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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). -
The COVID-19 vaccine preference for youngsters using promethee-ii in the ifss environment
Extensive decision-making during the vaccine preparation period is unpredictable. An account of the severity of the disease, the younger people with COVID-19 comorbidities and other chronic diseases are also at a higher risk of the COVID-19 pandemic. In this research article, the preference ranking structure for the COVID-19 vaccine is recommended for young people who have been exposed to the effects of certain chronic diseases. Multiple Criteria Decision-Making (MCDM) approach effectively handles this vague information. Furthermore, with the support of the Intuitionistic Fuzzy Soft Set (IFSS), the entries under the new extension of the Preference Ranking Organization Method for Enrichment Evaluation-II (PROMETHEE-II) is suggested for Preference Ranking Structure. The concept of intuitionistic fuzzy soft sets is parametric in nature. IFSS suggests how to exploit an intuitionistic ambiguous input from a decision-maker to make up for any shortcomings in the information provided by the decider. The weight of the inputs is calculated under the Intuitionistic Fuzzy Weighted Average (IFWA) operator, the Simply Weighted Intuitionistic Fuzzy Average (SWIFA) operator, and the Simply Intuitionistic Fuzzy Average (SIFA) operator. An Extended PROMETHEE-based ranking, outranking approach is used, and the resultant are recommended under the lexicographic order. Its sustainability and feasibility are explored for three distinct priority structures and the possibilities of the approach. To demonstrate the all-encompassing intuitionistic fuzzy PROMETHEE approach, a practical application regarding COVID-19 severity in patients is given, and then it is compared to other existing approaches to further explain its feasibility, and the sensitivity of the preference structure is examined according to the criteria. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Application of normal wiggly dual hesitant fuzzy sets to site selection for hydrogen underground storage
The hesitant fuzzy set is a mathematical tool to express multiple values in decision making. If they could not give a resolution, it is important to give priority and importance to a number of different values. Here, we propose normal wiggly dual hesitant fuzzy set (NWDHFS), as an extension of normal wiggly hesitant fuzzy set. We define a new score function of normal wiggly dual hesitant fuzzy information. The NWDHFS can express deep ideas of membership and non-membership information. In this work, we use hesitant fuzzy set to expose the deepest ideas hidden in the thought-level of the decision makers. We show that the NWDHFS can handle the hesitant fuzzy information. It expresses the deeper ideas of hesitant fuzzy set. An illustration is provided to demonstrate the practicality and effectiveness to the application of site selection of the underground storage of hydrogen. We are compelled to look for alternating fuels to suits changing weather conditions and increasing number of vehicles. This alternative fuel is necessary to control global warming and to be economically viable. Based on this, hydrogen gas is selected as a good alternative fuel. The most important statement is the saving of the selected hydrogen gas. Thus, when saving hydrogen fuel, a safe storage space must be selected. Here, we use the MCDM ideas by use of proposed NWDHFV method is to select the appropriate hydrogen underground storage location. 2019 Hydrogen Energy Publications LLC -
Use of DEMATEL and COPRAS method to select best alternative fuel for control of impact of greenhouse gas emissions
Generation of energy is a vital process for sustenance of human life. Quality of human life is undoubtedly linked to the efficient generation and use of energy. The choice of alternative fuels is of the utmost significance due to the decline of fossil fuel reserves and their effect on global warming. One of the most important areas of research all over the world is the generation and distribution of sustainable energy. There are, in fact, many sustainable fuel resources. In this study, we describe the problem of selecting alternate fuel using novel types of hesitant multi criteria decision-making equation methods. The considered fuel systems are Electricity, natural gas, biodiesel, ethanol, and propane. In the selection of alternative fuels, quantity, quality of performance, cost, and efficiency among others, have to be taken into account. The alternatives selected should, for example, increase the speed of buses, and provide for greater mileage while and not affecting the environment. Here, the DEMATEL (Decision Making Trial and Evaluation Laboratory Model) method is used to determine the weights of the criteria and the COPRAS (Complex Proportional Assessment) method is used to calculate the ranking of the alternatives. The main objective of this research paper is to select the best alternative, based on environmental safety, CO2 emission level, technical cost, and fuel cost. Thus a better alternative is selected with the selected alternatives and criteria. The results of this research are summarized as follows. These are natural gas ( R2 ) > propane ( R5 ) > biodiesel ( R3 ) > electricity ( R1 ) > ethanol (R4 ). The numeric values of these selected alternatives are R2=1>R5=0.5215>R3=0.4904>R1=0.4887>R4=0.3299. 2020 Elsevier Ltd -
A new extension of hesitant fuzzy set: An application to an offshore wind turbine technology selection process
Wind energy is an energy source that is naturally clean, safe and cheap. It comes from a variety of sources. The electric energy generated by a wind turbine manifests as kinetic energy throughout the earth. The energy received from the wind is clean and is permanently available and can be generated forever. Turbine characteristics also have an impact on wind energy production. The turbine properties within a wind farm are important in estimating the load on power generation and wind turbine energy. The amount of energy released is calculated according to the type of the turbine model applied. In many situations, the choices of turbine model can incur various vague and complicated hesitation situations. To manage this situation, a hesitant fuzzy set with the Multi Criteria Decision Making (MCDM) is used. In the present research, the newly proposed Normal Wiggly Hesitant Fuzzy-Criteria Importance Through Intercriteria Correlation (NWHF-CRITIC) and Normal Wiggly Hesitant Fuzzy-Multi Attribute Utility Theory (NWHF-MAUT) methods were employed to rank turbine models based on quality, power level, voltage, and capacity. As part of this process, the NWHF method was utilized to extract and gather deeper information from the decision-makers. 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. -
Designing of a Free-Standing Flexible Symmetric Electrode Material for Capacitive Deionization and Solid-State Supercapacitors
In this work, a highly efficient free-standing flexible electrode material for capacitive deionization and supercapacitors was reported. The reported porous carbon shows a high surface area of 2070.4 m2 g-1 with a pore volume of 0.8208 cm3 g-1. The material exhibited a high specific capacitance of 357 F g-1 at 1 A g-1 in a two-electrode symmetric setup. A solid-state supercapacitor device has been fabricated with a total cell capacitance of 152.5 F g-1 at 1 A g-1 in a solid PVA/H2SO4 gel electrolyte with an energy density of 21.18 W h kg-1 at a 501.63 W kg-1power density. A long-run stability test was carried out up to 15,000 cycles at 5 A g-1 that showed capacitance retention of 99% with ?100% Coulombic efficiency. Furthermore, the electrosorption experiment was conducted by a flow-through test by coating on commercially available cellulose thread that was employed, which shows electrosorption ability up to 16.5 mg g-1 at 1.2 V in a 500 mg L-1 NaCl solution. Complete experiments were conducted with a proper procedure, provided by scientific approaches with analytical data. Thus, the reported electrode material showed bifunctional application for energy storage and environmental remediation. 2023 American Chemical Society. -
A Comparative Benchmark of Deep Learning and Classical Models for BLE-Based Indoor Localization
Bluetooth Low Energy (BLE)-based indoor positioning has gained attention as a cost-effective solution for environments where GPS signals are unreliable. Despite advances in ML and DL techniques, few standardized benchmarks exist for comparing models under uniform conditions. This study evaluates seven models - K-Nearest Neighbor, Random Forest, Deep Neural Network, 1D CNN, Long Short-Term Memory, Bi-LSTM, and Transformer - on a publicly available dataset collected across multiple building floors. A preprocessing pipeline was applied to address missing values, refine RSSI signals, and generate temporal features. Performance was assessed using both accuracy metrics (MAE, RMSE) and efficiency metrics such as processing time, and model size. Results show that KNN, Random Forest, and DNN consistently outperformed complex sequential and attention-based models, achieving RMSE as low as 1.297 m. These findings suggest that simpler architectures align more effectively with BLE RSSI data than deeper models. This study establishes a benchmark that can support future work in developing efficient, lightweight, and generalizable indoor positioning systems. 2025 IEEE. -
Unveiling thePower ofBayesian Optimization: Methods, Insights, andApplications
Bayesian optimization (BO) has emerged as a popular approach for optimizing expensive black-box functions, which are common in modern machine learning, scientific research, and industrial design. This paper provides a comprehensive review of the recent advances in Bayesian optimization techniques, addressing new methodological developments such as multi-fidelity optimization, transfer learning, and neural network surrogates. Additionally, we explore the increasing role of BO in complex, high-dimensional, and multi-objective optimization problems, as well as its application in various fields like hyperparameter tuning, reinforcement learning, and neural architecture search. The goal of this review is to offer both theoretical insights and practical guidelines to researchers and practitioners working in areas where BO is a suitable tool. Finally, we discuss key challenges and propose directions for future research in the rapidly evolving field of Bayesian optimization. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A novel wide slice kronecker forward fractional network for osteoporosis detection using knee X-ray image
Osteoporosis is an asymptomatic and progressive skeletal disorder that maximizes the risk of fractures in people aged 50 to 60. Early and accurate detection is critical, yet challenging, due to the fine structural changes in bone that are often difficult to identify in routine medical images. Knee X-rays are commonly used diagnostic tools, but interpreting them for osteoporosis detection remains complex because of variations in bone geometry and trabecular patterns. To solve these challenges, the novel Wide Slice Kronecker Forward Fractional Network (WKFF-Net) is developed to detect osteoporosis efficiently. Initially, the input image is taken from the database for detection. Here, the denoising process is done using the Non-Local Means (NLM) filter, and the Otsu thresholding method is considered for the segmentation process. Further, a template search method is used for analyzing the femur geometry. Next, features, like spatial, adaptive Local Binary Patterns (aLBP), Convolutional Neural Networks (CNN), and medical-level features, are extracted, and osteoporosis detection is accomplished by the hybrid WKFF-Net model that integrates Deep Kronecker Network (DKN), Wide Slice Residual Network (WISeR), and fractional calculus. The experimental results obtained by the WKFF-Net are 90.868% accuracy, 92.876% True Positive Rate (TPR), 87.766% True Negative Rate (TNR), 89.888% precision, and 91.357% F1-score, for 90% of the training samples. 2026 Elsevier B.V. -
Biological Feature Selection and Classification Techniques for Intrusion Detection on BAT
Privacy is a significant problem in communications networks. As a factor, trustworthy knowledge sharing in computer networks is essential. Intrusion Detection Systems consist of security tools frequently used in communication networks to monitor, detect, and effectively respond to abnormal network activity. We integrate current technologies in this paper to develop an anomaly-based Intrusion Detection System. Machine Learning methods have progressively featured to enhance intelligent Anomaly Detection Systems capable of identifying new attacks. Thus, this evidence demonstrates a novel approach for intrusion detection introduced by training an artificial neural network with an optimized Bat algorithm. An essential task of an Intrusion Detection System is to maintain the highest quality and eliminate irrelevant characteristics from the attack. The recommended BAT algorithm is used to select the 41 best features to address this problem. Machine Learning based SVM classifier is used for identifying the False Detection Rate. The design is being verified using the KDD99 dataset benchmark. Our solution optimizes the standard SVM classifier. We attain optimal measures for abnormal behavior, including 97.2 %, the attack detection rate is 97.40 %, and a false-positive rate of 0.029 %. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
A Comprehensive Review on the Antimicrobial and Photocatalytic Properties of Green Synthesized Silver Nanoparticles
With the advancement of technology, there is a growing demand for new nanoparticles that are viable, eco-friendly, non-toxic, and non-hazardous, as well as having unique chemical and physical properties. Silver nanoparticles are currently promising for antibacterial, antimicrobial, and photocatalytic applications. Because of their toxicity, nanosilver particles are now widely used in various applications, including cosmetics, clothing, sunscreen, medicinal, sensing, antibacterial, antimicrobial, and photocatalytic. The importance of plant extracts in the synthesis of AgNPs is emphasized. The various mechanisms and characterization techniques used in the study of silver nanoparticles will also be covered. This review also discusses the role of green synthesized AgNPs in antimicrobial and photocatalytic applications, which adds to our understanding of improving health, and the environment and preventing contagious diseases. 2022 by the authors. -
AI and IoT in Digital Marketing: Enhancing Automation, Personalization, and Consumer Interaction
An interconnected system of Internet-enabled devices that can collect and transmit statistics via a wireless connection without the assistance of people is known as the Internet of Things (IoTs). Accordingly, the IoT's seductive power is causing significant shifts in the current corporate environment. The digital marketing industry stands to gain the most from this innovation, which is currently causing major shifts in many other sectors. Using a variety of digital marketing strategies, this innovation gathers numerous types of consumer statistics. IoT technological advancements' impact on digital marketing tactics and customer interaction has emerged as a crucial research topic as it begins to pervade many facets of everyday existence. This investigation examines the application of IoT-based machine learning (ML) in digital marketing for the food business. To give ML-based suggestions, consumer data is analyzed, interests are identified, and conduct is predicted using ML approaches. The ensemble technique aggregates the results of multiple ML techniques to produce an individual forecast. The accuracy matrices graphs for the K-nearest neighbor and decision trees produced excellent estimations, with 100% accuracy and 0.0 error, correspondingly. The nae Bayes method achieved 97.2% accuracy with a 0.029 error, successfully identifying the right tags across every category. The guided ensemble of 3 ML methods is demonstrated by effectively enhancing digital marketing tactics in the food distribution industry by reducing duration and expenses. 2025 IEEE. -
Moisture-Sensitive Fe2O3 Nanoparticle-Based Magnetic Soft Actuators
Multifunctional soft robots are emerging as a new-generation intelligent device for challenging environments. To meet the requirements of smart applications and soft robotics, developing a soft actuator capable of multiple functions and mechanical deformation is essential. In this study, we designed a free-standing magnetic soft actuator constructed from iron oxide (Fe2O3) nanoparticles and poly(vinyl alcohol) (PVA), that responds to both moisture and magnetic fields. We used computational modeling (density functional theory and ab initio molecular dynamics) to explain the experimental findings demonstrating the deformation and high-bending angle (?150), which is about twice as large under combined moisture and magnetic field exposure compared to their individual effect. Additionally, a flower-shaped soft robot was designed by using the continuous bending deformation of the actuator in response to moisture changes, performing directional bending in an ambient environment. These findings demonstrate the materials sensitivity to moisture and magnetic fields, opening up new possibilities for designing responsive structures in the smart device industry. 2024 American Chemical Society. -
Nickel Telluride Quantum Dots as a Counter Electrode for an Efficient Dye-Sensitized Solar Cell
Transition-metal dichalcogenides (TMDs) have recently emerged as highly appealing and efficient options for electrodes in dye-sensitized solar cells (DSSCs), effectively substituting the scarce and expensive metal platinum (Pt). In this work, nickel telluride (NiTe2) quantum dots (QDs) were effectively used as a counter electrode for DSSCs by providing a sustainable alternative to the scarce platinum (Pt). The DSSC based on NiTe2 QDs shows a power conversion efficiency (?) of ?8.06%, which is comparatively better than exfoliated NiTe2 (? ? 6.58%). The density functional theory (DFT) was employed to comprehensively understand the underlying mechanisms involved in the charge transfer between the QDs and the electrolyte species. The outcomes demonstrated the benefits of creating diverse structural configurations designed to enhance interfacial transport, ensure an even distribution of active facets, and improve the electrocatalytic performance in the DSSC process.(Figure Presented). 2023 American Chemical Society. -
Automated Leukaemia Prediction and Classification Using Deep Learning Techniques
Leukemia is typically diagnosed based on an abnormal blood count, frequently an elevated White Blood Cell (WBC) count. The diagnosis is established through bone marrow, replaced by neoplastic cells. Acute Lymphoblastic Leukemia (ALL) is a type of leukaemia that affects the blood and bone marrow. Leukaemia primarily affects children and adults around the world. Early leukaemia detection is critical for appropriately treating patients, especially children. This research aims to present a diagnostic method that uses computational intelligence and image processing algorithms to identify blast cells from ALL images. The medical image is prepared initially using the preprocessing and segmentation technique for efficient classification. In this research, the type is accomplished using Bidirectional Associative Memory Neural Networks (BAMNN), where the accuracy is 96.87%, the highest classification rate and outperforms the existing technique. 2023 IEEE. -
An Efficient Quantum Transfer Learning for Cancer Prediction Using Tumour Markers: New Era of Computer in Medical
Ovarian cancer prediction models or algorithms estimate a person's risk of getting the disease based on different variables, such as their medical history, genetics, and biomarkers. Early identification and intervention will enhance patient successive diagnosis outcomes. Tumour markers are chemicals frequently detected in higher concentrations than usual in cancer patient's blood, urine, or tissues. They could be certain chemicals or proteins linked to the presence of tumours or cancer kinds. Tumour markers are employed for diagnosis, prognosis, and treatment response monitoring. Applying information or models from one quantum job to enhance the performance of another requires quantum transfer learning. Transferring knowledge from one domain to another seeks to increase learning effectiveness in novel quantum contexts. The main goal of efficient Quantum Transfer Learning (QTL) is to minimize the resources (computer power, data, or time) necessary to transfer between tasks successfully. In this research work, QTL is used to predict Ovarian Cancer (OC) with the assistance of biomarkers. The Quantum Transfer Learning- Ovarian Cancer (QTL-OC) achieves 93.78% accuracy and outperforms the existing techniques. 2023 IEEE. -
CLASS onboard Chandrayaan-2: Five years around the Moon
Chandrayaan-2 Large Area soft X-ray Spectrometer (CLASS) is a remote X-ray Fluorescence experiment to map the lunar surface elemental abundances. With its large effective area and low energy threshold, CLASS generates the highest spatial resolution maps of all major rock-forming elements on the Moon, such as Mg, Al, Si, Ca, Ti, and Fe. Five years of operation in lunar orbit has resulted in global coverage. With several lunar missions planned for this decade for in situ exploration and sample returns, the 155 km geochemical maps from CLASS will serve as an important dataset. This article highlights the scientific results of CLASS in the last five years and discusses its potential applications. Indian Academy of Sciences 2025. -
A secure and light weight privacy preserving data aggregation algorithm for wireless sensor networks
WSN is a collection of sensors, which senses critical information related to military, opponent tracking, patient health details etc. These sensed critical and private data will be collected and aggregated by aggregators and forward it to the base station. Due to the involvement of sensitive data, there is a demand for secure transmission and privacy preserving data aggregation. In this paper, we propose a light weight, secure, multi party, privacy preserving data aggregation scheme, in which one or more sensors share their private data with aggregator securely without revealing the original content. The aggregators also perform the aggregation operation without knowing the original content. 2020 Alpha Publishers. -
The Influence of Alloying Constituent Fe on Mechanical Properties of NiTi Based Shape Memory Alloys
The influences of Fe-addition on phase transformation behavior, mechanical properties and microstructure of Ti50Ni50-xFex alloys were investigated by means of optical microscopy, scanning electron microscopy (SEM) and X-ray diffraction (XRD). Results indicate that, as a substitute for Ni, Fe added to TiNi alloys can dramatically decrease the martensite transformation temperature and R phase transformation and martensite transformation are accordingly separated. The results show that TiNiFe alloys exhibit two-step martensitic transformation. The start temperature of martensitic transformation increases sharply from 212 K to 267 K when 2% Fe is added in, and then decreases gradually if Fe content further increases. The hardness of TiNiFe ternary alloys before heat treatment is constant for up to 6% of the composition and suddenly increases for 9% composition and also it behaves same for heat treated specimens because of formation of equilibrium precipitates Ni3Ti formation. 2017 Elsevier Ltd. -
Investigation into the Mechanical, Fatigue and Superplastic Characteristics of Shape Memory Alloys (SMA) in CuAlMn, CuAlBeMn, and CuAlFeMn Compositions and Their Composite Variants
Shape memory alloys (SMAs) exhibit high sensitivity to compositional changes in terms of their super elasticity, shape memory effect, and transition temperatures. A deeper comprehension of SMA composition and its impact on mechanical properties can be attained by differential scanning calorimetry. The current study uses experimental work to assess the energy absorption capacity, mean fracture width, residual strength, and cracking strength of samples made of short shape memory alloy (SMA) fibers that are randomly distributed on the specimens tensile side. In this investigation, three samples were synthesized based on the Cu, Al, and Mn proportions found in CuAlMn shape memory alloys (SMA1, SMA2, and SMA3). Moreover, three samples with different ratios of Cu, Al, Mn, Be, and Fe were synthesized for the shape memory alloys CuAlBeMn and CuAlFeMn (SMA2, and SMA3). The synthesized CuAlMn, CuAlBeMn, and CuAlFeMn SMA alloys showed good strain recovery, ranging from 90 to 95%. The martensite that forms and changes when the alloys are heated and quenched mostly controls the strain recovery by the corresponding SMAs. SMA 2 of the CuAlBeMn has a greater strain recovery rate, rising by 8.5% and 44.38%, respectively, in comparison to SMA 1 and SMA 3. CuAlBiMn shape memory alloys demonstrated superior super elasticity and martensite stability in comparison to SMA 1 and SMA 2 respectively. SMA 1 and SMA 2 demonstrated greater residual strength, cracking strength, and energy absorption capacity for all fiber volume fractions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.