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Message from IEEE InC4 2023 Program Chair
[No abstract available] -
Message from IEEE InC4 2024 Program Chair
[No abstract available] -
Security mechanisms in cloud computing-based big data
In the existent system, data is encrypted and stored when passed to the cloud. During any operations on the data, it is decrypted and then the computation is done. This decrypted data is vulnerable and prone to be misused. After the computations are done, the data and the result are encrypted and stored back in the cloud. This creates an overhead to the system as well as increases time complexity. With this chapter, the authors aim to reduce the overhead of the systems to perform repeated encryptions and decryptions. This can be done by allowing the computations to happen directly on the encrypted text. The result obtained by performing computations on encrypted data will be the same as the ones done on the original plain text. This new security solution is fully fit for processing and retrieval of encrypted data, effectively leading to the broad applicable project, the security of data transmission, and the storage of data. The work is secured further with additional concepts like probabilistic and time stamp-based encryption processes. 2021, IGI Global. -
Security mechanisms in cloud computing-based big data
In the existent system, data is encrypted and stored when passed to the cloud. During any operations on the data, it is decrypted and then the computation is done. This decrypted data is vulnerable and prone to be misused. After the computations are done, the data and the result are encrypted and stored back in the cloud. This creates an overhead to the system as well as increases time complexity. With this chapter, the authors aim to reduce the overhead of the systems to perform repeated encryptions and decryptions. This can be done by allowing the computations to happen directly on the encrypted text. The result obtained by performing computations on encrypted data will be the same as the ones done on the original plain text. This new security solution is fully fit for processing and retrieval of encrypted data, effectively leading to the broad applicable project, the security of data transmission, and the storage of data. The work is secured further with additional concepts like probabilistic and time stamp-based encryption processes. 2021, IGI Global. -
A machine learning model for population analysis among different states in India which influences the socio, demographic and economic needs of society
In this work Data from 2011 census is taken to identify the state which influences more in Population census among the different states identified. The data is considered from Madhya Pradesh, followed with Utter Pradesh, then to Bihar, Bengal and Orissa. Similarly other case studies are also done for Southern Indian states and North Eastern States. Genetic algorithm will be tried to find the optimal location for the given study. A fitting function is calculated for the population data of 2011 using Lagrange Interpolation technique. This fitting function is given as input to Genetic algorithm to find the optimal state which have maximum influence in the population growth among different states of India as per the Case studies done. BEIESP. -
Revocable and Secure Multi-Authority Attribute-Encryption Scheme
Security is an important factor as nowadays many systems generates and process huge amount of data. This also leads many of us to rely on a third-party service provider for storing sensitive and confidential data. Providing outsourcing means the data owner will encrypt and store the data in a third-party storage system. In this paper, we are proving solutions for two main problems. The first issue is what if the attribute authority itself can access the data because the attributes and secret keys are known by attribute. This issue is called the key escrow problem. For solving it we are proposing a multi-authority system with Elliptic Curve Cryptography. The second issue that we addressed in this paper is the revocation problem, which means when someone leaves the system should be prohibited from accessing subsequent data this is called forward security and as a second case when someone joins the system should be prevented from accessing previous shared date this is called backward security. In this paper, we address both forward and backward security. For solving this problem we are using the concept of the Lagrange interpolation technique for generating and verifying secret keys. Based on this technique secret key will be dynamically altered and used for encryption and due to this can achieve greater security. 2023, Ismail Saritas. All rights reserved. -
Security Analysis for a Revocable Multi-Authority ABE-Attribute-Based Mechanism
Due to the tremendous increase in data, groups or even organizations are storing data with third-party providers to solve storage problems. Ciphertext policy attribute based encryption helps to outsource data, which means encrypt the data at the data owners end and uploading it to third-party storage with some access policy. In normal Identity-based encryption, if a data owner wants to send information to a data user, it will be sent with some identity of the data user, such as mail id, so that only that particular user can read the message. The main problem is that the data owner should know each users identity. For instance, in some organizations where a data owner wants to send a message to a group of people with an identical designation, it can be sent with the help of the users attribute using attribute-based encryption. Here, the data owner does not need to know the specific details of each user; instead, with the help of attributes and the provided access policy, they can access this message. This research mainly focuses on three aspects of CP-ABE: access policy, number of attribute authorities, and revocation. When it comes to access policy, the currently existing access policies are not secure due to their linearity in nature because shares are always calculated using the same linear equation. So, for this problem, this work has developed a non-linear SS-secret-sharing model that increases the confidentiality of the model. 2024 Seventh Sense Research Group. -
Predicting and improvising the performance of rocket nozzle throat using machine learning algorithms
This paper is a study of one dimensional heat conduction with thermo physical properties like K, row, Cp of a material varying with temperature. The physical problem is characterized by a cylinder of infinite length and thickness L, imposed with a net heat flux at x= 0, with the other end being insulated. The temperatures at the insulate end are measured by placing thermocouples. As the temperatures at the other end are very high, it is not possible to measure temperatures by keeping thermocouples which will burn away. So the problem is initialized with known sensor values near insulated end. By proper predicting values by ARIMA Model, the temperature distribution in Rocket Nozzle throat system (RNT) is calculated. The outcome of the work is processed with Machine Learning algorithm like Genetic algorithm in identifying the optimal location of sensor position which helps in improvising the performance of RNT. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Statistical tests for key strength identification in cryptography
The cryptographic study involves three algorithms, one for Encryption of Plain text to Cipher text, one for Decryption for Cipher text back to Plain text and third for the generation of the Key. Key generation algorithm works on the principle of Randomness. In this work, the randomness of Key is studied by using Statistical methods like Runs Up & Runs Down test, Runs (Above and Below the mean), Chi Square test & Auto correlation test for its usability in Cryptographic study. 2020 IJSTR. -
Design of a new curve based cipher
This work aims to develop a model with curve based cryptographic scheme which supports Confidentiality and Authentication at low computing resources and equal security. T TARU PUBLICATIONS. -
Rethinking human values
Just as there are many cultures within the world, so also are there many practices, beliefs, myths, values, and traditions within each culture. These unique ways of being can often present challenging frames of reference that may prevent a whole perspective from being attained. This essay examines the contextual formation of culture and the fundamentals intricate to the search for universal values. An illumination is also provided upon some of the major and extreme forms of cultural practices that may pose difficulty in achieving such a goal. -
Phytochemical analysis and antioxidant activity of leaf extracts of some selected plants of the family acanthaceae
The present era of scientific research has witnessed an enumerable amount of evidences to showcase the immense potential of medicinal plants. In the present investigation, the phytochemical analysis of Phlogacanthus pubinervius T. Anderson., Adhatoda vasica (L.) Nees,Phlogacanthus thyrsiflorus Nees, Phlogacanthus curviflorus (Wall.) Nees, and Ruellia tuberosa L. was carried out for the different plants extracted with methanol. Analysis was carried out to estimate the quantity of phenols, carbohydrates, tannins, flavonoids and proteins. The antioxidant property of these plants were analysed using DPPH method. The concentration of the plant samples required to decrease the DPPH concentration by 50% was calculated by interpolation from linear regression analysis and denoted IC50 value (g/ml). The qualitative analysis showed the presence of alkaloids, tannins, saponins, proteins, carbohydrate and phenols in all the sample extracts. The highest amount of tannins and phenols was observed in P. thyrsiflorus. P. pubinervius (77.83%), A. vasica (74.81%), P. curviflorus (94.20%), and R. tuberosa (70.78%) which showed highest antioxidant activity of DPPH-scavenging at 150 g/ml of methanol extract. The high percent of scavenging activities of those plants add value to their medicinal properties. The presence of the high amount of phytochemical compounds suggests that the plants have high amount of medicinal compounds and can be extensively used to extract the natural compounds. Kripasana & Xavier (2020). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/). -
Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease
A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98 % accurate in terms of efficiency. 2024 -
Going beyond tomato a fruit or vegetable debate: economic and policy challenges in tomato farming in India
Learning outcomes: The case was an application of a market demand and supply mechanism and its impact on the products price and focus on the following objectives:??Analyze the vegetable market in India and the challenges faced by the farmers (tomatoes) using demand and supply concepts.??Examine the impact of price elasticity on the revenue of the farmers.??Assess the challenges faced by the government in controlling prices of vegetables and food inflation.??Evaluate diversification strategies in agriculture to mitigate risk. Case overview/synopsis: The market for tomatoes was highly cyclical because of erratic rainfall, and farmers went through a difficult time, especially when the prices fell below the cost of production. They moved out for crops that had stable prices. They expected government support for price stability. Government and policymakers considered price fluctuations a short-term phenomenon that required limited interventions when prices were high. This case was about Dilip, a farmer who was into farming tomatoes on a large scale in Karnataka, India. He was facing a dilemma as to whether he had to continue or move to other crops because of the low price of tomatoes in May 2023 or to diversify into some small but related business. He was worried at the same time, curious to understand the volatility in the prices of tomatoes, government responses, risks and returns associated with the cultivation of this crop and Agri-supply chain. Based on his understanding, he should make decisions to continue or diversify into some other farming or related business. Complexity academic level: This case was written for microeconomics and managerial economics of undergraduate and postgraduate students. This case demonstrates the application of the demand and supply mechanism for a perishable product such as tomatoes. Price fluctuations are common in these markets because of various uncontrollable factors such as rain, pests and natural calamities. The case could show the relationship between the firms elasticities and revenue. This case also highlights the policy constraints in controlling the prices in the short run. This case could also be used for understanding macroeconomic concepts such as food inflation and its impact on general price inflation. The students or target audience with a background in the functioning of the markets could very well relate to the concepts discussed. Supplementary material: Teaching notes are available for educators only. Subject Code: CSS: Entrepreneurship (3); Management Science (7). 2025, Emerald Publishing Limited. -
Screen Time to Severity: Machine Learning Models for Teen Smartphone Dependency Prediction
This study presents a systematic comparison of fourteen supervised classifiers trained to predict binned smartphone addiction levels (Low/Medium/High) in a cohort of 300 teenagers, using demographic, usage, academic, and health related features. After cleaning and binning the continuous Addiction_Level score into three categories, we encoded all categorical variables and standardized inputs, then stratified into 80 % training and 20 % test splits. Our expanded model suite comprised: Logistic Regression, Gaussian Naive Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Extra Trees, AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost, Support Vector Machine, and a multilayer perceptron (MLP). Each classifier was evaluated on accuracy, precision, recall, macro-averaged F1-score, and multiclass ROC AUC; confusion-matrix entries were flattened into nine 'Actual_i to Pred_j' columns per model for granular error analysis. Logistic Regression achieved the highest test accuracy (98.83%) , outstanding ROC AUC (0.9982) and perfect precision in discriminating the majority class ('High' addiction), despite modest recall for minority classes. MLP followed (96.33 % accuracy, 0.9878 AUC), indicating that a shallow neural network can capture nonlinear patterns but struggles on underrepresented labels. Gradient Boosting, CatBoost, and LightGBM all exceeded 95% accuracy with strong F1-scores (?0.72-0.73) and AUCs above 0.96, demonstrating the power of tree-based ensembles on mixed data types. Simpler methods (e.g., GaussianNB, KNN, Decision Tree) performed moderately (86-91% accuracy, AUC 0.84-0.98), while AdaBoost lagged (77.5 % accuracy, AUC 0.867), suggesting sensitivity to noisy features. Confusion-matrix summaries revealed that most models rarely misclassify Low-addiction teens, but confusion arises between Medium and High classes important for targeted interventions. 2025 IEEE. -
Enterococcus faecalis CGz3 alleviating steatosis via BSH-mediated modulation in HepG2 cell-lines
The study aimed to evaluate the therapeutic potential of bile salt hydrolase (BSH)-producing probiotic Enterococcus faecalis CGz3 in alleviating steatosis in HepG2 hepatocarcinoma cells, with non-alcoholic fatty liver disease (NAFLD) induced by cholesterol and oleic acid (OA), focusing on its effects on lipid accumulation, metabolic gene expression and, inflammatory pathways. HepG2 cells were treated with cholesterol and OA to induce lipid accumulation, mimicking non-alcoholic fatty liver disease (NAFLD) conditions. Cells were then incubated with E. faecalis CGz3 for 6 hours at 37C and 5% CO2. Lipid levels were quantified using Oil Red O staining and cholesterol uptake assays, while gene expression of lipogenic, inflammatory and metabolic markers was assessed via quantitative real-time polymerase chain reaction (qRT-PCR). Treatment with E. faecalis CGz3 significantly reduced lipid accumulation from 42.961.35 mg/mL in NAFLD-induced cells to 29.731.26 mg/mL. It down regulated lipogenic genes (SREBP-1c, FAS and ACC) and inflammatory markers (TNF-?, IL-6, CRP, TLR4, TLR9, NF-?B, JNK, ERK) while upregulating PPAR? and AMPK, promoting fatty acid oxidation. No significant cytotoxicity was observed at 6 hours, though prolonged exposure (1224 hours) reduced cell viability. This study introduces E. faecalis CGz3, a novel BSH-producing probiotic isolated from chicken gizzard, as a promising candidate for NAFLD intervention. Its selective modulation of lipid metabolism and inflammation via BSH activity offers a new perspective on probiotic-based therapies for NAFLD, warranting further in vivo and clinical exploration. 2025, World Researchers Associations. All rights reserved. -
The Persistence of Untouchability: Working Conditions of Dalit Journalists in India
This article can be viewed as an extension of the Oxfam report of 2019, which revealed that Indian news media is dominated by upper castes and the near absence of Dalit and Adivasi journalists. Using critical political economy as a framework, and undertaking qualitative interviews of self-identifying Dalit journalists, their conditions of work in mainstream news media are examined. In addition to the problems faced by journalists in general, this research reveals that Dalit journalists experience considerable psychological stress and extra intensity of work. They tolerate a toxic work environment that results in mental trauma and have to navigate rigid caste networks. Supplementing in-depth interviews with secondary data, the article argues that the conditions within which Dalit journalists function contain all dimensions of untouchability: exclusion, humiliation, and exploitation. The article concludes with a call to end this untouchability, revive the Working Journalists Act to ameliorate the conditions of work of Indias fourth estate. Specific legislation is required to ensure favorable conditions of work for Dalit journalists. Further, the article calls for a theoretical revamping of critical political economy to include caste, particularly when analyzing South Asian media. The Author 2024 -
Global iPhone Local Labour: Exploring ICT Production, Labour and Cultural Production
A theory of value pertinent to the contemporary iPhone era focuses on formal and informal labour circuits. This study extends this framework by examining a labour dispute in an iPhone factory near Bangalore, delving into its dissemination through media and the broader critical political economy surrounding the recent iPhone production in India. Furthermore, it incorporates a geographical perspective into the circuit framework to illustrate the movement of capital and labour in Bangalore, rekindling discussions on coreperiphery dynamics in the context of capital and labour migration. Further, this research builds upon the typography of worker-generated content by illustrating a specific category of such content within the iPhone labour dispute. Utilising a critical political economy of media approach, this article aims to assess the broader implications of the updated framework and to open new avenues for research within the emerging field of information communication technologies, cultural production and labour. 2024 South Asian University. -
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DIGITAL TWINBASED INTELLIGENT MONITORING OF INDUSTRIAL SYSTEMS USING EXPLAINABLE AI
Industrial systems increasingly rely on Industrial Internet of Things (IIoT) sensors for real-time monitoring and predictive maintenance. However, most existing digital twinbased monitoring solutions depend on static or black-box machine learning models, limiting interpretability, operator trust, and safe deployment in safety-critical environments. In response to these challenges, the author develops the Adaptive Hybrid Digital Twin with Causality-Aware Explainable Artificial Intelligence (HADT-C-XAI) framework to offer transparency and intelligence in industrial monitoring. The framework describes three integrated layers: (i) acquisition of real-time sensors, (ii) continually synchronized hybrid digital twin modeling, which is the integration of physics and data hybrid modeling and (iii) an intelligent analysis layer where LSTM-based anomaly detection is ungraded with explainable feature attribution. A closed-loop learning mechanism updates the model dynamically to adapt to operational drift while generating interpretable fault causes for operator decision support. Experiments were conducted on a multi-sensor industrial testbed containing 120 hours of vibration, temperature, acoustic, and rotational data. The implemented system shows a 94.8% detection accuracy, 95.4% recall, and a 4.1% low false alarm rate, which surpasses standard LSTM (88.5%) and threshold-based monitoring (82.9%). With edge-level inference, detection latency has been reduced to 26-30 ms, which allows for real-time deployment. Results demonstrate that integrating adaptive digital twins with explainable AI improves reliability, transparency, and fault diagnosis while maintaining computational efficiency. The proposed framework provides a scalable and trustworthy solution for predictive maintenance, Industry 4.0 applications, and cyberphysical system monitoring. 2025, Technical institute of Bijeljina. All rights reserved.