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Degradation of azodyes in wastewater by using hydrodynamic cavitation technique
The organic waste water discharged from various industries consists of large amounts of dyes & cyanides & other toxic carcinogenic pollutants which are harmful to human health & ecosystem. Release of carcinogenic dyes is hazardous & has a detrimental effect on the well being of an individual. The present work is focussed at finding the viability of hydrodynamic cavitations process in the degradation of dyes. To study the degradation, influence of various parameters on degradation rate has been studied. BEIESP. -
Origami foldaway support for beginners using image processing
Various origami works are distributed as origami books in which a succession of collapsing operations with basic outlines is portrayed. In any case, the origami book reader often will give up the instruction of the book middle, because it is too difficult to understand a way to fold in accordance with the diagrams. This paper proposes an approach to find the next step how to do the folding operation, especially for origami beginners. First, a method of detecting the folding operation based on camera images is been detected by canny edge detection. Then, in order to get the next operation camera image is been compare with the database images with the help of Bag of a visual word and Speed up robust features(SURF) detector to detect the key points by finding out the nearest neighboring distance ration (NNDR) measures to find out the similarities. 2016 Authors. -
Rayleigh-Bard convection in mono and hybrid nanoliquids in an inclined slot
Linear stability analysis is conducted to investigate the longitudinal and transverse rolls (TRs) generated in Rayleigh-Bard convection in mono and hybrid nanoliquids confined between two infinite inclined parallel slots. Thermophysical properties of six mono nanoliquids and fifteen hybrid nanoliquids are calculated for different volume fractions (0.5%, 1%, 2%) using phenomenological laws and mixture theory. The shooting method is used to solve boundary eigenvalue problems to obtain the eigenvalues for 16 different boundary conditions. It is observed that as the inclination angle is increased, it delays the onset of longitudinal rolls in the case of all boundary conditions. However, it advances the onset of TRs except when the lower plate is adiabatic. The addition of mono and hybrid nanoparticles results in the advancement of the onset of convection. The addition of SWCNT and SWCNT Al 2 O 3 accelerates the onset of convection the most while Cu and Cu-Ag accelerates the onset of convection the least amongst the mono and hybrid nanoparticles considered in the study. 2023 IOP Publishing Ltd. -
Analysis and Prediction of Suitable Model for Coconut Production Estimates in South Indian States
The study attempts to forecast coconut production in major coconut-producing states in India. The future projections on coconut production have been calculated based on yearly data for 73 years (194950 to 202122) accessed from the database of Indiastat (2022). We have used prominent forecasting techniques for the purpose and a suitable model has been chosen based on the lowest results of MAPE. The damped linear trend has been chosen for forecasting coconut production in Karnataka whereas Differenced first-order Auto Regressive model with drift has been adopted for Kerala and Karnataka. This study has considered a large dataset compared to other existing works and has chosen states that produce coconut on a large scale in India. Along with this, this study also attempts to find which state will produce more nuts for the Indian coconut industry, which can help the concerned stakeholders to take necessary decisions. Future projections depict that Kerala will continue to be the largest producer of coconut and Karnataka will show remarkable performance in coconut production during the upcoming four years post-study period. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Machine learning approaches towards medical images
Clinical imaging relies heavily on the current medical services' framework to perform painless demonstrative therapy. It entails creating usable and instructive models of the human body's internal organs and structural systems for use in clinical evaluation. Its various varieties include signal-based techniques such as conventional X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US) imaging, and mammography. Despite these clinical imaging techniques, clinical images are increasingly employed to identify various problems, particularly those that are upsetting the skin. Imaging and processing are the two distinct patterns of clinical imaging. To diagnose diseases, automatic segmentation using deep learning techniques in the field of clinical imaging is becoming vital for identifying evidence and measuring examples in clinical images. The fundamentals of deep learning techniques are discussed in this chapter along with an overview of successful implementations. 2023, IGI Global. All rights reserved. -
Empowering Renewable Energy Using Internet of Things
The massive communication of information over network gadgets associated with the internet trades data starting from one to another with no sort of human cooperation. As innovation is advancing, interconnected organizations give data to each other to impart. The energy utilization is happening at an extremely quick rate, debilitating the assets in delivering it at a similar rate, and the entirety of this requires a transformation to save energy. Information is the focal point of the Internet of Things (IoT), and it has all the information to which there was no entrance before; this information can be utilized in the revolution of the energy management framework. By utilizing advanced IoT innovations, the embracement of renewable can be upgraded signifcantly further. The reconciliation of IoT in renewable energy is empowering its development by and large. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
FortGen IDS: The Fusion of SOAR and Hybrid IDS for Enterprise
In this data era, enterprise are encountering rise of challenges in detecting and responding to cyberattacks. There is a need for a sophisticated cyber approach that leverages cutting-edge technologies to fortify against the unexpected attacks. This paper presents FortGen IDS, a novel cybersecurity solution combining Security Orchestration, Automation, and Response (SOAR) with Hybrid Intrusion Detection Systems (IDS). The primary contribution of FortGen IDS is its innovative algorithm inspired by Genghis Khans military tactics, enhancing threat detection and response, particularly against Distributed Denial-of-Service (DDoS) attacks. The proposed model leverages advanced automation and orchestration capabilities to provide a more holistic approach to enterprise cybersecurity. Empirical validation studies have been carried out to determine the best algorithm for anomaly detection, also explicitly comparing the performance of FortGen and Hybrid IDS. It helps make businesses digital defenses stronger against evolving cyber threats. This approach has greater scope in improving cyber-defense in the context of enterprise security, ensuring that firms are well-fortified against potential cyber threats. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
HashPress: Building a Green Data Center for Tomorrow
Green data centers are a game-changer for the environment, offering substantial benefits over traditional data centers. They prioritize reducing carbon emissions, optimizing energy use, and enhancing overall energy efficiency. One of the key strategies involves using deduplication and compression technologies, which can significantly reduce the environmental footprint of data centers. By decoupling data-set sizes for compression and deduplication, these centers can optimize each process individually, ensuring that neither technique is compromised and both are fully leveraged for maximum efficiency. In this paper, the HashPress algorithm is introduced, which aims to further enhance the green credentials of data centers. This algorithm proposes innovative measures to optimize data handling processes, making them environment friendly. The empirical validation is conducted to support its efficacy, by improving storage efficiency and reducing energy consumption. The discussion highlights its potential in supporting the broader goal of establishing a green technology infrastructure. Through these advancements, green data centers not only decrease their environmental impact but also pave the way for a resource-efficient future in the technology industry. This paper also delves into the explosive growth of the green data center, importance of decarbonization and sustainability in reducing the environmental footprint and the current strategies and solutions being deployed by leading companies to address the challenge. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Design and Implementation of a High-Speed Level Shifter at 45nm, 90nm, and 180nm Technology Nodes using Cadence
In this work, a CMOS inverter-based level shifter in Differential Cascode Voltage Switch Logic (DCVSL) is constructed and its operation is investigated. The width and length variations of transistors at three technological nodes 45, 90, and 180 nm are compared based on the circuit behaviour. A critical analysis of the impact of supply voltage scaling on NMOS and PMOS transistors is also presented. There is also a comparison of the effects of transistor widths and lengths, as well as supply voltage variations of 1.8V, 1.5V, and 1.0V, on circuit performance. Additionally, this study compares wavelength variation and its impact on device attributes. Dynamic power, static power, energy, and delay are evaluated at the transistor and direct current levels. The Cadence Virtuoso simulation tool illustrates the variations in inverter performance under various scaling conditions. The results demonstrate that careful optimization of transistor dimensions and supply voltage can significantly enhance the performance and power efficiency of the level shifter, providing valuable insights for low-power, high-speed VLSI applications. 2025 IEEE. -
FISCAL DECENTRALIZATION IN INDIA AND CHINA: Experiences in service delivery
[No abstract available] -
Identifying a Range of Important Issues to Improve Crop Production
Crop yield production value update has a beneficial practical impact on directing agricultural production and informing farmers of changes in crop market prices. The main objective of the suggested method is to put the crop selection technique into practise so that it may be used to address a variety of issues facing farmers and the agricultural industry. As a result, the yield rate of crop production is maximised, which benefits our Indian economy. land conditions of several kinds. So, using a ranking system, the quality of the crops are determined. This procedure also alerts farmers to the rate of crops of low and high quality. Due to the use of multiple classifiers, using an ensemble of classifiers paves the way for better prediction decisions. The decision-making process for selecting the output of the classifiers also incorporates a rating system. The price of a crop that will produce more is predicted using this method. 2023 IEEE. -
A Novel Assessment of Healthcare Waste Disposal Methods: Intuitionistic Hesitant Fuzzy MULTIMOORA Decision Making Approach
Waste produced from medical facilities systems incorporates a blend of dangerous waste which can posture dangers to humans and ecological receptors. Lacking administration of healthcare waste can prompt hazard to medicinal service specialists, patients, public health, communities and the wider environment. Hence, proper management of healthcare waste is imperative to reduce the associated health and environment risk. In this paper, we extend the MULTIMOORA decision making method with intuitionistic hesitant fuzzy set to evaluate the healthcare waste treatment methods. Intuitionistic hesitant fuzzy set is a generalized form of a hesitant fuzzy set. Intuitionistic hesitant fuzzy set considers the uncertainty of data in a single framework and take more information into account. The MULTIMOORA method consists of three parts namely the ratio system, reference point approach and the full multiplicative form. In the optimal ranking methods, the IHF-MULTIMOORA method is uncomplicated it is able to be used practically with high dimension intuitionistic hesitant fuzzy sets. For pathological, pharmaceutical, sharp, solid and chemical wastes, the preferred waste disposal methods are deep burial, incineration, autoclave, deep burial, and chemical disinfection, respectively. 2013 IEEE. -
The hesitant Pythagorean fuzzy ELECTRE III: An adaptable recycling method for plastic materials
In this research article, introduce a novel decision making method called HPF-ELECTRE method by extending the ELECTRE III (ELimination and Choice Expressing REality) method with HPF (Hesitant Pythagorean Fuzzy) set. The efficiency of the new method is testing in the plastic recycling problem. One of the most hazardous domestic materials is plastic. The low biodegradability nature of plastic is a serious threat to the environment and to human life. Plastic is a synthetic chemical that do not belong to the natural world. Owing to the non-biodegradability, the only way to deal with this modern-world problem is recycling. Finding a suitable recycling method for disposing and recycling plastic materials is a major research issue. Propose the HPF-ELECTRE III method to find out the adaptable recycling method for plastics materials. The outranking in HPF-ELECTRE III method expand on concordance and discordance acceptability value values. Established method is an effective tool for decision making problems. 2020 Elsevier Ltd -
Addressing Security Challenges in AI-Driven Cyber Security: Enhancing Resilience While Fostering Sustainable Practices with Green Computing
The modern cyber security environment changed through increased sophistication and complexity of cyber threats that requires organizations to use artificial intelligence (AI) technologies for strengthened security frameworks. It also limits the adverse impact within the populace by a decrease in carbon dioxide emissions, energy conservation, decrease wastage of electronic gadgets and assistance to sustainability with renewable resources. Among the practices of creating the green environment are the measures of virtualization, improving the quality of the hardware to increase the energy efficiency and using the cooling technologies efficiently. The Sustainable Cyber security practices are examining the measures and innovation for minimizing energy usage by integrated cyber security tools/infrastructure, green data center/network, and practicing green software engineering. In this domain, Sustainability Cyber security employs energy conservative cryptographic algorithms and software architecture, low energy cryptographic physical devices, power-conscious security protocols; efficient virtualization by integrating these approaches, they can advance sustainability into higher security statures. Besides, the enforcement of those security practices will help to address green data center objectives like server virtualization, and other efficiency data storage products. Cyber Security algorithms will act to lessen the time construct of cryptographic operations to less computational power, and hence less energy consumption. It also looks at the direction that sustainability is likely to take in the future, which may include such policies and structures as are likely to promote green computing in cyber security. Also through this case study, we have been able to incorporate green computing into its cyber security programs for a better and environmentally friendly future. The advanced strategic planning through automation enables organizations to develop stronger defense capabilities as they adjust to security threats which keep evolving in the present-day landscape. 2026 Scrivener Publishing LLC. -
Resource allocation in cloud auction-based market by hybrid optimization algorithm
Effective resource allocation is essential in the rapidly changing cloud computing landscape to maximize provider revenue and user satisfaction. Through competitive bidding procedures, the auction-based market model has become a potent tool for allocating cloud resources among users. In this paper, a new method for cloud computing environments is presented: Double Auction-based Resource Allocation (DARA). The auction model and optimal resource allocation are the two main parts of the DARA methodology. The Double Auction mechanism is used as the auction model in the suggested DARA framework. In this model, resource prices and allocations are decided through a competitive auction process that involves both buyers and sellers.The highest price that buyers are willing to pay for resources is expressed in bids, and the lowest price that sellers are willing to accept is expressed in asks. There are many intricate tasks involved in this two-way auction process, including matching bids and asks, determining market prices, and handling transactions. Finding the equilibrium price requires the method to solve complex optimization problems in order to balance supply and demand. In order to overcome these obstacles, the study suggests the Hippopotamus Updated Pufferfish Optimization (HUPO) algorithm for the best possible resource distribution. The HUPO algorithm is made to handle limitations like truthfulness, resource density, execution time, and operating expenses. In order to ensure that users pay fair prices and service providers make the most money, it is crucial to implement effective resource allocation strategies that balance the cost of resources with their availability. According to the mean statistical metric, the resource density for the HUPO model is 17.862, which is greater than the values of all other traditional approaches, including BES at 14.960, AOA at 12.546, ACO at 14.274, COA at 13.693, SMO at 13.452, HOA at 13.686, and POA at 13.907. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Smart Online Oxygen Supply Management though Internet of Things (IoT)
We are surrounded by oxygen in the air we We cannot even exist without the ability to breathe. The need for oxygen has increased during the COVID19 pandemic, and although there is enough oxygen in our country, the main issue is getting it to hospitals or those in need on time. This is simply due to a significant communication gap between suppliers and hospitals, so we plan to implement an idea that will close this gap using real-time tracking as we can track the movement of oxygen tankers by gathering the requirements. We are using an ESP32 Wi-Fi module, a MEMS pressure sensor that enables the combination of precise sensors, potential processing, and wireless communication, such as Wi-Fi, Bluetooth, IFTTT, and MQTT protocols, to implement it successfully. The pressure sensor publishes the value of oxygen remaining from the location to the MQTT broker. 2022 IEEE. -
What We Think Others Think and Do About Climate Change: A Multicountry Test of Pluralistic Ignorance and Public-Consensus Messaging
Most people believe in human-caused climate change, yet this public consensus can be collectively underestimated (pluralistic ignorance). Across two studies using primary data (n = 3,653 adult participants; 11 countries) and secondary data (ns = 60,230 and 22,496 adult participants; 55 countries), we tested (a) the generalizability of pluralistic ignorance about climate-change beliefs, (b) the effects of a public-consensus intervention on climate action, and (c) the possibility that cultural tightness-looseness might serve as a country-level predictor of pluralistic ignorance. In Study 1, people across 11 countries underestimated the prevalence of proclimate views by at least 7.5% in Indonesia (90% credible interval, or CrI = [5.0, 10.1]), and up to 20.8% in Brazil (90% CrI = [18.2, 23.4]. Providing information about the actual public consensus on climate change was largely ineffective, except for a slight increase in willingness to express ones proclimate opinion, ? = 0.05 (90% CrI = [?0.02, 0.11]). In Study 2, pluralistic ignorance about willingness to contribute financially to fight climate change was slightly more pronounced in looser than tighter cultures, highlighting the particular need for pluralistic-ignorance research in these countries. The Author(s) 2025. -
A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer's Disease
Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer's disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer's disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber's law which determines the luminance factor of the image. In the region extraction process, Chan-Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively. 2022 World Scientific Publishing Europe Ltd. -
IoT innovation in COVID-19 crisis
The COVID-19 pandemic is a current global threat that surpasses provincial and radical boundaries. Due to the onset of the pandemic disease, the whole world turned entirely in a couple of weeks. Its consequences have come across the personal and professional life of human beings. The current situation focuses on precautions such as wearing a mask, maintaining social distancing, and sanitizing hands regularly. An innovative platform, and smart and effective IoT technology may be applied to follow these steps. This platform fulfills all critical challenges at the time of lockdown situations. IoT technology is more helpful in capturing real-time patient data and other essential information. IoT allows the tracing of infected people and suspicious cases and helps diagnose and treat patients remotely. It also paves the way to deliver essential medical devices and medicines to quarantined places. In the present ongoing crisis, IoT technology is inevitable in monitoring patients infected with COVID-19 through sensors and intertwined networks. The consultations are given to the patients digitally through video conferencing without meeting the medical expert in person. After the diagnosis is made digitally, IoT devices are used to track health data. Smart thermometers are used instead of traditional ones to collect valuable health data and share it with experts. The IoT robots are now a proven technology used for cleaning hospitals, disinfecting medical devices, and delivering medicines, thus giving more time to healthcare workers to treat patients. 2023 Bentham Science Publishers. All rights reserved. -
Impact of Risk Perception on Use and Satisfaction with Online Pharmacies and Proposed Use of IoT to Minimize Risks
This study investigates consumer risk perceptions regarding online pharmacies and their impact on usage frequency and satisfaction. The growing popularity of online pharmacies offers benefits such as accessibility, cost savings, and privacy. However, significant risks, including the potential for counterfeit drugs and insufficient medical oversight, raise concerns. This study has measured consumer perceptions of risk, satisfaction, and usage frequency through a survey conducted in Northeast India, excluding Sikkim (online) and Sikkim (offline). The findings reveal that the fear of receiving counterfeit medications is a significant risk factor, negatively influencing both the frequency of use and consumer satisfaction. Despite this, the impact is relatively weak, suggesting that while risk perception is a concern, it does not significantly deter online pharmacy usage. The study suggests that integrating advanced technologies such as IoT, RFID, and blockchain can mitigate these risks by ensuring the authenticity of medications in the supply chain. 2024 IEEE.