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Enhanced Energy Efficient Routing for Wireless Sensor Network Using Extended Power Efficient Gathering in Sensor Information Systems (E-PEGASIS) Protocol
Recent technological advancement in wireless communication and sensors made Wireless Sensor Networks (WSNs) as one of the demanding platforms in the current scenario. In WSN, tiny sensor nodes are collecting and monitoring the biological data or physical data or environmental data and transmitted to the base station (BS) through gateway routers. These data can be accessed anywhere and anytime. Usually sensor nodes have restrained battery power which creates the rigorous lifetime duration issues in WSN. Sensor nodes can communicate with each other using various routing protocols. Data transmission devours more amounts of energy and power. So, energy preservation is an important factor in WSN. There are plenty of researches going on in designing less energy consuming protocols for data transmission which helps to increase the lifetime of WSN. In this paper we have proposed Extended Power Efficient Gathering in Sensor Information Systems (E-PEGASIS) protocol for enhanced energy efficient data transmission based on PEGASIS protocol. In this proposed method average distance between the sensor nodes are considered as the criterion for chaining and fix the outermost node's radio range value the base station. Later it chains the related nodes available in the radio range. Consequently, the chained node checks their distance with the next nearest end node to go on with the chaining procedure which will enhance the performance of data transmission between the sensor node and the base station. The simulation of the proposed work shows that lifetime of the network is increased when comparing to the LEACH and PEGASIS protocol. 2021 The Authors. Published by Elsevier B.V. -
Optimization of sustainable portfolios considering behavioral biases: ESG risk management
As the role of sustainability is gaining importance among investors, they are more focused on adopting ESG principles into their portfolios. Despite this, bringing a balance between financial returns and sustainability objectives is frequently challenged by the behavioral biases affecting investor's decision- making. Biases like herd behavior, overconfidence, and loss aversion disrupt the investor's investment decisions, weakening the effectiveness of ESG strategies and negatively affecting portfolio returns. Therefore, it is essential to embed behavioral finance concepts into optimizing sustainable portfolios. This research explores the optimization of sustainable portfolios by addressing behavioral biases and the application of effective risk management techniques. The research identifies the significant cognitive biases that shape investor's behavior in the context of ESG investing. The research then investigates how these biases influence financial returns and ESG goals. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Long-term optical and infrared variability characteristics of Fermi blazars
We present long-term optical and near-infrared flux variability analysis of 37 blazars detected in the ?-ray band by the Fermi Gamma-Ray Space Telescope. Among them, 30 are flat spectrum radio quasars (FSRQs) and 7 are BL Lac objects (BL Lacs). The photometric data in the optical (BVR) and infrared (JK) bands were from the Small and Moderate Aperture Research Telescope System acquired between 2008-2018. From cross-correlation analysis of the light curves at different wavelengths, we did not find significant time delays between variations at different wavelengths, except for three sources, namely PKS 1144-379, PKS B1424-418, and 3C 273. For the blazars with both B- and J-band data, we found that in a majority of FSRQs and BL Lacs, the amplitude of variability (?m) in the J band is larger than that in B band, consistent with the dominance of the non-thermal jet over the thermal accretion disc component. Considering FSRQs and BL Lacs as a sample, there are indications of ?m to increase gradually towards longer wavelengths in both, however, found to be statistically significant only between B and J bands in FSRQs. In the B-J v/s J-colour magnitude diagram, we noticed complicated spectral variability patterns. Most of the objects showed a redder when brighter (RWB) behaviour. Few objects showed a bluer when brighter (BWB) trend, while in some objects both BWB and RWB behaviours were noticed. These results on flux and colour characteristics indicate that the jet emission of FSRQs and BL Lacs is indistinguishable. 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Search for low-mass objects in the globular cluster M4. I. Detection of variable stars
With every new discovery of an extrasolar planet, the absence of planets in globular clusters (GCs) becomes more and more conspicuous. Null detection of transiting hot Jupiters in GCs 47 Tuc, ? Cen, and NGC 6397 presents an important puzzle, raising questions about the role played by cluster metallicity and environment on formation and survival of planetary systems in densely populated stellar clusters. GCs were postulated to have many free-floating planets, for which microlensing (ML) is an established tool for detection. Dense environments, well-constrained distances and kinematics of lenses and sources, and photometry of thousands of stars simultaneously make GCs the ideal targets to search for ML. We present first results of a multisite, 69-night-long campaign to search for ML signatures of low-mass objects in the GC M4, which was chosen because of its proximity, location, and the actual existence of a planet. M4 was observed in R and I bands by two telescopes, 1 m T40 and 18-inch C18, of the Wise Observatory, Tel Aviv, Israel, from 2011 April to July. Observations on the 1 m telescope were carried out in service mode, gathering 12 to 48 20 s exposures per night for a total of 69 nights. C18 observations were done for about 4 hr a night for six nights in 2011 May. We employ a semiautomated pipeline to calibrate and reduce the images to the light curves that our group is developing for this purpose, which includes the differential photometry package DIAPL, written by Wozniak and modified by W. Pych. Several different diagnostics are employed for search of variability/transients. While no high-significance ML event was found in this observational run, we have detected more than 20 new variables and variable candidates in the M4 field, which we present here. 2016. The American Astronomical Society. All rights reserved. -
Internet of things: Service-oriented architecture opportunities and challenges
Internet of Things is now a subject that is increasingly growing on both the job and modern devices. It is a concept that maybe not just get the potential to influence how we live but in addition how we work. Intelligent systems in IoT machines in many cases are used by various events; consequently, simultaneous information collection and processing are often anticipated. Such a characteristic that is exclusive of systems has imposed brand new challenges towards the designs of efficient data collection processes. This article is to be discussing various layers in Internet of things. Those layers are sensing layer, network layer, service layer and application layer. Various data processing techniques are integrated along with data filtering and data conversion. Protocol transformation is also feeling the major challenges faced by enterprises wanting to shift to the style in brand new technology. Springer Nature Singapore Pte Ltd. 2020. -
Measuring Indian Blackbuck Antilope cervicapra (Mammalia: Cetartiodactyla: Bovidae) abundance at Basur Amruth Mahal Kaval Conservation Reserve, Chikkamagaluru, southern India
Grasslands are among the most critically endangered ecosystems in the tropics, but they are often treated as wastelands and conservation efforts are seldom directed towards these landscapes. The Blackbuck Antilope cervicapra is a large wild herbivore found in most grassland ecosystems across India. Despite their critical role in their trophic web, there are no reliable estimates of Blackbuck populations from their geographic range that takes detection probability into consideration. In this study, we conducted field surveys to estimate Blackbuck density in Basur Amruth Mahal Kaval Conservation (BAMKCR) with an area of 7.36km2 in southern India. We surveyed Blackbucks for a week in July 2014 along straight line transects between 09:00-12:00 hr and used the distance sampling approach to address the imperfect detection. A total of three transect lines of lengths 3.01km, 2.4km and 1.2km were sampled for seven temporal replicates. With an effort of 46.27km, 56 sightings of Blackbucks were recorded that was analyzed using the program DISTANCE. With a detection probability of 0.58 (0.053 SD) the estimated density of Blackbuck was 26.23 (6 SD) individuals/km2. The derived abundance estimate was 193 (c. 148-238) individuals in the study area. Our results show implications of a statistically robust design that accounts for imperfect detection. It provides an insight into a resident population of Blackbucks in a dynamic and fragile habitat. Blackbuck density estimate from this study sets the background for periodic monitoring of their populations, examination of the impacts of habitat modifications and gauge long-term viability of the grassland habitat in BAMKCR. Sagar & Antoney 2017. -
Current advancements in AI-driven education
With the rise of Artificial Intelligence (AI), smart computing, and high- speed internet, education is undergoing a profound transformation. Learning is no longer confined to traditional classrooms; instead, AI- driven tools are making education more personalized, interactive, and accessible to students worldwide. In an era where global migration, conflicts, and resource shortages pose serious challenges to education, AI offers innovative solutions to bridge learning gaps and ensure that quality education reaches even the most underserved communities. Additionally, today's learners-often referred to as cyber nomads-are growing up surrounded by technology, making it essential to adapt educational methods to their digital-first way of learning. AI is also proving to be a game- changer for students with special needs, such as those with autism, by offering tailored support that traditional systems struggle to provide. This chapter delves into three major areas where AI is reshaping education. First, AI-powered personalized learning systems are revolutionizing how students learn by offering adaptive platforms, AI tutors, and real- time analytics that customize education to fit individual needs. Second, AI- driven content creation and assessment are enhancing the way educational materials are developed, automating lesson planning and grading while integrating interactive experiences like gamification and AR/VR- based learning. Finally, AI- driven administrative and institutional innovations are streamlining operations in schools and universities, making tasks like enrolment, scheduling, and performance tracking more efficient, while also using predictive analytics to help students stay on track. By exploring these advancements, this chapter aligns with the book's goal of highlighting cuttingedge AI applications that are shaping the future of education. It provides insights into how AI is transforming learning at all levels-from kindergarten to higher education-and discusses its potential to create a more inclusive, efficient, and engaging educational experience. This chapter will serve as a valuable resource for educators, administrators, and technology developers looking to harness AI's power to build a smarter, more adaptable learning environment for the next generation. 2025, IGI Global Scientific Publishing. All rights reserved. -
Climate-Smart Livelihood - A Case Study of Dodaballapura Taluk of Bangalore Rural District
More than a billion farmers around the world are on the frontier of climate change. These farmers' livelihoods are directly and indirectly affected by the impact of climate change. Climate smart livelihood explains the practices in agriculture sector which sustainably contributes to productivity and income. This study tries to explore the adaptation of climate smart livelihood techniques by the farmers in the Doddaballapur taluk of Bangalore rural district. The data was collected primarily from the five villages and 50 households of Doddaballapur taluk. The survey revealed that 81.67% of the respondents faced problems during adaptation of climate smart agriculture was due to poor support of local and national authorities with climate related issues and ranked it one of the major constraints. This was followed by lack of financial constraints, lack of knowledge about adaptive practices (78.50%), non-availability of agriculture inputs in time (76.17%), lack of education about the adaptation strategies (75.33%), unavailability of new technologies (78.83%), higher cost of the agricultural inputs used for the practices (71.17%), lack of improved communication facility about the climate change (71 %), migration of youth due to urbanization and better employment (70.83%), lack of knowledge about post-harvest technology (68.83%), lack of awareness about climate change issues (59.83 %). The study reveals that as most farmers believe they have low capacity to adapt to climate-smart agriculture due to lack of availability of resources. Government can help farmers through National Agricultural Extension Project (NAEP), Krishi Prashasthi, etc. 2022 - Kalpana Corporation. -
Blockchain based emanative unassailable system: Use cases and repercussions /
International Journal of Recent Technology And Engineering, Vol.7, Issue 6S5, pp.540-543, ISSN No: 2277-3878. -
Haptics: Prominence and Challenges
Derived from a Greek word meaning sense of touch, Haptic is a communication technology which applies tactile sensation for human-computer interaction with computers. Haptic technology, or haptics, is a tangible feedback technology that takes benefit of a users sense of touch by applying forces, sensations, or motions to the user. These objects are used to methodically probe human haptic capabilities, which would be complex to achieve without them. This innovative research tool gives an understanding of how touch and its core functions work. The article will provide a detailed insight into the working principles, uniqueness of the technology, its advantages and disadvantages along with some of its devices and notable applications. Future challenges and opportunities in the field will also be addressed. 2020, Springer Nature Switzerland AG. -
Machine Learning Based Time Series Analysis for COVID-19 Cases in India
The World Health Organization declared the Coronavirus Infection, or COVID-19, to be widespread. One of the most appropriate methodologies for COVID-19 is time series analysis. The most appropriate technique for COVID-19 is time series analysis. It can be applied to Recognizing Information Patterns and Predicting Insights. The paper summarises the components of time series using the COVID-19 dataset for India as an example of one of the most important methodologies in predictive analytics. Time series models are chosen because they can predict future outcomes, comprehend prior outcomes, provide strategy recommendations, and much more. These common goalrists of temporal arrangement modelling do not differ significantly from those of cross-sectional or board data modelling. Machine Learning may be a well-known fact that it is an excellent technique for imagining, discourse, and standard dialect management for a large clarified accessible dataset. The results for confirmed, recovered, and death cases are presented in this study. 2022 IEEE. -
Risk management of future of Defi using artificial intelligence as a tool
This chapter explores AI's pivotal roles in managing risks within DeFi, emphasizing strategic implementation to enhance risk assessment, management, and decisionmaking processes for a better user experience. The convergence of AI and DeFi presents unprecedented opportunities, fostering transparency and decentralization. Drawing from diverse sources, the study evaluates AI's effectiveness, particularly in machine learning, in addressing emerging risks. It focuses on how AI can guide DeFi's future while managing market and credit risks through tasks like data preparation, modeling, stress testing, and validation. Additionally, AI aids in data quality assurance, text mining, and fraud detection. Emphasis is placed on identifying and managing risks that could hinder DeFi's future, highlighting key AI techniques. Given the financial industry's ongoing transformation, these insights are increasingly vital. 2024, IGI Global. All rights reserved. -
Learning foreign languages: A comparative analysis of online learning process vs. traditional educational processes /
Internet has pervaded every aspect of the life of the modern person today in the contemporary world. The fact that the education sector is undergoing vast amount of change in terms of the digital revolution via the internet medium is exemplary of the powerful aspect of the internet medium. This is also the case with the practice of foreign languages by many people, especially the urban educated youth. -
Pixels to Pathogens: A Deep Learning Approach to Plant Pathology Detection
It is known that accurately identifying, early and timely treatment and elimination of the plant diseases is essential for crop protection and healthy crop growth. In traditional or conventional methods, identification and classification were done by testing in laboratories or through visual inspection by farmers. Now going through the testing in labs is very time consuming, while the visual inspection requires enough experience and knowledge. To solve this problem, our study proposes a robust plant pathogen detection method based on a Deep Learning approach on a large dataset containing about 38 categories of different species like Maize, Potatoes, Tomatoes, Bell Pepper, Peach, Strawberry etc. and diseases like rust, molds, blight (late and early). This crop disease detection model leverages the power of the EfficientNetB3 architecture, a state-of-art convolutional neural network(CNN). The main backbone is served by EfficientNetB3and then it is fine-tuned using different hyperparameters and other regularization techniques like weight decay, dropout method and optimizers like RAdam,to enhance the overall accuracy coupled with dynamic learning rate adjustment. In the testing set of the dataset, the proposed model shows encouraging accuracy of about 99.25%, high precision of about 97.35%. A thorough evaluation of the model's functionality is given by the help of training and validation line chart and loss chart that gives the in-depth information on the prediction. And then we implemented the detection model in our mobile application whose interface screen shots are given below. In the application the image can be taken by camera or fed from folders and it will detect the type of disease. 2024 IEEE. -
Fake News Detection using Machine Learning and Deep Learning Hybrid Algorithms
Spreading misinformation or fake news for personal, political, or financial gain has become very common these days. The influence of this misinformation on peoples opinions can be significant, i.e., the 2016 presidential election in the United States was a perfect illustration of how false news may be used to deceive people. In todays fast-paced world, automatic detection of fake news has become an importantrequirement. In this paper, multiple machine learning algorithms have been implemented to perform classification. A proposition of a hybrid architecture consisting of CNN along with LSTM has also been made. The proposed model outperforms the other traditional approaches. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Effect of COVID-19 on ETF and index efficiency: evidence from an entropy-based analysis
We examine the informational efficiency of domestic equity ETFs vis-a-vis their underlying market indices during the COVID-19 pandemic. To do so, we employ a multiscale entropy-based methodology. Our findings indicate that the informational efficiency of all ETFs as well as the indices fall sharply during the COVID induced market crash in February-March 2020. Having said so, we find disproportionate deterioration in market efficiency of ETFs and indices pertaining to USA and Canada as compared to those of China, Hong Kong and Taiwan. Interestingly, ETFs and indices pertaining to certain developed markets were found to be less efficient than their emerging market counterparts even during the pre-covid timeline. Lastly, there is a discernible difference between the efficiency of ETFs vis-a-vis their underlying indices. These findings should nudge investors to exercise caution while dealing with ETFs, for domestic ETFs do not exactly mimic the dynamics of their underlying indices. 2021, Academy of Economics and Finance. -
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A census of young stellar population associated with the Herbig Be star HD 200775
The region surrounding the well-known reflection nebula, NGC 7023, illuminated by a Herbig Be star, HD 200775, located in the dark cloud L1174 is studied in this work. Based on the distances and proper motion values from Gaia DR2 of 20 previously known young stellar object (YSO) candidates, we obtained a distance of 335 11 pc to the cloud complex L1172/1174. Using polarization measurements of the stars projected on the cloud complex, we show additional evidence for the cloud to be at ?335 pc distance. Using this distance and proper motion values of the YSO candidates, we searched for additional comoving sources in the vicinity of HD 200775 and found 20 new sources, which show low infrared excess emission and are of age ?1 Myr. Among these, 10 YSO candidates and 4 newly identified comoving sources are found to show X-ray emission. Three of the four new sources for which we have obtained optical spectra show H ? in emission. About 80 per cent of the total sources are found within ?1 pc distance from HD 200775. Spatial correlation of some of the YSO candidates with the Herschel dust column density peaks suggests that star formation is still active in the region and may have been triggered by HD 200775. 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Star formation around three co-moving HAeBe stars in the Cepheus Flare
Context. The presence of three more Herbig Ae/Be (HAeBe) candidates in the Cepheus Flare within a 1.5 radius centered on HD 200775 suggests that star formation is prevalent in a wider region of the LDN 1147/1158, LDN 1172/1174, and LDN 1177 clouds. A number of young stellar objects (YSOs) are found to be distributed toward these cloud complexes along with the HAeBe stars. Various star formation studies clearly indicate ongoing low-mass star formation inside the clouds of this region. Sources associated with less near-infrared excess and less H? emission raise the possibility that more low-mass YSOs, which were not identified in previous studies, are present in this region. Aims. The aim is to conduct a search for additional young sources that are kinematically associated with the previously known YSOs and to characterize their properties. Methods. Based on the Gaia DR2 distances and proper motions, we found that the HAeBe candidates BD+681118, HD 200775, and PV Cep are all spatially and kinematically associated with previously known YSOs. Based on the Gaia DR2 data, we identified a number of co-moving sources around BD+681118. These sources are characterized using optical and near-infrared color-color and color-magnitude diagrams. Results. We estimated a distance of 3407 pc to the whole association that contains BD+681118, HD 200775, and PV Cep. Based on the distance and proper motions of all the known YSOs, a total of 74 additional co-moving sources are found in this region, of which 39 form a loose association surrounding BD+681118. These sources are predominantly M-type sources with ages of ?10 Myr and no or very little near-infrared excess emission. The distribution of co-moving sources around BD+681118 is much more scattered than that of sources found around HD 200775. The positive expansion coefficients obtained via the projected internal motions of the sources surrounding BD+681118 and HD 200775 show that the co-moving sources are in a state of expansion with respect to their HAeBe stars. A spatiooral gradient of these sources toward the center of the Cepheus Flare Shell supports the concept of star formation triggered by external impacts. 2021 ESO. -
Interpreting Scope of Predictive Analytics in Advanced Driving Assistant System
Distracted driving, caused by various factors such as human emotions or reading distracting messages on the roadside, has become a leading cause of traffic accidents today. Ensuring the safety of both individuals and vehicles while minimizing maintenance costs poses a significant challenge for the automotive industry. Fortunately, recent advancements in machine learning offer a potential solution. One promising method is the further development of Advanced Driver Assistance Systems (ADAS), for which machine learning serves as an ideal solution. The proposed model develops an advanced predictive learning enabled driving assistance system with prediction capabilities like traffic light behavior and parking availability detection. The model gave an optimum accuracy of 98.2% with 50 epochs count and the validation loss retains a constant value of 0.3 over epochs. 2023 IEEE.