law enforcement. Big data analytics are used by the US National Security Agency (NSA) to foil terrorist plots, while other agencies use big data to identify and handle cyber-attacks. Credit card companies use big data analytics tools to detect fraud transactions, while police departments use big data methods to track down criminals and forecast illegal activity. Big data is being used in amazing ways in todays information world, but security and privacy are the primary concerns when it comes to protecting massive amounts of data. Real-time data collection, standardization, and analysis used to analyze and enhance a companys overall security is referred to as Security Intelligence. The security intelligence nature entails the formation of software assets and personnel with the goal of uncovering actionable and useful insights that help the organization mitigate threats and reduce risks. To identify security incidents and the behaviors of attackers, todays analysts use machine learning and big data analysis. They also use this cutting-edge technology to automate identification and security events analysis and to extract security intelligence from event logs generated on a network. This chapter will discuss how Big Data analytics can help out in the world of security intelligence, what the appropriate infrastructure needs to be in order to make it useful, how it is more efficient than more traditional approaches, and what it would look like if we built an analytic engine specifically for security intelligence. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
AI and IoT in Improving Resilience of Smart Energy Infrastructure
In todays world, we cant live without energy. Its essential for the growth and development of the economy. Changes in climate, sustainable growth, health, food security for the world, and environmental protection all require it if we are to make any headway. Governments around the world are looking for innovative ways to generate, control, supply, and save energy because of the rising cost and rising demand for it. Photovoltaic systems, hydropower, wind energy, tidal power, and geothermal energy are examples of traditional renewable energy sources that have advanced significantly in recent years. They, however, are unable to deal with environmental variations. It is critical to developing smart and cost-effective generators in order to meet the advanced worlds energy demands. In this chapter, we introduced the concept of smart energy, smart grid, and smart energy systems in a brief manner. Smart energy portfolio and smart energy management are introduced in the frst section. We also discuss how AI and IoT can be used to improve the different energy sources like wind power, solar power, geothermal power, etc. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Genetic Algorithms for Wireless Network Security
[No abstract available]