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Community Empowerment through Technology
A UN report estimated that 10% of people on the planet will experience chronic hunger by 2022, with a large share of those people coming from rural origins. In Manipur, a Northeastern state of India characterized by hilly terrain and sparse population, predominantly inhabited by Nagas and Kukis tribes, urban migration threatens to exacerbate poverty. Although hunger is not frequent among Nagas at the moment, one cannot rule it out in future. When smart technologies ably support agriculture, it increases production in a sustainable way, thereby reducing hunger among the tribal populace. For this, the government must assist farmers in discouraging environmentally damaging activities like Jhum cultivation and cultivating poppies. For now, tribals have food to eat; nevertheless, future hunger problems could result from a lack of assistance. This research imagines a future where environmental sustainability is maintained while supplying for the requirements of the people of Manipur by giving technology-assisted farming prime importance. By using a participatory observation approach, the authors highlight how urgent it is to stop more ecological deterioration and protect the biodiversity of the area. Ultimately, a balanced ecological future for Manipurs rural and urban inhabitants depends on the adoption of sustainable smart agriculture. 2025 A. Jose Anand and Saravanan Krishnan. -
Indigenous Beliefs and Practices for Sustainability Among the Mao Nagas
The present society of Mao Nagas is sandwiched between trends to modernity and tendencies to be rooted in the cultural past. Prior to the arrival of Christianity, the Maos were considered animists; the sway of the one Supreme Being, and human relations with nature permeated the social, cultural, and spiritual realm. When the sky represented the father, and the earth, the mother; exploitation becomes inconsequential. Despite the odds of having limited ancestral land, the Maos have proven themselves self-sustainable within the place of habitation. The fact that there are no beggars among the Maos proves that certain aspects of the SDGs are ingrained in the beliefs. The Feast of Merit prevented extreme riches in society. With education, the Mao Nagas learned the harmful effects of shifting cultivation and abandoned its entirety. This paper tries to conceptually prove that if ancient beliefs and practices are tempered with scientific knowledge, life is sustainable. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Reshaping the Education Sector of Manipur Through Blockchain
The use of technology in education has been over a century, yet blockchain is in its nascent stage in education. Over the years, technology has enhanced the teaching-learning method, and blockchain can improve even in the administrative section of education. The states of North East, India, in general, lag behind the rest of Indian states in almost all sectors, and the lack of transparency in the administrative sector significantly contributed to it. If blockchain is incorporated into the education department at the administrative level, it could pave the way for a faster, more transparent, and smoother administration. Given the harsh reality that transportation is hard and expensive, a standardised blockchain can alleviate the need to be physically present for any academic-related activity. The attempt of this study would be to show how blockchain can be beneficially used even at the institutional level, where unabated printing could be reduced and adopting to e-paper be maximised. Besides the educational institutions, the administrative sector in education could profitably use them in offices, thereby avoiding red tape for the common people. The chapter points out how blockchain can be a trailblazer in reshaping the education sector in Manipur. Educational institutions must take the lead towards a sustainable future, and blockchain can aid in bringing some visible change in the educational sector. This chapter uses an interdisciplinary approach to substantiate the importance and need for blockchain in the context of Manipur to change for a sustainable future. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Critical Estimation of CO2Emission Towards Designing a Framework Using BlockChain Technology
The automobile industry is a significant global contributor of carbon footprint this industry has impacted climate change, the research explores the existing methods of carbon footprint tracking and creates a framework by applying blockchain technology by connecting all the countries into one system as blockchain carries the capability to do due to its transparency, security and immutability the proposes of decentralised framework for real time tracking quarterly and implementing the necessary policies to mitigate the raising emission. The methodology encompasses of data analysis of using time series analysis globally and focusing certain parts of the world to show the emissions and creating a design that can help us in tracking the carbon footprint making all over the countries around to participate in suggesting to create a pathway for the future generations a better world as advance technologies come into the world for better ways to save the environment. 2024 IEEE. -
Confrontations faced by women in higher education institutions and strategies to overcome the anomalies in the mid-career
Women do succeed in higher positions in the higher education system but only to a certain point, and many women are really motivated by the traditional academic values such as passion to the discipline, pursuit of knowledge, good working environment, and flexibility. Women in higher education .spend the majority of their life at the mid-career stage. Some of them feel wedged, undervalued, and find no motivation to go forward in their mid-career. Hence, the mid-career stage is very much important with women academicians, and they feel there is little support or mentoring. Hence, the mid-career period is increasingly difficult to navigate. Women encounter enormous obstacles in their academic career, including unequal task distribution and balancing caring responsibilities to name a few. The aim of this chapter is to discuss in detail the challenges and obstacles faced by women in their mid-career in higher education and a few strategies to overcome the encounters. 2022, IGI Global. All rights reserved. -
Generative AI and its impact on creative thinking abilities in higher education institutions
Generative AI technologies such as ChatGPT have started gaining increased popularity among higher education institutions. Students, as well as teaching professionals, can utilize these tools for various academic purposes due to the immense benefits they provide by way of customization of data generated and ease of access to data. However, this chapter seeks to analyze how such tools may impact students' creative thinking ability. It also analyses the drawbacks faced by teachers after implementation of such tools. The methodology adopted for the study was two surveys: one administered to gather students' opinions and the other for understanding teachers' perspectives. The analysis of the data collected shows that the over-reliance of students on such generative AI tools might hinder students' ability to think creatively to some extent. The chapter also suggests some of the strategies that can be adopted by teachers to ensure students' capabilities are assessed accurately. 2024, IGI Global. All rights reserved. -
COVID-19 outbreak prediction using quantum neural networks
Artificial intelligence has become an important tool in fight against COVID-19. Machine learning models for COVID-19 global pandemic predictions have shown a higher accuracy than the previously used statistical models used by epidemiologists. With the advent of quantum machine learning, we present a comparative analysis of continuous variable quantum neural networks (variational circuits) and quantum backpropagation multilayer perceptron (QBMLP). We analyze the convoluted and sporadic data of two affected countries, and hope that our study will help in effective modeling of outbreak while throwing a light on bright future of quantum machine learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
An IoT-based agriculture maintenance using pervasive computing with machine learning technique
Purpose: In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc. In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset. Design/methodology/approach: In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis. Findings: In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like Threshold segmentation and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained. Originality/value: The implemented machine learning design is outperformance methodology, and they are proving good application detection rate. 2021, Emerald Publishing Limited. -
Enhancing Investment Advisory with Machine Learning for a New Era in Financial Services
The current financial service environment, where the volatility of markets and the need to offer flexible solutions is growing, is starting to challenge the traditional investment advisory models. This paper implements a new framework, which incorporates the most advanced methods of machine learning, to make investment advising a process driven by real data. This is unlike the current models which are overly dependent on historical trends or fixed risk profiles, our system allows us to use real time behavior analytics, sentiment analysis and dynamic portfolio optimization to give hyper personalized investment recommendations. The framework feeds the ensemble learning, attention-based neural networks, explainable AI (XAI) to make sure the transparency, regulatory, and investor trust. The innovation in particular is based on the constant interaction between client and adjustment of the model in terms of a ready and sensitive advisory intervention. The study will not only improve the relevance and precision of financial advice, but will with its informed automation of advisor-client relationship led to a redefinition of the advisor-client relationship. The insights guide to a world of advisory services where ML and machine learning complement strategic decision-making with unheard levels of specificity and individuality. 2025 IEEE. -
The Impact of AI on High-Frequency Trading: A New Paradigm in Share Market Dynamics
A fresh approach in financial trading has emerged as a result of the significant shifts in the dynamics of global share markets brought about by the incorporation of artificial intelligence (AI) into high-frequency trading (HFT). In order to analyse large datasets in real-time, perform trades in microseconds, and AI-driven Deep learning, machine learning, and natural language processing are all examples of things that HFT uses to help people make the best choices they can in markets that have to change all the time. This change will help people make better decisions, and sellers will be able to respond quickly to changes in the market. Individuals can trade faster, better, and for greater amounts of cash with it than ever before. AI does have some problems when used in HFT, though. It can make the market less stable, lead to legal problems, and make it more diligently to be fair and honest. The amount of money, how well markets work, along with the way risks are handled are all changed by AI. It also discusses about how AI changes HFT. This study talks about the pros and cons of HFT powered by AI. Along with the way shares are sold, it also hints at how it might change future rules. 2025 IEEE. -
Machine Learning in Investment Analysis-Enhancing or Replacing Human Judgment
Machine Learning (ML) involvement in investment analysis is quickly revolutionizing the investment-based decisions through becoming highly accurate, quick, and embracing increased data processing capabilities. This paper is to research on whether ML is complementary or a possible replacement to human financial judgment. We run experiments over 1.2 million financial transactions between 150 firms comparing old style analyst recommendations and ML-models, including XGBoost, LSTM and Random Forest. The findings indicate that ML models outperformed prediction capability by 19.6 percent and lowered the volatility of the portfolios by 14.3 percent in 5-year investment. Also, the ML-Aided decision-making was better than human (only) approaches in 78 percent of the cases in markets with high volatility or that involved trading in complicated assets. The qualitative variables like regulatory policy changes and investor sentiment however were too difficult to decipher under the leadership of ML only. Our results indicate that ML supports rather than supers the human judgement and thus demonstrates a hybrid paradigm of decision making that resolves computational exactitude with context sensitive understanding in the modern investment scenarios. 2025 IEEE. -
AGRI 4.0 AND THE FUTURE OF CYBER-PHYSICAL AGRICULTURAL SYSTEMS
Agri 4.0 and the Future of Cyber-Physical Agricultural Systems is the first book to explore the potential use of technology in agriculture with a focus on technologies that enable the reader to better comprehend the full range of CPS opportunities. From planning to distribution, CPS technologies are available to impact agricultural output, delivery, and consumption. Specific sections explore ways to implement CPS effectively and appropriately and cover digitalization of agriculture, digital computers to assist the processes of agriculture with digitized data and allied technologies, including AI, Computer Vision, Big data, Block chain, and IoT. Other sections cover Agri 4.0 and how it can digitalize, estimate, plan, predict, and produce the optimum agricultural inputs and outputs required for commercial purposes. The global team of authors also presents important insights into promising areas of precision agriculture, autonomous systems, smart farming environment, smart production monitoring, pest detection and recovery, sustainable industrial practices, and government policies in Agri 4.0. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Data Science in the Medical Field
Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Colonial Migration and Cultural Transformation in India and Burma: Exploring the Role of Transnational Mobility in Shaping Chettinad Heritage
Migration has played an important role in the transformation of tangible and intangible cultural heritage across regions, including in the Global South, especially among postcolonial nations, due to their longstanding people-to-people contacts leading to socio-economic and historical-cultural transferences over time. In the case of India, global migratory forces have irreversibly transformed its tangible and intangible sociocultural landscape into a form of syncretism reflected in our civilizational ethos of Vasudhaiva Kutumbakam. In this context, this paper explores the role of international in-/outmigration in historical and contemporary times toward the evolution of India's regional cultural identities using a case of Chettiars' migration from the hinterlands of Chettinad in present-day Tamil Nadu, which is in the south of the Indian subcontinent, to the far-flung nation state of Burma/Myanmar in the northeast, including their subsequent return, primarily during the 19th and the 20th centuries. Using the 3i Framework (Interests, Ideas and Institutions), the study explains the role of cross-border migration in shaping the tangible and intangible heritage of Chettinad, as reflected in its architecture, cuisine and social customs. 2026 Association for the Study of Ethnicity and Nationalism and John Wiley & Sons Ltd. -
Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis
Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 20072022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price. 2023 World Scientific Publishing Co. Pte Ltd. All rights reserved. -
Relationships between Ultrasonographic Placental Thickness in the Third Trimester and Foetal Outcomes
Poor neonatal outcomes, including low birth weight (LBW), poor APGAR scores, more NICU hospitalizations, and a higher chance to develop Pre-Eclampsia, IUGR, and Oligo Hydramnios, are all linked to thin placental thickness. While both thin and thick placentae are connected to a greater prevalence of C-sections, thick placentae are linked with a greater possibility of developing GDM and an increase in NICU hospitalizations. Objective of this research was to investigate the association between placental thickness as measured by ultrasonography in the third trimester and foetal outcome, including the relationship between placental histopathology and placental thickness. investigate the link among placental thickness, foetal outcome, and placental histology. Most newborns had fibrinoid necrosis and calcifications. Babies with Macrosomia and IUGR, respectively, were more likely to develop Syncytial knots and thickening of the vessel wall. Patients with normal placenta thickness at 36 weeks' gestation experienced fewer difficulties than those with thin or thick placentas at the same time. The study emphasizes the value of evaluating placental thickness using ultrasound in the third trimester to detect high-risk pregnancies. The study also shows that aberrant foetal and neonatal events are linked to certain placental histological characteristics, like artery wall thickening and infarctions. RJPT All right reserved. -
Single Port Multimode Reconfigurable UWB-NB Antenna for Cognitive Radio Applications
In this paper, a compact, single port, multimode reconfigurable UWB-NB antenna with a novel feeding network is presented. The proposed antenna consists of a pentagonal-shaped monopole radiator, a beveled-shaped partial defected ground plane with a rectangular slot, and a reconfigurable bypass feeding network. The antenna realizes a wideband frequency range from 2.4 to 18 GHz and four narrow band frequency ranges, 5.3 to 6.8 GHz, 6.0 to 7.6 GHz, 7.2 to 8.8 GHz and 8.4 to 11.4 GHz. The antenna provides an omnidirectional radiation pattern with gain from 2.2 to 6.2 dBi maximum at 12 GHz and voltage standing wave ratio (VSWR) ranges from 1 to 2. The fabricated antenna has an overall dimension of 181.6 mm3. Sensing and tuning ranges of the fabricated antenna shows good agreement with the simulation results. The proposed antenna has an advantage of simple design, low profile, single port excitation and omnidirectional radiation pattern making it suitable for applications such as handheld mobile cognitive radio systems. 2022 SBMO/SBMag -
A Compact Super Wideband Antenna with Controllable Dual Notch Band Capability
In this paper, a novel super wideband (SWB) antenna with dual band notch capability is designed and analyzed for wide band applications. The proposed antenna consists of a pentagonal shaped radiator, beveled-shaped partial ground plane with slot and U-shaped parasitic strips. The beveled-shaped defected ground structure with rectangular slot helps to realize wideband characteristics from 2.4 to 28.2 GHz. Independent control of the notch band's center frequency and bandwidth is achieved by using U-shaped parasitic strips. This key feature is achieved in the WiMAX (3.3 to 3.7 GHz) and WLAN (5.1 to 5.9 GHz) bands. Furthermore, it exhibits a stable radiation pattern and offers acceptable gain over the entire operating bandwidth with sharp decrease in gain at the notches. The percentage bandwidth of 169% is achieved with a bandwidth dimension ratio (BDR) of 6986. Group delay is less than 1 ns in the entire operating bandwidth except at the notch bands. The measured reflection and radiation characteristics of fabricated SWB antenna are in good agreement with the simulation results. The proposed antenna has the advantage of simple design and compact size with an overall dimension of 18 x 21 x 1.6 mm3. The performance of the proposed antenna is superior compared to reported antenna designs in terms of controllable sharp notches and size for the bandwidth achieved. 2022 IAMOT -
Breaking the Glass Ceiling: Will the Role of Organizational Workplace Policies Perpetuate or Mitigate Gender Bias?
Despite significant global progress in narrowing gender gaps, inequality persists across many countries. Organizations like the Global Gender Gap Index and the European Institute for Gender Equality monitor improvements in political leadership, economic opportunities, health, and education. However, women continue to face challenges, including unequal pay, limited career advancement, and imbalanced household labor. The "glass ceiling" refers to invisible barriers that prevent women from achieving top positions despite equal qualifications. Long-term effects include temporary employment and lower retirement savings. True gender equality requires more than quotas-it demands equitable opportunities, flexible work policies, pay transparency, and mentorship programs. Tackling unconscious bias and fostering inclusive environments is essential for sustainable change and women's holistic success. 2026, IGI Global Scientific Publishing. All rights reserved. -
An introduction to multimodal data representation
The contemporary digital epoch is characterized by a radical transformation of data representation methodologies that imply increased intricacy as well as an enlarged bulk of data. An unimodal approach focusing on judicious data types, considered in isolation, was the earlier norm. The emphasis was on structured data, which had the advantage of being arranged systematically within relational databases and entity-relationship frameworks. This facilitated efficient data management. With the introduction of the internet and digital communication, such unstructured data as textual content, images, and audio began to be placed up front. But unimodal techniques were not adequately equipped to manage the intricate and interconnected nature of real-world phenomena. The welcome result was the development of multimodal data representation methodologies, which constitute a sophisticated paradigm that integrates data from such varied sources as text, images, audio, video, and sensor data. This results in a more holistic comprehension of complex scenarios. Distinct attributes and inherent challenges characterize each modality. To exemplify, text data need advanced natural language processing strategies to comprehend context and semantics; Image data necessitate methodologies well versed in managing spatial features and elevated dimensionality; audio data requires concentration on temporal patterns and noise; video data, on the contrary, integrates these complexities, leading to efficient processing techniques to accommodate its substantial volume and dynamic characteristics. The unsynchronous and heterogeneous sensor data complicate the integration of diverse data streams. Sophisticated fusion techniques, that is, early fusion, late fusion, and hybrid fusion, capable of integrating features from various modalities, are employed to mitigate the challenges faced by multimodal data representation. It increases interpretative insights and precision. The deep learning technologies, such as convolutional neural networks for image analysis, recurrent neural networks for sequential data processing, and attention mechanisms, have led to advancements in this domain. These models have become competent in recognizing complex patterns across modalities. Naturally, they bring about significant progress in domains such as health care, autonomous systems, multimedia processing, and natural language comprehension. This chapter explores the historical background of data representation, right from the beginnings in unimodal to its advancement in multimodal. The unique characteristics and challenges associated with each modality are scrutinized; Fusion techniques alongside contemporary deep learning models are examined; and underscore real-world applications, which are effective examples of the transformative potential of multimodal data representation. The chapter also emphasizes the necessity of escalating these methodologies in an increasingly data-centric world. It lays the foundation for advancements in the future with the goal of overcoming existing limitations and enlarging the scope of multimodal applications. 2026 Elsevier Inc. All rights reserved.