Browse Items (16481 total)
Sort by:
-
An advanced variable temperature refrigerator for preservation and management of food items
All food items will have shelf life period. The main aim of food preservation is to maximize the shelf life period and preservation of nutrients for a long period. One of the preservation methods is refrigeration. Each food item will have its own optimum storage temperature to maximize the shelf life period. Normal refrigerators have fixed temperature. The work proposes a refrigerator with six compartments which is equipped with temperature sensors to maintain the fixed temperature for that compartment and with weighing sensors to monitor the depleting food items with the help of a controller. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
An advanced machine learning framework for cybersecurity
The world is turning out to be progressively digitalized raising security concerns and the urgent requirement for strong and propelled security innovations and procedures to battle the expanding complex nature of digital assaults. This paper examines how AI is being utilized in digital security in both resistance and offense exercises, remembering exchanges for digital attacks focused on AI models. Digital security is the assortment of approaches, systems, advancements, and procedures that work together to ensure the confidentiality, trustworthiness, and accessibility of processing assets, systems, programming projects, and information from attacks. Machine learning-based examination for cybersecurity is the following rising pattern in digital security, planned for mining security information to reveal progressed focused on digital threats and limiting the operational overheads of keeping up static relationship rules. In this paper, we are mainly focusing on the detection and diagnosis of various cyber threats based on machine learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gamma Distribution and Support Vector Machine
As indicated by a measurable report distributed by the registry of central brain tumor at United States (CBTRUS), roughly 59,550 individuals were recently diagnosed to have essential benign and essential harmful brain tumors in 2017. Besides, in excess of 91,000 individuals, in the United States alone, were living with an essential harmful cerebrum tumor and 367,000 were living with an essential kind brain tumor. The task of detecting the position of the tumor in the body of the patient is the starting point for a medical treatment in the diagnosis process. The main aim of this study is to design a computer system, which is able to detect the tumor presence in the digital images of the brain in the patient and to accurately define its borderline. In this proposed model, gamma distribution method is used for training, testing, and for the feature extraction process, while SVM, support vector machine is used for the classification process. Most of the algorithms find it difficult to segment the tumors that were present in the edges. But with the help of gamma distribution along with the use of edge analysis, it is easier to identify those tumor areas that are present in the edges, thus making it easier for the preprocessing process. Gamma distribution also provides us with high accuracy, and it can also point the exact location of the tumor than compared to other algorithms. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Adoptable Multi-Criteria Decision-Making Analysis to Select a Best Hair Mask Product-Extended Weighted Aggregated Sum Product Assessment Method
Hair masks (HMs) act as one of the solutions for most of the hair problems like dandruff, frizziness, breakage, premature- greying and so on. Due to its various benefits, HM products are acquiring more popularity among the individuals. As there are different varieties of HM products available in the market, the confusion arises in choosing a HM which suits the individuals hair profile and causes less side effects. Here, we have employed multi-criteria decision-making (MCDM) combined with fuzzy set theory to obtain better results. We used the extended Weighted Aggregated Sum Product Assessment (WASPAS) method based on trapezoidal interval type-2 fuzzy set (TIT2FS) in this research paper to handle vagueness and complexity in real-world problems. For determining the objective weights of the criteria, we used the entropy method of weight finding. An example of selecting a hair mask product (HMP) among four alternatives based on five criteria is provided to illustrate the applicability of the proposed method. In comparison to other MCDM methods, the approach yielded more practical results. By doing a sensitive study, the methods stability is also assessed. 2021, The Author(s). -
An adept approach to ascertain and elude probable social bots attacks on twitter and twitch employing machine learning approach
There has been a tremendous increase in the popularity of social media such as blogs, Instagram, twitter, online websites etc. The increasing utilization of these platforms have enabled the users to share information on a regular basis and also publicize social events. Nevertheless, most of the multimedia events are filled with social bots which raise concerns on the authenticity of the information shared in these events. With the increasing advancements of social bots, the complexity of detecting and fact-checking is also increasing. This is mainly due to the similarity between authorized users and social bots. Several researchers have introduced different models for detecting social bots and fact checking. However, these models suffer from various challenges. In most of the cases, these bots become indistinguishable from existing users and it is challenging to extract relevant attributes of the bots. In addition, it is also challenging to collect large scale data and label them for training the bot detection models. The performance of existing traditional classifiers used for bot detection processes is not satisfactory. This paper presents: A machine learning based adaptive fuzzy neuro model integrated with a hist gradient boosting (HGB) classifier for identifying the persisting pattern of social bots for fake news detection. And Harris Hawk optimization with Bi-LSTM for social bot prediction. Results validate the efficacy of the HGB classifier which achieves a phenomenal accuracy of 95.64 % for twitter bot and 98.98 % for twitch bot dataset. 2023 -
An Adaptive Scriptless Behavior-Driven Development Automation Framework with Self-Healing Intelligence for Evolving Software Applications
Background: The high rate of user interface (UI) and source code changes in contemporary software development resulted in automated testing failures that augmented maintenance expenses and decreased the usefulness of automated testing. The current tools need regular updating by manual means, which is ineffective and expensive. Purpose: To present the Adaptive Scriptless Behavior-Driven Development (BDD) Automation Framework with Self-Healing Intelligence, which is an artificial intelligence (AI) and machine learning (ML)-driven framework of automatic test failure detection and resolution based on UI drift, broken locators, or timing. Approaches: The framework uses dynamic locator approaches, adaptive test generation, and reinforcement learning to allow updating test scripts in response to application changes. Such a self-healing feature will minimize human intervention and reduce maintenance expenses. An experimental case study was conducted in order to assess the performance of the framework in a practical context. Findings: The framework demonstrated significant advances in automated testing, such as a 30% drop in maintenance speed, reduced number of resources to update tests, a 25 % reduction in total cost of testing since less manual effort is needed, and a 40 % rise in stability of the test suites, which can execute its tests more reliably and with greater accuracy despite the presence of changes to the application. Conclusions: The Adaptive Scriptless BDD Automation Framework with Self-Healing Intelligence goes a long way to improving the flexibility, scalability, and efficiency of automated testing. It enhances the speed of testing, saves costs, and adds confidence in the quality of software, and is therefore valuable for ensuring high-quality standards in dynamic software landscapes. 2026, Innovative Information Science and Technology Research Group. All rights reserved. -
An adaptive inertia weight teachinglearning-based optimization for optimal energy balance in microgrid considering islanded conditions
The energy balance in islanded microgrids is a complex task due to various operational constraints. This paper proposes a new approach to multi-objective optimization for achieving energy balance in aMicrogrid(MG) in both islanded and normal modes. Optimal load control (OLC)is achallenge, due to a lack of capacity to generate the global optimum after each run. The latest variant of Teaching Learning Based Optimization (TLBO), known as Adaptive-TLBO, includes both modifications during exploitation and exploration stages (ATLBO). The results achievedwith the proposed method are exceptional on a modified IEEE 33-bus system. In addition to the improvement of the voltage profile and the decrease of the distribution losses, the energy balance improves with the method. The proposed ATLBO algorithm overrides any proposed other algorithm, as shown by comparison with PSO, base TLBO, Backtrackingsearch algorithm (BSA) and cuckoo search algorithms, etc. (CSA). The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. -
AN ADAPTIVE HYBRID SCHEDULING APPROACH FOR SUSTAINABLE AND RELIABLE CLOUD SERVICES
The modern cloud computing systems have to plan the heterogeneous workloads and balance performance effectiveness, service availability, and sustainability. In this study, an adaptive hybrid scheduling framework is developed Adaptive Ant-guided Min-Max (AAMM) combining ant-guided optimization with dynamic Min-Min and Max-Min in deciding how to allocate cloud tasks as a multi-objective. The scheduler jointly evaluates task completion time, the likelihood of Service Level Agreement violations, energy consumption, and monetary cost within a unified scoring framework, enabling informed trade-offs among competing objectives. AAMM is assessed based on a real disaggregated Deep Learning Recommendation Model workload of 1,544 heterogeneous tasks, running on heterogeneous virtual machines. Comparative experiments are done with Min-Min, Max-Min and ACO-guided Min-Min scheduling strategies. According to experimental findings, the suggested approach has been very effective in reducing energy per task, cost per task, SLA violations are significantly lowered, and flow time stability is enhanced. Though moderate growth in the makespan is witnessed, the accompanying trade-off has created equal distribution of resources and service reliability. 2026 Academy and Industry Research Collaboration Center (AIRCC). All rights reserved. -
An Adaptive Cluster based Vehicular Routing Protocol for Secure Communication
In todays scenario, Vehicular Ad-hoc Network (VANET) is one of the modern fields in vehicle communication; it includes a large number of nodes that can be changed arbitrarily with the ability to link or exit the system anytime. Moreover, it has various complexities because of the attacks model in the transmission and communication channel. Besides, most of the attacks are known as black hole attack and wormhole attack. The presence of these attacks causes large damage in the data broadcasting region that ends in data drops or collapses. To defeat these problems, a novel Clustered Vehicle Location protocol for Hybrid Krill Herd and Bat Optimization (CVL-HKH-BO) technique is proposed. Thus, the proposed mechanism of hybrid krill herd and bat optimization is to detect and prevent attacks based on the fitness function. Moreover, secure communication can be enhanced by the proposed technique. Consequently, the solution to energy consumption and packet delay issues are solved using the CVL protocol. The projected strategy is implemented in the Network simulator (Ns-2) platform, and the outcomes show the node energy, overload and delay are minimized by increasing the quantity of packets transmitted in the network. Sequentially, the proposed technique is compared with existing techniques in terms of throughput, packet loss, delay time and data broadcasting ratio. Therefore, the duration of the node can be enhanced and can attain high energy capable data transmission. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
An Accurate Multiple Data Based Stock Prediction and Sentiment Analysis Using Synergic Deep Info Convolutional Neural Network
Sentiment analysis is one of the most widely used methods for forecasting stock market action from consumer feedback. Most of the methods associated with sentiment analysis are limited due to low accuracy and enhanced error rate. This is addressed by proposing a synergic squeeze deep info convolutional neural network-advanced variable capsule equilibrium auto encoder (SSDCNN-AVCEAE) for sentiment analysis and accurate multiple data-based stock prediction. Stock market data from NSE Nifty 50 (Mar 2, 2020May 10, 2021) and real-time twitter sentiment analysis are pre-processed through data cleaning and sentiment analyzer lexicon processes. Merging features using SSDCNN, optimized with random search algorithm, mitigates overfitting. SSDCNN eliminates redundant features. Selected features undergo classification by AVCEAE, a fusion of advanced capsule auto encoder (ACAE) and variable equilibrium optimization algorithm, enhancing prediction accuracy for rising or falling stock market movements while minimizing errors. Variable equilibrium optimization refines ACAE parameters. The proposed framework demonstrates exceptional performance with F1-Score, accuracy, false alarm rate, sensitivity, precision, specificity, and error rate reaching 98%, 99%, 0.1%, 99%, 99%, and 0.2%, respectively. The measurements highlight the model's ability to handle a variety of issues, making it a reliable option for precise stock prediction. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
An Abstractive Text Summarization Using Decoder Attention with Pointer Network
Nowadays, large amounts of unstructured data are currently trending on social media and the Web. Text summarising is the process of extracting pertinent information in a concise manner without altering the content's core meaning. Summarising text by hand requires a lot of time, money, and effort. Although deep learning algorithms are commonly applied in abstractive text summarization, further research is clearly needed to fully understand their conjunction with semantic-based or structure-based approaches. The resume dataset is taken for this research work, which is gathered from Kaggle and the dataset includes 1,735 Resumes. This paper presents a unique framework based on the combination of semantic data transformations and deep learning approaches for improving abstractive text summarization. In an attempt to tackle the problem of unregistered words, a solution called Decoder Attention with Pointer Network (DA-PN) has been introduced. This method incorporates the use of a coverage mechanism to prevent word repetition in the generated text summaries. DA-PN is utilized for protecting the spread of increasing errors in generated text summaries. The performance of the proposed method is estimated using the evaluation indicator Recall Oriented Understudy for Gisting Evaluation (ROUGE) and attains an average of 26.28 which is comparatively higher than existing methods. 2023 IEEE. -
Amyotrophic Lateral Sclerosis: Insights and New Prospects in Disease Pathophysiology, Biomarkers and Therapies
Amyotrophic Lateral Sclerosis (ALS) is a severe neurodegenerative disorder marked by the gradual loss of motor neurons, leading to significant disability and eventual death. Despite ongoing research, there are still limited treatment options, underscoring the need for a deeper understanding of the diseases complex mechanisms and the identification of new therapeutic targets. This review provides a thorough examination of ALS, covering its epidemiology, pathology, and clinical features. It investigates the key molecular mechanisms, such as protein aggregation, neuroinflammation, oxidative stress, and excitotoxicity that contribute to motor neuron degeneration. The role of biomarkers is highlighted for their importance in early diagnosis and disease monitoring. Additionally, the review explores emerging therapeutic approaches, including inhibitors of protein aggregation, neuroinflammation modulators, antioxidant therapies, gene therapy, and stem cell-based treatments. The advantages and challenges of these strategies are discussed, with an emphasis on the potential for precision medicine to tailor treatments to individual patient needs. Overall, this review aims to provide a comprehensive overview of the current state of ALS research and suggest future directions for developing effective therapies. 2024 by the authors. -
Amorphous versus crystalline Al2O3nanoparticles: A comparative study in photocatalytic dye degradation
This study focuses on the synthesis of aluminum oxide (Al2O3) nanoparticles and compares their amorphous and crystalline phases, emphasizing their suitability for photocatalytic dye degradation. The as-prepared Al2O3, synthesized using the sol-gel technique, is found to have an amorphous nature, which is later annealed at 1200C to obtain its ? phase of crystalline nature. Despite the widespread applications of aluminum oxide in various fields, the differences between its amorphous and crystalline phases are not well understood. This work bridges this gap by evaluating the amorphous and crystalline phases of Al2O3, particularly for dye degradation. As technologies advance to enhance aluminum-containing photocatalytic materials by doping, composites, and hybrids, understanding the impact of material phase on photocatalytic capabilities becomes crucial. The research comprehensively assesses structural, functional, morphological, optical, and dye degradation characteristics. Remarkably, amorphous Al2O3 demonstrates superior dye degradation efficacy compared with its crystalline counterpart, achieving an enhanced degradation efficiency of 87.2% for rhodamine B, a commonly used azo dye in the printing and textile industries. 2024 Emerald Publishing Limited: All rights reserved. -
Amorphous Ru-Pi nanoclusters decorated on PEDOT modified carbon fibre paper as a highly efficient electrocatalyst for oxygen evolution reaction
Amorphous Ru-Pi nanoclusters deposited on PEDOT modified carbon fibre paper electrode have been investigated as a potential oxygen evolution electrocatalyst. CFP/PEDOT/Ru-Pi electrode was prepared by electrodeposition of Ru-Pi nanoclusters on PEDOT decorated CFP using cyclic voltammetry (CV). Field emission scanning electron microscopy with energy-dispersive X-ray spectroscopy (FESEM-EDS), attenuated total reflection with Fourier-transform infrared spectroscopy (ATR-FTIR) and X-ray diffraction (XRD) were used for physicochemical characterization. Linear sweep voltammetric (LSV) studies corroborated that CFP/PEDOT/Ru-Pi has exhibited higher oxidation peak current when compared to other modified electrodes. CFP/PEDOT/Ru-Pi electrode has displayed better catalytic activity towards oxygen evolution reaction at low onset and over potential. The modified electrode has also offered better stability towards the oxidation reaction in phosphate buffer solution (PBS) and the working stability of these electrodes were determined using LSV and CV. 2021 Elsevier B.V. -
Amorphous Ru-Pi nanoclusters coated on polypyrrole modified carbon fiber paper for non-enzymatic electrochemical determination of cholesterol
A facile electrochemical sensor based on Ruthenium-Phosphate (Ru-Pi) was developed by electrodeposition of Ru-Pi on Polypyrrole (PPy) modified carbon fiber paper (CFP) electrode. Phosphate buffer solutions of neutral pH containing RuCl3 was used for voltammetric deposition of Ru-Pi on PPy/CFP electrode. The modified electrodes were characterized by High resolution transmission electron microscopy (HRTEM), High-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), Field emission scanning electron microscopy (FESEM) with energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and electroanalytical techniques. A significant amount of phosphate (Pi) and Ru centers in higher oxidation states were present on Ru-Pi film deposited on PPy/CFP substrate. Pi plays a significant role during catalyst deposition and in its activity toward determination of Cholesterol. DPV studies demonstrated that under optimum conditions, the developed sensor has displayed a wide linear dynamic range between 0.16 nM and 20.0 nM with a superior detection limit of 0.54 10-10 M. The proposed method was effectively applied in the nonenzymatic determination of cholesterol at an ultralow level in human blood serum samples. The method displayed high selectivity toward cholesterol in the presence of other interfering substances. 2019 The Electrochemical Society. -
Amine-functionalized MIL-101(Fe)-NH2@ZIF-8 composite for efficient adsorption of Pb2+ ions
Heavy metal contamination of water resources poses a serious environmental and public health threat, necessitating the development of efficient and selective adsorbent materials. In this study, a hierarchical MIL-101(Fe)-NH2@ZIF-8 composite was successfully fabricated via an interfacial growth strategy, integrating amine-functionalized MIL-101(Fe)-NH2 and ZIF-8 to achieve a synergistic micro-mesoporous architecture with accessible functional sites. The composite was thoroughly characterized by FTIR, PXRD, TGA, BET, and SEM-EDX analyses, with elemental mapping confirming the structural integration and resulting in enhanced porosity, thermal stability, and functional group availability. The material exhibited a remarkable Pb2+ adsorption efficiency of 94.9 % and a maximum adsorption capacity of 297 mg/g, significantly superior to the adsorption of other metal ions (Cd2+, Cu2+, Ni2+, and Cr2+). Atomic absorption spectroscopy (AAS) validated the exceptional selectivity of MIL-101(Fe)-NH2@ZIF-8 for Pb2+ ions. The enhanced performance is attributed to the synergistic effect of accessible amine (?NH2) functionalities, Fe?O coordination sites, and hierarchical porosity enabling strong metal binding and rapid diffusion. These findings highlight the exceptional potential of MIL-101(Fe)-NH2@ZIF-8 as an advanced adsorbent for Pb2+ removal from water, offering a practical pathway to address critical environmental challenges and promote sustainable human health and ecological protection. 2025 Elsevier B.V. -
Amine functionalized carbon quantum dots from paper precursors for selective binding and fluorescent labelling applications
We report a novel synthesis route for preparing carbon quantum dots (CQDs) of customized surface functionality from readily available precursors. The synthetic strategy is based on the chemical modification of paper precursors prior to preparing CQDs from them. The pre-synthesis modification of paper precursors with (3-Aminopropyl) triethoxy silane (APTES) enabled us to synthesize CQDs with amine functional groups on the surface. The silane coupling via condensation between the ethoxy group of APTES and the cellulose hydroxyl group on the paper resulted in the tethering of amine groups on the paper substrates, which are retained as surface-bound species during the synthesis of CQDs from the modified paper. Amine functionalization on the surface of CQDs helped us use them in applications such as DNA binding. We analyzed the interaction of CQDs with calf thymus DNA (CT-DNA), and the results imply their propensity as an efficient biological probe. The synthetic strategy presented here can also be extended to other functional groups. 2022 Elsevier Inc. -
Amide-enriched pod-based carbon nanospheres for enhancing supercapacitor performance: A value-added approach for solid state supercapacitors
The present work involves the fabrication of symmetric solid-state supercapacitors (SSSCs) using amide-functionalized carbon nanospheres (CNS) derived from Magnolia champaca pods, a bio-waste material. The pods were carbonized at temperatures ranging from 400 C to 1000 C, with CNS at 800 C (MC800) showing best electrochemical performance. The synthesized materials, i.e., MC400, MC600, MC800, MC1000, were characterized by techniques such as FESEM, HR-TEM, FTIR, XRD, Raman spectroscopy, and BET. Amide functionalization, achieved through the use of 2,3,4-trifluoroaniline (TFA), enhanced charge storage capacity by improving ion transport and surface interaction, resulting in the functionalized CNS labeled as MC800/COOH-TFA. The electrochemical investigation of the CNS was studied via techniques such as cyclic voltammetry (CV), galvanostatic charge-discharge (GCD) and electrochemical impedance spectroscopy (EIS). The functionalization led to two-fold increase in specific capacitance from 243 Fg?1 to 410 Fg?1 at a current density of 0.25Ag?1 in 3 M KOH. The SSSCs was fabricated using MC800/COOH-TFA with a PVA-KOH gel electrolyte demonstrating a good areal capacitance of 40 mFcm?2 at 1.0 mAcm?2. Moreover, the device exhibited excellent energy density of 5.54 ?Whcm?2 and cycle stability, retaining 71.75 % of its capacitance after 10,000 charge-discharge cycles. The response time of the functionalized sample has been reduced to 2.31 s (MC800/COOH-TFA) from 4.73 s (MC800). These results highlight the potential of amide functionalized CNS in producing efficient, sustainable energy storage devices with improved performance. 2025 Elsevier Ltd -
Ambient monitoring in smart home for independent living
Ambient monitoring is a much discussed area in the domain of smart home research. Ambient monitoring system supports and encourages the elders to live independently. In this paper, we deliberate upon the framework of an ambient monitoring system for elders. The necessity of the smart home system for elders, the role of activity recognition in a smart home system and influence of the segmentation method in activity recognition are discussed. In this work, a new segmentation method called area-based segmentation using optimal change point detection is proposed. This segmentation method is implemented and results are analysed by using real sensor data which is collected from smart home test bed. Set of features are extracted from the segmented data, and the activities are classified using Naive Bayes, kNN and SVM classifiers. This research work gives an insight to the researchers into the application of activity recognition in smart homes. Springer Nature Singapore Pte Ltd. 2019. -
Amberlite-15 promoted an unprecedented aza Michael rearrangement for one pot synthesis of dihydroquinazolinone compounds
A new one pot multicomponent annulation strategy for the synthesis of various dihydroquinazolinone compounds has been developed using Amberlite-15 as a catalyst, giving good to moderate yields. In this reaction the substrate scope for amines and aldehydes was also investigated. The reaction has been checked on a large scale and the possible reaction mechanism has also been proposed. The Royal Society of Chemistry 2018.