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CONFIDENTIAL TRAINING AND INFERENCE USING SECURE MULTI-PARTY COMPUTATION ON VERTICALLY PARTITIONED DATASET
Digitalization across all spheres of life has given rise to issues like data ownership and privacy. Privacy-Preserving Machine Learning (PPML), an active area of research, aims to preserve privacy for machine learning (ML) stakeholders like data owners, ML model owners, and inference users. The Paper, CoTraIn-VPD, proposes private ML inference and training of models for vertically partitioned datasets with Secure Multi-Party Computation (SPMC) and Differential Privacy (DP) techniques. The proposed approach addresses complications linked with the privacy of various ML stakeholders dealing with vertically portioned datasets. This technique is implemented in Python using open-source libraries such as SyMPC (SMPC functions), PyDP (DP aggregations), and CrypTen (secure and private training). The paper uses information privacy measures, including mutual information and KL-Divergence, across different privacy budgets to empirically demonstrate privacy preservation with high ML accuracy and minimal performance cost. 2023 SCPE. -
Conductivity/Electrochemical Study of Polyvinyl pyrrolidone-Poly(vinyl alcohol)/I3? Thin Film Electrolyte for Integrated Dye-Sensitized Solar Cells and Supercapacitors
Abstract: The current era focuses not only on producing solar energy but also preserving it for future use. Dye-sensitized solar cells (DSSC) and supercapacitors (SC) are such energy-based devices. DSSCs capture the solar energy and SCs store this captured energy. A natural anthocyanin dye extracted from Garcinia indica (kokum fruit) was used in the DSSCs. SnO2, one of the promising electrode materials for DSSC, was synthesized via a microwave technique. Blend polymer electrolytes (BPE) were prepared through a solution casting technique. A polyvinyl pyrrolidone (PVP) and polyvinyl alcohol (PVA) blend with varying concentrations of potassium iodide, along with iodine dopant, was prepared as a BPE electrolyte composition. The best of the PVA-PVP/KI composition was chosen using Nyquist plots of electrochemical impedance spectroscopy (EIS). Varying the temperature, the dielectric and conductivity study of the chosen composition was studied in detail. A fast/single-step synthesis technique, namely a laser-engraved approach, was used for few-layer graphene synthesis. This graphene serves as a common platform for the DSSC-SC integrated device: as a counter electrode in DSSC and graphene-graphene symmetric electrode in SC. A DSSC-SC integrated device was fabricated and characterized using various analytical and microscopy techniques. The integrated device showed a 0.42 fill factor and 0.56% efficiency. The discharge time for integrated DSSC-SC cells was found to be increased threefold. Graphical Abstract: [Figure not available: see fulltext.] 2020, The Author(s). -
Conducting polymers: A versatile material for biomedical applications /
ChemistrySelect, Vol.7, Issue 42, ISSN No: 2365-6549.
Conducting polymers (CPs) are organic polymers with metallic conductivity or semiconducting properties which have drawn considerable attention globally. They are versatile materials because of their excellent environmental stability, electrical conductivity, economic importance as well as optical and electronic properties. CPs are interesting because they can be functionalized in several ways and the chemical properties are fine-tuned by incorporating new functionalities, making them more suitable in biomedical and other applications. -
Conducting Polymers: A Versatile Material for Biomedical Applications
Conducting polymers (CPs) are organic polymers with metallic conductivity or semiconducting properties which have drawn considerable attention globally. They are versatile materials because of their excellent environmental stability, electrical conductivity, economic importance as well as optical and electronic properties. CPs are interesting because they can be functionalized in several ways and the chemical properties are fine-tuned by incorporating new functionalities, making them more suitable in biomedical and other applications. They act as appropriate mediums of biomolecules and can be employed to improve the speed, stability, and sensitivity of various biomedical devices. They can transit between conducting and semiconducting states and have the ability to change mechanical properties by regulated doping, chemical modifications, etc. In this paper, we review the potential biomedical uses of conducting polymers such as smart textiles, bioactuators, hydrogels, and the use of CPs in neural prosthetic devices. 2022 Wiley-VCH GmbH. -
Condensate phases of nuclear matter from AdS hardwall models
This work develops our previous study of confined phases at finite densities in AdS/QCD by systematically exploring the possibility of baryonic condensates. Using phenomenologically motivated boundary conditions in an AdS hardwall model, we show that both baryonic and quark-type condensates dominate the phase diagram at low temperatures. We also undertake a careful scan of the parameter space to extract robust conclusions. (2025), (American Physical Society). All rights reserved. -
Concurrent design, modeling and analysis of Microelectromechanical Systems products - Design for 'X' abilities
In this paper, we present the need for concurrent engineering in Microelectromechanical System (MEMS) device and product development. MEMS system is considered as six subsystems: micromachined element design subsystem, microelectronics circuit design subsystem, fabrication subsystem, packaging subsystem, materials subsystem and environment subsystem. Design for 'X' abilities is addressed by considering six subsystems/abilities. A concurrent model is developed using graph theory to show the interaction between subsystems. This work utilizes the advantages of the graph theoretic approach to consider all design aspects together in a single methodology with the help of a multinomial defined using matrix algebra. The design index developed using the proposed methodology shows the interaction among the subsystems and indicates whether the overall design is acceptable or not, by considering all the aspects related to micromachined element design, microelectronics circuit design, fabrication, packaging, materials, environment etc. A MEMS based RF power sensor is designed and the proposed methodology is explained. Simulated results of the RF MEMS power sensor are presented to validate the proposed methodology. A power sensor with VSWR of 1.08002 is reported. 2012 Bentham Science Publishers. -
Conclusion and future research directions
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Conclusion
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Conclusion
Digital data produced through data-processing algorithms has fundamental advantages of transportability, proficiency, and accuracy; but on the other hand, the data thus produced brings in several redundancies. To solve this challenging problem with data transmission in network surroundings, research on information security and forensics provides efficient solutions that can shield the privacy, reliability, and accessibility of digital information from malicious intentions. Despite two decades of rigorous research, multicarrier communications still suffer from high complexity and low convergence, which have an immense practical impact. It is also more challenging to ensure proper transmission of multimodal data. Novel techniques have been proposed that can effectively abate these problems and provide good symbol error rate performance. The singularity expansion method provides a superior way to identify targets. This type of method may be useful for addressing contemporary problems faced by radar and antenna researchers. 2021 John Wiley & Sons Ltd. -
Conclusion
We all can agree at one point: the COVID-19 pandemic has had a massive and unanticipated impact on all the lives of all tourists. The global tourism and hospitalityindustry has been heavily damaged, but the societal impact cannot be overlooked. Consumer behavior, and ultimately consumer spending, has been and will continue to change, and company planning must adapt to these new realities. The major findings of this edited book in the contexts of tourism, destination recovery and crisis management thus have value for the industry and for researchers seeking to understand these changes. Chapter 1 analyses evolution of tourism and hospitality during times of crisis and how these businesses might rebound. Academics in the field of tourism and hospitality can use this collection to understand the most recent studies on crises and recovery. The impact of the COVID-19 crisis on tourism and hospitality was examined in several published pieces. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Conclusion
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Concerns in IoT Environments: Adoption, Architecture, and Innovation of Enterprise IoT Systems
The Internet of Things (IoT) has received a lot of interest in recent times. IoT depicts the upcoming internet and is defined as an environment of linked gadgets, computational processes, and other items that collaborate to transmit information or data with greater ease and economic advantages. Nevertheless, because of the presence of numerous concerns, IoT adoption, architecture, and innovation continue concerns. As a result, the purpose of this study was to identify and analyze the concerns in the adoption, architecture, and innovation of IoT systems in construction enterprises in the Indian environment. The research analysis and professional comments have been employed to identify the barriers to IoT adoption, architecture, and innovation. This research may assist professionals and policymakers in addressing barriers to successful IoT adoption and spread. At last, findings and potential research possibilities are provided. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Concernment of Feature Selection Using Classification Algorithms and Developing the Web Frame for Breast Cancer Prediction
Breast cancer is invasive cancer and it is the most common cancer diagnosed in women. The survival rate of breast cancer patients is increasing due to timely detection, better empathy about the disease, and new tailored approach for the treatment. Even hormonal imbalance, environmental factors, gene mutation, and lifestyle are also the reasons for breast cancer. Stages of breast cancer majorly depend on the size of the tumor as well as the spreading of cancer to the lymph nodes. An instinctive disease detection system and computer-aided diagnosis will help the medical practitioners in early prediction of breast cancer using machine learning algorithms. In this paper, Random Forest for ranking the features by assigning the weights and selection of features using support vector machine and Nae Bayes are used. The Breast Cancer Wisconsin Dataset from the UCI Repository has been taken for examination purposes. Features selected from support vector machine and Naive Bayes have been tested by using seven different classifiers: logistic regression, random forest, K-nearest neighbor, support vector classifier, linear support vector classifier, Gaussian Naive Bayes, and decision tree. Based on the experimental results with 7030 and 8020 splits, 7030 is obtained with the best accuracy. Support vector machine with 12 features resulted in an accuracy of 97.66% and Nae Bayes with 17 features resulted in an accuracy of 96.49% with the improved results as compared to without feature selection. As support vector machine resulted with best accuracy with 12 features, by using these 12 features, web application for the prediction of breast cancer has been developed using Web framework using Python Flask, PyCharm IDE, and the instance has been executed virtually in the Amazon EC2 cloud Platform. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Conceptual framework of artificial intelligence in human resource management /
Patent Number: 202221003373, Applicant: Rishu Roy.
Developing a conceptual framework for using artificial intelligence in human resource management is the goal of this study (HRM). The six main human resource management theory characteristics are integrated with AI technology's possible applicability. Human resource management encompasses a variety of facets, including human resource strategy and planning, recruiting, training and development, performance assessment, and management of employee relations. -
Conceptual comprehension analysis of a student using soft cosine measure
Knowledge is the substantial wealth of a man and he possesses an innate thirst to acquire it. Knowledge embodies facts or ideas acquired through study, investigation and observation or experience. In this context, technology with its varied techniques comprising endless algorithms in natural language processing (NLP) plays an imperative role in the pursuit of knowledge. Inferences thus gathered are a clear pointer to the content teaching of students. Soft cosine measure algorithm is used in this analysis process to provide an answer regarding the grasping ability of each student with optimum learner participation and creativity. After each lecture, students have to upload their corresponding notes and this in turn would be compared with the teacher's lecture notes. The soft cosine computation gives individual results, on how much each student has comprehended a concept. This new methodology is a much awaited contribution of the educational field. 2022 Author(s). -
Concept Mapping of Issues of Students Life in University
The undergraduate student body forms around 85.9% of the total number of students enrolled in India, which is a significant population. It has become imperative to understand the issues that these students face during their undergraduate years as a precursor to developing mechanisms and strategies to enable student progress, both academically and developmentally. This study aimed at developing a concept map to outline the various aspects and issues of the undergraduate students life in India utilizing the concept mapping method. Data from participants (n = 141) at different phases was analysed resulting in 49 unique life issues and aspects and 8 clusters. The emerging issues have relevance and implications for teachers, parents, administrators and other stakeholders in structuring and developing services targeted towards undergraduate students in India. 2015, National Academy of Psychology (NAOP) India. -
Concept Drift Detection for Social Media: A Survey
The research over information retrieval from social media data has progressed for streaming data since the last decade. Recently, academic researchers have witnessed users' changing topics, trends, and intent on social media. This change of information with time takes into account the temporal attribute for real-time data, and thus, advances in this domain are exponentially growing. Although concept drift is still not explored due to a shortage of available datasets, concept drift for social media is minimally explored. This manuscript makes attempts to identify the types of concept drift for social media data, discuss the historical perspective of concept drift on social media, and enlist the possible research directions. 2021 IEEE. -
Concentration-dependent luminescence characterization of terbium-doped strontium aluminate nanophosphors
The present investigation describes the synthesis of luminescent terbium-doped strontium aluminate nanoparticles emitting bright green light, which were synthesized through a solid-state reaction method assisted by microwave radiation. Various samples containing different concentrations of Tb were synthesized, and an analysis of their structural and morphological features was conducted using powder x-ray diffraction, Fourier transform infrared spectroscopy and field emission scanning electron microscopy. The band gaps of the samples were determined utilizing the KubelkaMunk method. The quenching mechanism observed was identified to be due to dipoledipole interaction using the Dexter theory. The optimized sample with a terbium concentration of 4at.% has a luminescence lifetime of 1.05 ms with 20.62% quantum efficiency. The results of this study indicate that the terbium-doped strontium aluminate fluorescent nanoparticles exhibit promising potential for a wide range of applications, including bioimaging, sensing and solid-state lighting. 2024 John Wiley & Sons Ltd. -
Computing isogeny on Edwards curves for quantum safe cryptography
In recent years, cryptographic research has seen a surge of interest in post-quantum cryptography driven by the potential threat that quantum computers pose to traditional public-key cryptosystems. Isogeny-based cryptography is a promising method in post-quantum cryptography, relying on the computational challenge of calculating isogenies, which are specific mappings between elliptic curves. The efficiency of isogeny computations is vital for real-world cryptographic applications. However, computing isogenies, especially with large parameters, can be very resource intensive. To overcome this challenge, we purpose an efficient method for computing odd-degree isogenies on certain form of an elliptic curves by employing an auxiliary coordinate. Our work appears to bridge the gap in computational efficiency for odd-degree isogenies, especially in terms of reducing the complexity of the isogeny computations when compared to traditional affine and projective methods. The derived formula is more efficient than affine and projective cases. We also analyse the algebraic complexity of these calculations and compare them to alternative formulae. Additionally, we evaluate the runtimes for isogeny computation across different prime numbers and compare them with other elliptic curve model to check the performance. At last, we suggest potential avenues for future work. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
Computerized grading of brain tumors supplemented by artificial intelligence
For effective diagnosis of health conditions, there is a need to process medical images to obtain meaningful information. The diagnosis of brain tumors begins with magnetic resonance imaging (or MRI) scan. This is followed by segmentation of the medical images so obtained which can prove cumbersome if it were to be performed manually. Determining the best approach to do segmentation remains challenge among multiple computerized approaches. This paper combines both the identification and classification of tumors from the MRI results and is backed by a cloud-based framework to provision the same. The phase of extraction of features includes the utilization of a Hadoop framework and Gabor filter along with variations in terms of orientation and scale. Artificial bee colony algorithm and support vector machine classifier have been used to designate the degree of optimal features and categorize the same. The grading of brain tumors from MRI images can be fulfilled by the aforementioned approach. The said approach is believed to deliver promising results in terms of accuracy, which has also been verified experimentally. 2019, Springer-Verlag GmbH Germany, part of Springer Nature.