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Implementation of hybrid machine learning approach for intrusion detection system
The Intrusion Detection System (IDS) enforces information security and is responsible to identify attacks and vulnerabilities inside a network. It does this by analyzing the packet stream throughout the network. In traditional IDS systems, the analysis is done by looking for signatures of known attacks or deviations of normal activity as described by the rules provided for the IDS system. Machine learning helps in deriving predictive knowledge and this makes it ideal to apply Machine learning in an IDS system to detect attacks. This paper focuses on creating a hybrid model that is best to implement in an IDS system. A hybrid model is implemented which combine multiple machine learning algorithms using Ensemble method. The experiments include evaluating machine learning algorithms such as Decision Tree, MLP (Multi-Layer Perceptron), Gradient Boosting etc. The algorithms with the best results are taken to construct Hybrid model. This Hybrid approach will improve the accuracy and efficiency for identifying the attacks by the IDS system. Depending on the type of attack, the IDS system can classify packets as DoS (Denial of Service), Probe, R2L (Root to Local), U2R (User to Root) or Normal. The experiments are carried using NSL-KDD Dataset. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
AI and IoT for universal health and well-being across generations
Over the last several years, the confluence of AI and the Internet of Things (IoT) has caused tremendous changes in many areas of our life, including the healthcare industry. Because of this cooperation, new possibilities have emerged with the aim of enhancing the health and welfare of people across all different generations. The ability to efficiently gather, analyze, and derive insights from large volumes of real-time data has revolutionized healthcare, allowing for better patient treatment and community health management. This is made feasible by combining algorithms powered by artificial intelligence with IoT-connected devices. Examining the gamechanging possibilities of AI and the IoT in the healthcare industry is the goal of this introductory piece. The function of AI and the Internet of Things in advancing health equity and wellness across diverse age groups is the primary emphasis of this study. Countless and varied uses of AI and the internet of things may be found in the medical field. Some examples of these uses include remote patient monitoring and the development of predictive analytics tools for use in illness prevention.Health outcomes and quality of life for individuals of all ages can be improved via the development of individualized therapies and treatment programs that cater to each person's specific needs. It is feasible to create these opportunities with the help of these technologies. Healthcare issues may be effectively addressed in a variety of locations, from densely populated cities to more rural places, by implementing solutions that leverage the internet of things and artificial intelligence. Because these solutions are both accessible and scalable, this is the result. It is possible for healthcare systems to overcome barriers to service delivery and access by utilizing these technologies. As a result, people of all ages and from all over the world will be able to live the kind of healthy, fulfilling lives they deserve. 2024, IGI Global. All rights reserved. -
Visiting Indian Hospitals Before, during and after COVID
The prevailing COVID-19 situation has brought in temporary and permanent changes in the attitude and lifestyle of people. Starting from Hand sanitizers and face masks, it extends to online classrooms and work from home culture. In case of visiting hospitals and medications, people with pre-existing medical conditions and minor health issues tend to delay or avoid visiting hospitals due to fear of infection, which is dangerous. Further, people or patients tend to access several alternatives and precautions. The alternatives include home remedies, ayurvedic medication, yoga and meditation. On the other hand, hospitals are trying to adapt online consulting and telemedicine. Besides, Cancellation or delay of nonemergency surgeries became inevitable in the lockdown phase. This survey conducted among the people of Erode district, Tamilnadu to study the perception of people concerning visiting hospitals for health issues. The results show that fear of infection, financial and transportation difficulties are the major factors which affected people from visiting hospital. Also, changing trends like Telemedicine and home remedies are likely to be permanently opted by people. In Brief, the outcomes reveal the changing attitude of people towards medication and hospital visiting habits. 2022 World Scientific Publishing Company. -
Let there be Light, but not too much: The Need to Legally Address Light Pollution in India
Electricity and artificial lights were synonymous with economic growth and development. Unfortunately, over usage of artificial lights has proven adverse effects. Research shows that excessive light impacts human health and endangers ecological balance, disturbs wildlife, causes decline in insect, moth, reptile pollution and depletes energy resources. Countries around the world have gradually started recognising light pollution as an emerging challenge and have brought in regulations to curb it. However, India is yet to recognise the threat of light pollution. Against this backdrop, the authors have established the need to recognise light pollution as a matter requiring dedicated and concerted focus. This was achieved through the analysis of recent and credible journal articles category with a cite score of over ten. Reliance was also placed on the light pollution map to understand the intensity of the problem, especially in India. The authors next conducted a study of legal regimes governing light pollution and artificial light, in different jurisdictions around the globe. The paper draws upon the best practices from these jurisdictions and suggests that India adopt techno-legal legislation, at the earliest, to combat light pollution. 2023- Kalpana Corporation. -
Growth Of ZnSnN2 Semiconductor Films For Gas Sensor Applications
ZnSnN2 is a member of class of nitride semiconductors which have the additional benefits of earth abundance and non-toxicity. For device applications, NH3 gas detecting senor, which finds use in chemical, pharmaceutical, and food process industries, are fabricated with zinc-tin-nitride (ZnSnN2) thin-films on glass substrate by making use of metal as contacts. The ZnSnN2 sensor is found extremely selective to ammonia (NH3) amongst other gases like ethanol, NO2, H2S and exhibited good detecting responses at room temperature. There are many ways to develop thin films of ZnSnN2, and hence in this work we are trying to find a cost effective, feasible and easier method of synthesis, i.e., chemical vapor deposition method. The first step was the optimization of process parameters to grow Zinc-Tin (ZnSn) thin-films. Later, optimization of the process parameters for the growth of compound ZnSnN2 was completed. The grown films are characterized by material quality using X-Ray Diffraction and UV- Vis spectroscopy. 2022 American Institute of Physics Inc.. All rights reserved. -
Does environmental reporting ofbanks affect their financial performance? Evidence from India
Purpose: The present study aims to investigate the effect of environmental reporting on the financial performance of banks in India. Design/methodology/approach: The study is based on the secondary data. The sample includes the banks listed in the NSE Nifty Bank Index from 20162017 to 20202021. The environmental reporting data was obtained through the content analysis technique. The financial data was collected from the CMIE Prowess database. Panel regression analysis was used to analyse the data. Findings: The findings indicate a negative significant influence of environmental reporting on the ROA and ROE of banks. On the other hand, environmental reporting does not significantly influence the EPS of banking institutions. Originality/value: To the best of the authors knowledge, this study is the first to contribute to the scarce literature on the influence of environmental reporting on financial performance, pertinently in the context of a developing nation's banking sector. 2023, Emerald Publishing Limited. -
Do Bank Characteristics Really Matter for its Environmental Reporting?
The last few decades have seen an increasing number of researches in the area of environmental reporting. Institutions across the globe have been extensively reporting their environmental initiatives through their annual reports. There is a dearth of research on environmental reporting in the Indian context. Thus, this study comprehensively analyzes the environmental reporting practices of scheduled banks in India. It further attempts to explore the association between environmental reporting and bank characteristics. The secondary data is collected from the annual reports, sustainability reports, and business responsibility reports for the period 2017-2022. The sample consists of ten top-rated commercial banks chosen based on market capitalization during June 2022. The content analysis technique is used to extract information on environmental practices under twelve major categories. This study employs correlation analysis to examine the association between environmental reporting and bank characteristics. The findings of this study reveal that Indian commercial banks are increasingly reporting environmental information in their bank reports and websites. Further, the results of correlation analysis revealed a significant association between environmental reporting and the banks' age, size, and profitability. Further, this study recommends policymakers and concerned professional bodies introduce additional environmental guidelines and widen the scope of reporting in the banking industry. 2024 National Institute of Science Communication and Policy Research. All rights reserved. -
The linkage between green banking practices and green loyalty: A customer perspective
The aim of this study is to explore the bank customers perceptions towards green banking practices. This study uses a convenient sampling method. Pre-tested questionnaires were employed to collect data. The data were collected conveniently from 358 bank customers. However, the final sample includes 304 responses after ignoring null responses (n = 304). The Structural equation modeling (SEM) was applied for the analyses. The significant results of the study indicate that green banking practices positively influence green image (p = 0.001) and green trust (p = 0.025), while it does not significantly affect green loyalty (p = 0.642). The mediation analysis reveals that green image mediates the relationship between green banking practices and green loyalty, while green trust does not mediate the relationship between the same. The results have practical implications for banking institutions in India to recognize the importance of environmental initiatives in influencing the decisions of bank customers. Deepthi S. Pawar, Jothi Munuswamy, 2022. -
Machine Learning Methods to Identify Aggressive Behavior in Social Media
With the more usage of Internet and online social media, platforms creep with lot of cybercrimes. Texts in the online platforms and chat rooms are aggressive. In few instances, people target and humiliate them with the text. It affects victim mental health. Therefore, there is a need of detecting the abuse words in the text. In this paper, a study of machine learning methods is done to identify the aggressive behavior. Accuracy can be improved by incorporating additional features. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Evidence Acquisition in Social Media for Cyber Crime
Social Media forensics a branch of forensics involves in collecting the evidences for the cyber crime. Investigating social media is a complex process which involves the privacy issues for accessing the users, suspects and victims information on social media. Manual processing of social media data is not feasible as it contains large volumes of data. An automated process is needed to incident specification, evidence extraction and for provenance. The need for handling heterogeneity of data as users have accounts with multiple social websites is also explained. This study briefs the existing models and the challenges faced in analyzing with those models. The research goals in this field are also addressed. A pool of tools which can contribute in guarding the solution for cyber crime is also presented. 2022 IEEE. -
An AI-Based Forensic Model for Online Social Networks
With the growth of social media usage, social media crimes are also creeping sprightly. Investigation of such crimes involves the thorough examination of data like user, activity, network, and content. Although investigating social media looks quite straight forward process, it is always challenging for the investigators due to the complex process involved in it. Due to the immense growth of social media content, manual processing of data for investigation is not possible. Most of the works from this area provide an automatic model or semi-automated, and much of the contributions lacks the logical reasoning and explainability of the evidence extracted. Searching techniques like entity-based search and explainable AI add value to the quick retrieval within appropriate scope and explain the results to the court of law. This paper provides a model by adding these new techniques to the basic forensic process. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Explainable AI Method for Cyber bullying Detection
People of all ages and genders are using social media platforms to engage themselves in all sorts of activities. People create profiles on online social networks in order to communicate with one another in this virtual environment. Hundreds or thousands of friends and followers are split across many profiles. Along with the virtual communication in this social media life, cyber-crimes also creep in many distinguished forms to grab user's information and emotionally degrade them with harassment and arrogant behavior. A set of machine learning methods are proposed and used to detect such a bullying behavior. Along with the detection of such an act, the model should also provide the logical reasoning of the evidence extracted. The explain ability of the models classification will give us a view of the way towards portraying a suspect as a bullier. This paper illustrates a machine learning model that works on a twitter data set to suggest the tweets as category bullying or non-bullying. LIME a tool to predict the interpretability of the model is used to depict the performance of model and provides explainability. 2022 IEEE. -
Impact of knowledge management on organizational performance: An application of structural equation modeling
Purpose: The purpose of this paper is to explore the dynamic relationships among the essential knowledge management (KM) constructs, i.e. strategy, enablers and processes, and to establish their links to organizational performance using a holistic integrated model. Design/methodology/approach: The structural equation modeling approach was used in the research study. The primary data were collected from IT managers in Indian software firms. Findings: The study successfully tested an integrated KM model in an Indian scenario. The study found that the KM strategy, enablers and processes had a significant positive relationship with the organizational performance. An appropriately designed KM strategy significantly influenced the KM enablers and KM process. KM enablers nurtured in an organization positively impacted the KM process. Furthermore, the KM process partially mediated the relationship between the KM strategy and organizational performance, and partially mediated the relationship between KM enablers and organizational performance. Originality/value: This study is one of the few to empirically establish how the essential KM constructs of strategy, enablers and processes together impact organizational performance. 2019, Emerald Publishing Limited. -
Physicochemical Properties, Chemical Composition and Antioxidant Activities of Artemisia pallens Wall. Seed Oil
The physico-chemical characteristics, fatty acid composition and antioxidant capacities of Artemisia pallens seed oil were determined in this study. The moisture, oil content, fatty acid, iodine, peroxide, saponification values, specific gravity and refractive index were 4.13 %, 25.53 %, 1.84 % (as oleic acid), 138.14 (mg/ l00 g), 10.20 (meqO2/kg oil), 194.21 (mg KOH/g oil) 0.92 and 1.47, respectively. Linoleic acid (C18:2, 79.963 %), oleic acid (C18:1, 9.40 %) and palmitic acid (C16:0, 7.89 %) were the major fatty acids. High amount of total unsaturated fatty acids (89.74 %) make it highly desirable as a source of nutrition. The antioxidant capacities of the seed oil showed that it can be a source for natural antioxidants and functional food. The results of the present study showed that the A. pallens is a promising seed oil crop and can be used for making of soaps, shampoos and alkyd resins. Further, the high amount of polyunsaturated fatty acid i.e. linoleic acid makes it desirable in terms of nutrition. 2019, 2019 Har Krishan Bhalla & Sons. -
Ceramic-Polymer-Carbon Composite Coating on the Truncated Octahedron-Shaped LNMO Cathode for High Capacity and Extended Cycling in High-Voltage Lithium-Ion Batteries
Long-term electrochemical cycle life of the LiNi0.5Mn1.5O4 (LNMO) cathode with liquid electrolytes (LEs) and the inadequate knowledge of the cell failure mechanism are the eloquent Achilles heel to practical applications despite their large promise to lower the cost of lithium-ion batteries (LIBs). Herein, a strategy for engineering the cathode-LE interface is presented to enhance the cycle life of LIBs. The direct contact between cathode-active particles and LE is controlled by encasing sol-gel-synthesized truncated octahedron-shaped LNMO particles by an ion-electron-conductive (ambipolar) hybrid ceramic-polymer electrolyte (IECHP) via a simple slot-die coating. The IECHP-coated LNMO cathode demonstrated negligible capacity fading in 250 cycles and a capacity retention of ?90% after 1000 charge-discharge cycles, significantly exceeding that of the uncoated LNMO cathode (a capacity retention of ?57% after 980 cycles) in 1 M LiPF6 in EC:DMC at 1 C rate. The difference in stability between the two types of cathodes after cycling is examined by focused ion beam scanning electron microscopy and time-of-flight secondary ion mass spectrometry. These studies revealed that the pristine LNMO produces an inactive layer on the cathode surface, reducing ionic transport between the cathode and the electrolyte and increasing the interface resistance. The IECHP coating successfully overcomes these limitations. Therefore, the present work underlines the adaptability of IECHP-coated LNMO as a high-voltage cathode material in a 1 M LiPF6 electrolyte for prolonged use. The proposed strategy is simple and affordable for commercial applications. 2024 The Authors. Published by American Chemical Society. -
Miniaturized Band Stop Frequency Selective Surface for Stable Resonance Characteristics
In this paper, miniaturized 7.45 GHz resonant frequency band stop frequency selective surface (FSS) is designed. The unit cell dimensions of designed FSS is only about 0.1?0 at the 7.45 GHz. Proposed design involves a crossed dipole metallic element together with meander shape on the substrate. Simulation results provide about 800 MHz bandwidth (7.1 GHz-7.9 GHz) with-20 dB insertion loss. The FSS properties are studied on a unit cell using electromagnetic (EM) solver to observe the characteristics. Proposed FSS demonstrates a stable resonance frequency behavior for the arbitrary angle of incidences in both the polarizations such as TM and TE modes. Thus, the design holds a polarization independent characteristic for all incident angles and polarizations. Finally, the FSS properties are validated by a fabricated array of 311 mm2. 2018 IEEE. -
Understanding Agape Leadership: A Scoping Review
Agape, a Greek term for unconditional love, is often overlooked in the context of leadership, which has traditionally emphasized control and dominance. Agape leadership is a type of leadership that places the well-being and growth of individuals, communities, and society at the forefront. This style of leadership is characterized by a profound sense of compassion and consideration for others and is grounded in the principles of love, compassion, and empathy. This paper reviews the literature on agape leadership, exploring how it is conceptualized, practiced, and studied. The review finds that agape leadership is linked to favourable results, including increased trust, motivation, performance, respect, and collaboration, in which people can thrive and reach their full potential. This style of leadership is not only focused on achieving specific goals or outcomes, but also on fostering a sense of purpose, meaning, and fulfilment in the lives of those being led. The leadership style of agape can bring about enduring and constructive transformations in society as it motivates and galvanizes individuals to collaborate toward a collective aspiration of a more promising tomorrow. Agape leaders also place a high value on personal growth and development and are constantly seeking to learn and grow in their own leadership journey. 2023 Open Access/Author/s - Online @ http//: www.pharosjot.com -
Eye-Tracking Measures in Aviation: A Selective Literature Review
Objective: The aim of this article is to present a comprehensive review of eye-tracking measures and discuss different application areas of the method of eye tracking in the field of aviation. Background: Psychophysiological measures such as eye tracking in pilots are useful for detecting fatigue or high-workload conditions, for investigating motion sickness and hypoxia, or for assessing display improvements and expertise. Method: We review the uses of eye tracking on pilots and include eye-tracking studies published in aviation journals, with both a historical and contemporary view. We include 79 papers and assign the results to the following three categories: Human performance, aircraft design, health and physiological factors affecting performance. We then summarize the different uses of eye tracking in each category and highlight metrics which turned out to be useful in each area. Our review is complementary to that of Ziv (2016). Results: On the basis of these analyses, we propose useful application areas for the measurement of eye tracking. Eye tracking has the potential to be effective in terms of preventing errors or injuries by detecting, for example, fatigue or performance decrements. Applied in an appropriate manner in simulated or real flight it can help to ensure optimal functioning of manmachine systems. Conclusion: Further aviation psychology and aerospace medicine research will benefit from measurement of eye movements. 2018, 2018 The Author(s). Published with license by Taylor and Francis Group, LLC. -
Chernobyl Disaster Optimizer-Based Optimal Integration of Hybrid Photovoltaic Systems and Network Reconfiguration for Reliable and Quality Power Supply to Nuclear Research Reactors
In view of the complexity and importance of nuclear research reactor (NRR) installations, it is imperative to uphold high standards of reliability and quality in the electricity being supplied to them. In this paper, the performance of low-voltage (LV) distribution feeders integrated with NRRs is improved in terms of reduced distribution loss, improved voltage profile, and reduced greenhouse gas (GHG) emissions by determining the optimal location and size of photovoltaic (PV) systems. In the second stage, the power quality of the feeder is optimized by reducing the total harmonic distortion (THD) by optimally allocating D-STATCOM units. In the third and fourth stages, the reliability and resilience aspects of the feeder are optimized using optimal network reconfiguration (ONR) and by integrating an energy storage system (ESS). To solve the non-linear complex optimization problems at all these stages, an efficient meta-heuristic Chernobyl disaster optimizer (CDO) is proposed. Simulations are performed on a modified IEEE 33-bus feeder considering the non-linear characteristics of NRRs, variability of the feeder loading profile, and PV variability. The study reveals that the proposed methodology can significantly improve the service requirements of NRRs for attaining sustainable research activities. 2024 by the authors. -
Machine Learning in Financial Distress: A Scoping Review
Predicting financial distress is crucial for stakeholders, policymakers, governments, and management in decision-making processes. Researchers have developed various prediction models encompassing both traditional and machine-learning approaches. Notably, recent attention has shifted towards employing machine learning models to address the limitations of traditional methods. This study seeks to offer insights into current trends, identify gaps, and suggest future research directions using machine learning models for financial distress prediction, employing the PRISMA Extension for Scoping Reviews methodology. To achieve this, a comprehensive search was conducted across three databasesScience Direct, EBSCO, and ProQuestspanning from 2020 to 2023, identifying 34 relevant articles for analysis. The findings underscore the prevalent use of Support Vector Machine in financial distress prediction, followed by the Random Forest Classifier and Artificial Neural Network, with little attention paid to other models. Furthermore, the study underscores the necessity for more research in developing countries, noting the predominance of studies from developed nations. While machine learning models hold promise for enhancing the accuracy and efficiency of financial distress prediction, additional research is imperative to evaluate their effectiveness and applicability across diverse contexts. This scoping review aims to furnish researchers, policymakers, and institutions with valuable insights and policy recommendations, shedding light on underexplored machine-learning techniques. 2024, Iquz Galaxy Publisher. All rights reserved.