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Inquiry into reverse logistics and a decision model /
International Journal Logistics Systems And Management, Vol.33, Issue 3, pp.353-382, ISSN No: 1742-7967. -
Insider attack detection using deep belief neural network in cloud computing
Cloud computing is a high network infrastructure where users, owners, third users, authorized users, and customers can access and store their information quickly. The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently. This cloud is nowadays highly affected by internal threats of the user. Sensitive applications such as banking, hospital, and business are more likely affected by real user threats. An intruder is presented as a user and set as a member of the network. After becoming an insider in the network, they will try to attack or steal sensitive data during information sharing or conversation. The major issue in today's technological development is identifying the insider threat in the cloud network. When data are lost, compromising cloud users is difficult. Privacy and security are not ensured, and then, the usage of the cloud is not trusted. Several solutions are available for the external security of the cloud network. However, insider or internal threats need to be addressed. In this research work, we focus on a solution for identifying an insider attack using the artificial intelligence technique. An insider attack is possible by using nodes of weak users systems. They will log in using a weak user id, connect to a network, and pretend to be a trusted node. Then, they can easily attack and hack information as an insider, and identifying them is very difficult. These types of attacks need intelligent solutions. A machine learning approach is widely used for security issues. To date, the existing lags can classify the attackers accurately. This information hijacking process is very absurd, which motivates young researchers to provide a solution for internal threats. In our proposed work, we track the attackers using a user interaction behavior pattern and deep learning technique. The usage of mouse movements and clicks and keystrokes of the real user is stored in a database. The deep belief neural network is designed using a restricted Boltzmann machine (RBM) so that the layer of RBM communicates with the previous and subsequent layers. The result is evaluated using a Cooja simulator based on the cloud environment. The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine. 2022 CRL Publishing. All rights reserved. -
Insight into the effects of waste vegetable oil on self-healing behavior of bitumen binder
The application of waste vegetable oil (WVO) in bitumen has been the subject of research for years, however, the self-healing behavior of WVO modified bitumen (WMB) has not been adequately reported. In this research, molecular dynamics (MD) simulations and laboratory experiments were performed to reveal the effects of WVO on the self-healing behavior of bitumen. Models of base bitumen and WMB were constructed. Further, dynamic calculations were carried out for the self-healing models of base bitumen and WMB both with 10 microcracks. The energy properties, conformation and density of bitumen during the self-healing process were analyzed. Meanwhile, the effects of WVO on the fractional free volume (FFV) of bitumen, the distribution of bitumen components and the mobility of bitumen molecules were investigated. Finally, the modified fatigue-healing-fatigue (FHF) test was conducted to verify the effects of WVO on the self-healing efficiency of bitumen. Results show that Van der Waals forces drive the mobility of bitumen molecules. Along with the disappearance of the central microcrack, the density of the self-healing system gradually increases and finally reaches that of the bulk bitumen. WVO with superior mobility capacity increases the FFV of bitumen and converts asphaltene large aggregated structure into small aggregated structure, which facilitates the mobility of the bitumen during the self-healing process. Thus, the addition of WVO contributes to the self-healing efficiency of the bitumen. The modified FHF test also verified that the self-healing efficiency of bitumen is improved with the presence of WVO. These findings provide further insight into the self-healing behaviors of WMB. 2022 -
Insights into Artificial Neural Network techniques, and its Application in Steganography
Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions. 2021 Institute of Physics Publishing. All rights reserved. -
Insights into the performance of farmer producer companies: An exploratory analysis in kerala, india
India faces challenges in farmers well-being due to small land holdings and the absence of an organized agricultural market system, prompting the introduc-tion of initiatives such as the promotion of Farmer Producer Organizations (FPOs) to fortify the agricultural market. However, ambiguity remains over the functioning of such initiatives. This study aims to tackle the aforementioned issue by collecting and analyzing data on the operations of 400 registered Farmer Producer Companies (FPCs) in the state of Kerala, India. The analysis focuses on key indicators such as the age of the FPC, paid-up capital, and activity status (whether active or struck-off). A primary survey has also been conducted, both telephonic and face-to-face inter-views, based on convenience, using a semi-structured questionnaire. Both farmers and representatives from FPCs are interviewed. The issues discussed are marketing assistance, input sales, type of services offered, managerial capacities, etc. The study finds that not all registered FPCs effectively improve the welfare of farmers. The most common service FPCs provide is marketing, which helps increase product sales through collective bargaining power and access to higher-paying markets. FPCs that engage in contract farming, sell through supermarkets, or explore inter-state or inter-national markets are more successful in increasing farmers revenues than those that only use traditional market chains. However, it is important to note that FPCs face several challenges, including a lack of infrastructure and support, limited access to credit, and a lack of skilled and trained staff. These essential factors must be addressed for FPCs to achieve their full potential. The Author(s). -
Insights into the plant growth promotion properties of bacterial endophytes isolated from Alternanthera philoxeroides and Eichhornia crassipes from Bellandur lake, India
The presence of macrophytes such as Alternanthera philoxeroides and Eichhornia crassipes in Bellandur Lake, India, has been observed despite the high pollution levels. Our research aims to explore the potential role of endophytes in promoting the growth of these macrophytes in such heavily contaminated environments. In current study, we isolated 20 endophytic bacteria from various parts of A. philoxeroides (12) and E. crassipes (8) plants, including shoots, roots, leaves, and flowers. We found that a significant proportion of endophytes from A. philoxeroides (42 %) and E. crassipes (25 %) produced more than 100 g/mL of indole acetic acid (IAA). Similarly, the majority of the isolates possessed other plant growth promoting traits like ammonia production, nitrogen fixation, phosphate and potassium solubilization, siderophore production, and ACC deaminase production. Many of these isolates demonstrated extracellular enzyme production and halotolerance properties. We identified Acinetobacter soli ON529869 and Bacillus licheniformis ON506048 as the most effective plant growth promoters among the isolates, which also displayed antifungal properties against Fusarium solani and Cladosporium tenuissimum in vitro. Furthermore, greenhouse trials using these two endophytes revealed their significant plant growth promotion abilities in Amaranthus viridis. The pigment indices were evaluated using CI-710 Leaf Spectrometer. In conclusion, our findings provide compelling evidence for exploring the endophytic microbiomes of macrophytes in polluted areas for sustainable agriculture and bioprospecting of unique traits. This research could lead to the discovery of new and valuable resources for agricultural practices and other applications. 2023 SAAB -
Insights into thyroid disease: Harnessing machine learning for analysis and classification of multi-label medical data
Thyroid disease refers to a wide range of disorders that occur due to dysfunction of the thyroid gland, a small gland located at the base of the neck that produces thyroid hormones. Through the analysis of this comprehensive dataset, we aim to utilize machine learning (ML) techniques for the analysis and classification of thyroid data. Employing ML techniques for thyroid classification has the potential to improve diagnostic accuracy, facilitate timelier interventions, lower expenses, optimize doctor time, and foster a more personalized approach to thyroid care. The objective of this research was to conduct multiclassification, encompassing the broadest array of classes for the target variable. To address the imbalances within the dataset, we employed the Synthetic Oversampling Technique (SMOTE) as a resampling method. Specifically, classes with a minimum of 10 samples were retained, resulting in the inclusion of 19 out of the total 34 classes in the dataset. The importance of SMOTE in addressing class imbalance is examined in this chapter, with an emphasis on how it may be used to enhance classifier model performance. Moreover, we conducted a comparative analysis of Classification Models, including Random Forest, K-Nearest Neighbor, Decision Tree, SVM, Gradient Boosting, Multinomial Naive Bayes, and Logistic Regression, to assess their accuracies. Following the resampling of the dataset, the highest accuracy of 99.99% was achieved with the gradient booster. Additionally, this research incorporated the association rule technique to uncover meaningful relationships within the dataset. 2025 selection and editorial matter, Arun Kumar Rana, Vishnu Sharma, Sanjeev Kumar Rana, and Vijay Shanker Chaudhary; individual chapters, the contributors. All rights reserved. -
Insights of Evolving Methods Towards Screening of AI-Enhanced Malware in IoT Environment
Internet-of-Things (IoT) has been encountering a series of potential form of threats since past half decades. Artificial Intelligence (AI), which is frequently seen to be adopted to solve various challenges in IoT operation, has now been adopted even by attackers for their malicious purposes. Of all forms of threats, AI-enhanced malwares are one of the most potential forms of threats which has its extensive effectiveness towards the complete operation of the entire IoT environment. Hence, this manuscript discusses existing detection and prevention approaches evolved in current literatures to understand various taxonomies of solution-based methodologies for circumventing such threats. The paper also contributes towards highlighting the potential open-ended issues that are yet to be addressed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Insights on bar quenching from a multiwavelength analysis: The case of Messier 95
The physical processes related to the eect of bars in the quenching of star formation in the region between the nuclear/central subkiloparsec region and the ends of the bar (bar region) of spiral galaxies is not fully understood. It is hypothesized that the bar can either stabilize the gas against collapse, inhibiting star formation, or eciently consume all the available gas, leaving no fuel for further star formation.We present a multiwavelength study using the archival data of an early-type barred spiral galaxy, Messier 95, which shows signatures of suppressed star formation in the bar region. Using optical, ultraviolet (UV), infrared, CO, and HI imaging data we study the pattern of star formation progression and stellar/gas distribution, and try to provide insights into the process responsible for the observed pattern. The FUV NUV pixel colour map reveals a cavity devoid of UV flux in the bar region that matches the length of the bar, which is 4.2 kpc. The central nuclear region of the galaxy shows a blue colour clump and along the major axis of the stellar bar the colour progressively becomes redder. Based on a comparison to single stellar population models, we show that the region of galaxy along the major axis of the bar, unlike the region outside the bar, is comprised of stellar populations with ages 350 Myr; there is a star-forming clump in the centre of younger ages of 150 Myr. Interestingly the bar region is also devoid of neutral and molecular hydrogen but has an abundant molecular hydrogen present at the nuclear region of the galaxy. Our results are consistent with a picture in which the stellar bar in Messier 95 is redistributing the gas by funnelling gas inflows to nuclear region, thus making the bar region devoid of fuel for star formation. ESO 2019. -
Insights on research techniques towards cost estimation in software design
Software cost estimation is of the most challenging task in project management in order to ensuring smoother development operation and target achievement. There has been evolution of various standards tools and techniques for cost estimation practiced in the industry at present times. However, it was never investigated about the overall picturization of effectiveness of such techniques till date. This paper initiates its contribution by presenting taxonomies of conventional cost-estimation techniques and then investigates the research trends towards frequently addressed problems in it. The paper also reviews the existing techniques in well-structured manner in order to highlight the problems addressed, techniques used, advantages associated and limitation explored from literatures. Finally, we also brief the explored open research issues as an added contribution to this manuscript. 2017 Institute of Advanced Engineering and Science. All rights reserved. -
Insights on the Optical and Infrared Nature of MAXI J0709-159: Implications for High-Mass X-ray Binaries
In our previous study (Bhattacharyya et al., 2022), HD 54786, the optical counterpart of the MAXI J0709-159 system, was identified to be an evolved star, departing from the main sequence, based on comparisons with non-X-ray binary systems. In this paper, using color-magnitude diagram (CMD) analysis for High-Mass X-ray Binaries (HMXBs) and statistical t-tests, we found evidence supporting HD 54786s potential membership in both Be/X-ray binaries (BeXRBs) and supergiant X-ray binaries (SgXBs) populations of HMXBs. Hence, our study points towards dual optical characteristics of HD 54786, as an X-ray binary star and also belonging to a distinct evolutionary phase from BeXRB towards SgXB. Our further analysis suggests that MAXI J0709-159, associated with HD 54786, exhibits low-level activity during the current epoch and possesses a limited amount of circumstellar material. Although similarities with the previously studied BeXRB system LSI +61? 235 (Coe et al., 1994) are noted, continued monitoring and data collection are essential to fully comprehend the complexities of MAXI J0709-159 and its evolutionary trajectory within the realm of HMXBs. 2024 Societe Royale des Sciences de Liege. All rights reserved. -
Insinuating cocktailed components in biocompatible-nanoparticles could act as an impressive neo-adjuvant strategy to combat COVID-19
The novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread, freezes all sectors, and was declared as a life-threatening disease by the World Health Organization on Jan 30, 2020. So far, no specific drugs are identified or approved for treating SARS-CoV-2. In the past few years, nanomaterials are in the limelight for their ability to deliver the drugs effectively and selectively like siRNA to target/prime infection sites, and benefits us to visualize the particular regions, treatment reactions via non-intruding imaging techniques. As intranasal delivery interacts directly to the infection site with minimal side effects on the healthy cell, we postulate to administer a mixture of few polyherbal formulations like the golden spice curcumin, sitopaladi churna (SPC), and kaempferol in zein-chitosan nanoparticles as a life-saving measure for treating Coronavirus disease (COVID-19) cases. This viewpoint will shed light on the antiviral role of curcumin, SPC, and kaempferol zein-chitosan nanoparticle to modulate immune responses and observe its curative approach to the current pandemic COVID. 2021, Visagaa Publishing House. All rights reserved. -
Instabilities in ferrofluids with temperature and magnetic field dependent viscosity under different modulations
There has been a vigorous effort by researchers to study and characterize Rayleigh Be and#769;nard convection in ferrofluids owing to their interesting applications. From the survey of the literature pertinent to the problems under consideration in our study we realize the importance of using variable viscosity and thus explore the dynamics of a ferrofluid with temperature and magnetic field dependent viscosity which is in a Rayleigh- Be and#769;nard situation. The system under consideration is subjected to external constarints viz., an imposed time-periodic body force, rotation speed modulation, temperature modulation and magnetic field modulation. The problem considers both sinusoidal and non-sinusoidal time-periodic variations of these modulations to study the onset and post-onset regimes of Rayleigh-Be and#769;nard ferroconvection. We perform a weakly non-linear stability analysis using a truncated Fourier series representation and arrive at the Lorenz system for ferrofluid convection with variable viscosity. By using the linearized form of the Lorenz system we arrive at the critical Rayleigh There has been a vigorous effort by researchers to study and characterize Rayleigh Be and#769;nard convection in ferrofluids owing to their interesting applications. From the survey of the literature pertinent to the problems under consideration in our study we realize the importance of using variable viscosity and thus explore the dynamics of a ferrofluid with temperature and magnetic field dependent viscosity which is in a Rayleigh- Be and#769;nard situation. The system under consideration is subjected to external constarints viz., an imposed time-periodic body force, rotation speed modulation, temperature modulation and magnetic field modulation. The problem considers both sinusoidal and non-sinusoidal time-periodic variations of these modulations to study the onset and post-onset regimes of Rayleigh-Be and#769;nard ferroconvection. -
Instruments for measuring Digital Citizenship Competence in schools: a scoping review
The integration of digital technology into the teaching and learning process has both good and negative consequences. Several schools have incorporated digital citizenship to teach the responsible use of technology. The purpose of this scoping review is to provide an overview of research on tools for measuring digital citizenship competency among school children. This scoping study focuses on three main areas: (a) defining digital citizenship and competency; (b) instrument development and characteristics; and (c) key findings. The main outcomes of this research may help students, teachers, and school administrators implement digital citizenship education programs in schools. Italian e-Learning Association. -
Insurance coverage framework for assisted reproductive treatments for women
Giving birth to a child is considered as one of the purest and highest forms of giving by any human being. The harsh reality is that not all men and women can reproduce. Some remain childless their entire life. Infertility hurts both men and women, but women more. Women continue to face social stigma of not bearing a child and go through stress, anxiety, and depression (Donkar, 2007; Widge, 2002; Reissman, 2000). One of the most significant contributions of Medical Science is the invention of Assisted Reproductive Technology Treatments that help infertile couples to conceive. India has been a pioneer in adapting to this technology and since 1978 many couples have been able to give birth to a child. Unfortunately, these treatments are expensive. Infact, the high treatment cost is the predominant source of anxiety in patients going through these treatments across the world (Iaconelli, 2013). There are instances where, couples leave the treatment, mid way as they are not able to arrange more money (Brennan et al., 2006). However, countries like Denmark, Canada, New Zealand, Belgium, and Japan, to name a few have included these treatments under their Government health insurance policy, whereas countries such as UK, USA, and Singapore have their Private Health insurance companies covering them. In comparison with its western counterparts, India is far behind in using Insurance as a method to finance Assisted Reproductive treatments. A preliminary study indicated two things- The resistance from the Insurance company's side in venturing into a product of this sort and secondly, unavailability of an insurance framework to guide them to venture into such a product. Thus, study began with the sole intention of creating an Insurance framework for assisted Reproductive Treatments particularly for women. A Qualitative Methodology has been adapted for the study. As a first step, infertility treatment polices from developed and developing countries were gathered and analyzed to extract the components of the drafting an Assisted re productive Treatment policy framework. Using these components, interview schedules were made to solicit information from the three different stakeholders. In-depth face to face interviews with 13 Doctors, 12 Patients, and 10 Insurers were conducted and data was further analyzed using Qualitative Content Analysis Method as prescribed by Olle Rudolf Holsti in 1968 and refined by Downe-Wamboltdt in 1992. The concepts of reduction, distillation, and condensation as prescribed by Olle Findahl in 1981, Stephen Cavanagh in 1997, and Paul Atkinknson in 1996 have been used. The results pave the way for the proposed framework. This framework can be used as a guide by Insurance companies in defining the disease, designing the value proposition, entry and exit age, coverage as per stage of treatment, designing the proposal form to solicit information from the insured, arriving at the sum Insured, drafting conditions and exclusions of the policy, pricing, and promotional aspects, mitigation of moral hazards and claims management. The framework suggests a model that can benefit patients to avail insurance at a nominal price. -
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Insurance Data Analysis with COGNITO: An Auto Analysing and Storytelling Python Library
Data pre-processing has taken an enhanced role with the advent of Machine learning. It is a vital element that forms the encore of the data science and business analytics process. Data pre-processing involves generating descriptive statistical summary, data cleaning, and data manipulation based on inputs gained after the initial analysis. Of late, it has been observed that data science practitioners spend 45% to 50% of their time cleaning and processing the data. Much time can be saved if the data transformation process can be automated. The COGNITO framework helps in performing the automated feature engineering and data storytelling of the dataset based on end-user discretion. The present work discusses the process and results obtained when automated feature engineering was performed on an insurance dataset using COGNITO. 2021 IEEE. -
Insuretech: Saviour of insurance sector in India
Technology in finance has propelled financial literacy and inclusiveness and may give the insurance sector an edge to reach its potential consumers. The current study aimed to identify the role of Fintech in transforming the insurance sector and improving the penetration rate in India. With the descriptive research design, the study collects the primary data through a survey technique targeting the general public and personnel in the insurance sector as a study population. A conceptual model is proposed to understand the interlink between consumer attitude towards Insurance, factors influencing their decision, and the role of Fintech in bridging the gap in insurance penetration. This study focuses on three areas, namely health insurance, life insurance, and vehicle insurance. The study's findings reveal that the insurtech will significantly improve the efficiency of the insurance sector which will result in significant financial performance. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Integral Transforms andGeneralized Quotient Space ontheTorus
In this chapter, we discuss one of the recent generalization of Schwartz distributions that has significantly influenced the expansion of various mathematical disciplines. Here, we study the space of generalized quotient on the torus. Different integral transforms are investigated on the space of generalized quotients on the torus BS?(Td). The space BS?(Td) is made of both distributions as well as space of hyperfunctions on the torus. Further, by introducing the relation between the Fourier and other integral transforms, the conditional theorems are proved for generalized quotients on tours. Moreover, we study the convergence structure of delta-convergence on the generalized quotient space, and an inversion theorem is proved. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence
In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brain function is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer's disease, Schizophrenia and Parkinson's diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks (DBN). Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm (DBNJZZ) approach. The suggested approach is executed and tested by using the performance metric measure such as accuracy, root mean square error, Mean absolute error and mean absolute percentage error. Proposed DBNJZZ gives better performance than previously available methods. 2023 Authors. All rights reserved.