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Artificial immune system based frameworks and its application in cyber immune system: A comprehensive review
Computer science has always mixed the concepts of biology and computers to enhance the way in which systems are designed. Artificial Immune System (AIS) is a Computational Intelligence strategy dependent on an organically enlivened computational system that can be utilized for taking care of complex computational issues. It tends to be seen that AIS is an incredibly various locale of research, going from the modeling immune systems to complex algorithms for specific applications. This paper exhibits an exhaustive survey of different frameworks developed in the artificial immune system and its application. Reviews of frameworks in AIS are uncommon and henceforth this paper gives an inside out audit of progressing research and challenges in AIS. We start by presenting AIS and give a thorough survey of different systems in AIS and its application in anomaly detection. We investigate the utilization of AIS in the Intrusion Detection System named the Cyber Immune System(CIS) and compares various AIS works applied to CIS. We conclude with various future extensions in the area of AIS research. 2019 by Advance Scientific Research. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) -
Enhanced AIS Based Intrusion Detection System Using Natural Killer Cells
Intrusion detection system is used to monitor the system and network activities to identify anomalies and attacks so that integrity, availability, and confidentiality can be preserved. Here an intrusion detection system based on Artificial Immune System is proposed based on Natural Killer (NK) cells with immunological memory. NK cells are created and each NK cells detection radius is determined using the negative selection algorithm and is trained to detect various attacks. Effective cells with high fairness values are proliferated and distributed to the network using clonal selection algorithm. In this paper, two types of NK cell are used-a Heavyweight NK cell (HWNK) and a number of Lightweight NK cells (LWNK). The incoming data is vectorized and Major Histocompatibility Complex Class I (MHC1) is created. Then based on this MHC1, any of the receptors i.e. Activating Receptor or Inhibiting Receptor is activated. If it is the signature of an attack, Activating Receptor is activated. Activating receptor activation results in either cytokine release or apoptosis. Here cytokine release means an alarm is generated informing the administrator and apoptosis stands for dropping of the packet. If Inhibiting Receptor is activated, it's a normal packet there is no action taken. The technique proposed yields high accuracy, better detection rate and quick response time. 2020 River Publishers. All Rights Reserved. -
A generic cyber immune framework for anomaly detection using artificial immune systems
Intrusion detection systems play a significant role in computer security. Artificial immune systems are the prime contender in developing an anomaly-based intrusion detection system due to their simplicity. The fundamental goal of this paper is to create a generic framework for an artificial immune system which is fast and accurate in detecting anomalies using artificial immune system concepts. Natural killer cells in the immune system and their quick response to foreign pathogens inspired the adaptation of those cells into an artificial immune system based framework. A natural killer cell-based framework is proposed to improve the accuracy and speed of anomaly detection. The structure of the proposed framework includes major histocompatibility complex class 1 representation, affinity calculation, cell generation, and cell proliferation. This framework addresses the overlapping and hole problem while creating natural killer cells to increase the system's performance. The negative selection algorithm and the positive selection algorithm generate the cells that enhance the anomaly detection technique and give high precision. The parameter response time introduced in this paper is crucial for an intrusion system to be used in real-time. 2022 Elsevier B.V. -
Representation of Cancer in the Digital Space
[No abstract available] -
Platt number of total graphs
The degree of an edge uv is defined as the number of edges incident on vertices u and v other than itself. The Platt number of a graph is the sum of degrees of all its edges. In this paper, the concept of degree of an edge is analysed in social networks. The Platt number is investigated in certain classes of graphs and their total graphs. Also related bounds are proposed on connected graphs. An algorithm developed to determine the Platt number of any connected graph is presented. 2018 Academic Publications. -
Advancing Intrusion Detection Using Deep Learning: A Hybrid Approach
Intrusion detection systems (IDSs) are vital for securing networks against evolving cyberthreats. Traditional machine learning models often struggle with complex network traffic and imbalanced attack patterns. This study proposes an advanced ensemble model integrating ANN, LSTM, random forest, and LightGBM to enhance detection accuracy and robustness. Evaluations on the KDD99 dataset demonstrate that the ensemble model outperforms standalone ANN-LSTM models, achieving 92.4% accuracy, 97.4% precision, 87.1% recall, and a 91.9% F1 score. Hybrid models also showed significant improvements, with Nadam optimization yielding an F1 score of 93.10% for ANN-LSTM-random forest and Adam optimization achieving 93.30% for ANN-LSTM-LightGBM. By addressing data imbalance and improving attack pattern detection, this approach provides a scalable, efficient solution for real-time intrusion detection with superior performance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
GASP. XV. A MUSE view of extreme ram-pressure stripping along the line of sight: Physical properties of the jellyfish galaxy JO201
We present a study of the physical properties of JO201, a unique disc galaxy with extended tails undergoing extreme ram-pressure stripping (RPS) as it moves through the massive cluster Abell 85 at supersonic speeds mostly along the line of sight. JO201 was observed with multi-unit spectroscopic explorer as part of the GASP programme. In a previous paper (GASP II) we studied the stellar and gas kinematics. In this paper we present emission-line ratios, gas-phase metallicities, and ages of the stellar populations across the galaxy disc and tails. We find that while the emission at the core of the galaxy is dominated by an active galactic nucleus (AGN), the disc is composed of star-forming knots surrounded by excited diffuse gas. The collection of star-forming knots presents a metallicity gradient steadily decreasing from the centre of the galaxy outwards, and the ages of the stars across the galaxy show that the tails formed ? 109 yr ago. This result is consistent with an estimate of the stripping time-scale (?1 Gyr), obtained from a toy orbital model. Overall, our results independently and consistently support a scenario in which a recent or ongoing event of intense RPS acting from the outer disc inwards, causes removal and compression of gas, thus altering the AGN and star formation activity within and around the galaxy. 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Prevalence and Predictors of Restless Leg Syndrome in Adolescents and Young Adults of Bengaluru City, India: A Cross-Sectional Study
INTRODUCTION: Restless leg syndrome (RLS) is a sensorimotor disease characterized by an urge to move the legs, often caused by uncomfortable and unpleasant sensations in the legs. It affects the quality of sleep which in turn affects scholastic performance in children and predisposes them to cardiovascular diseases in the long run. Hence, the primary aim of this study was to assess the prevalence and predictors of RLS, poor sleep quality, and excessive daytime sleepiness (EDS). METHODS: This was a cross-sectional observational study conducted between September 2017 and March 2020 in Bengaluru, India, including all consenting PreUniversity College, Degree College, and Higher Secondary school students. After parental consent and assent (if applicable) was obtained, a semi-structured standardized pilot-tested questionnaire consisting of the RLS diagnostic criteria, Pittsburgh Sleep Quality Index (PSQI), Epworth sleepiness scale, and questions on sleep hygiene was administered. The prevalence was expressed as proportions and 95% confidence intervals (95% CI). Regression analysis was done to determine the predictors. RESULTS: The overall prevalence (95% CI; frequency) of students with RLS, poor sleep quality, and EDS in our study population was 8.36% (7.54, 9.24; n = 1,544/4,211), 36.67% (35.21, 38.14; n = 1,544/4,211), and 39.87% (38.39, 41.37; n = 1,679/4,211), respectively. PSQI and Epworth score were the significant predictors of RLS. Age, Epworth score, knowledge score, and the number of unacceptable sleep habits were the significant predictors of sleep quality. Female gender, PSQI, RLS, knowledge score, and the number of unacceptable sleep habits were the significant predictors of EDS. CONCLUSIONS: The prevalence of RLS, those with poor sleep quality and EDS among adolescents and young adults was higher when compared to the historical data of general population in the same city. 2022 S. Karger AG, Basel. -
The Prevalence of Alcohol and Tobacco Use Among Adolescents aYoung Adults in Bengaluru, India
Background: Substance use can affect scholastic performance. The present study aimed to estimate the prevalence of alcohol and tobacco use and associated outcomes of their use among adolescents and young adults. Methods: We carried out a cross-sectional study in Bengaluru, India, over the period of September 2017 to September 2021. The study participants included students enrolled in pre-university courses, undergraduate colleges, and higher secondary classes (11th and 12th grades). Upon obtaining consent/assent, the study participants completed a pre-tested semi-structured questionnaire covering basic demographics and history of alcohol and tobacco use and their associated characteristics. Data were summarized using frequencies, medians (IQR), and proportions with 95% CI; sex-wise differences were assessed using Chi-square test and odds ratios, with a significance level set at P<0.05. Results: A total number of 4093 students participated in the study including 54.41% (n=2227) male and 45.59% (n=1866) female students. Lifetime alcohol use was reported by 33.33% (95% CI: 31.8834.79; n=1364) and tobacco use by 17.84% (95% CI: 16.6719.04; n=730) of the study participants. Curiosity was the most commonly reported reason for initiating alcohol (55.2%) and tobacco use (48.8%). Among alcohol consumers, 12.4% reported having sought help to quit, with no significant sex-wise difference (OR 1.234; 95% CI 0.8861.719; P=0.213). In contrast, 34.2% of tobacco users reported having sought help to quit, which was significantly more common in men than women (OR 1.483; 95% CI 1.0802.037; P=0.015). Conclusions: Alcohol and tobacco use appeared more common in Indian adolescents in our study compared with previously reported rates in the countrys general population. Therefore, appropriate interventions should be planned in early school/college days by the concerned authorities. 2026, International Journal of School Health. -
Improved File Security System Using Multiple Image Steganography
Steganography is the process of hiding a secret message within an ordinary message extracting it at its destination. Image steganography is one of the most common and secure forms of steganography available today. Traditional steganography techniques use a single cover image to embed the secret data which has few security shortcomings. Therefore, batch steganography has been adopted which stores data on multiple images. In this paper, a novel approach is proposed for slicing the secret data and storing it on multiple cover images. In addition, retrieval of this secret data from the cover images on the destination side has also been discussed. The data slicing ensures secure transmission of the vital data making it merely impossible for the intruder to decrypt the data without the encrypting details. 2019 IEEE. -
Framework for proactive visualization of text based narrative using NLP
Language is an essential mode, not only for human communicationbut also for thinking. A story is conveyed or a report of an incident is being told, humans perceive the conveyed information in the form of visual insights. The increasing advancements in the field of artificial intelligence can help with the same in machines. This paper reflects on the internalization of stories from a cognitive perspective and outlines a scalable framework for supporting the visualization of narrative text data. This paper leverages natural language processing (NLP), probabilistic modelling of discourse knowledge, information extraction of narrative components (who, where, when, what) and the narrative visualization. The graphics knowledge base storage structure has been redesigned to obviate the necessity of having a larger database for all graphics entity. With the developed framework, any user can input unrestricted natural language for the dynamic generation of animated scenes. This provides users with direct visual output in response to their natural language input. This tool can potentially impact the way humans interact with computers and expand a completely new way of understanding conversations. 2020 IJSTR. -
Barriers to Green Supply Chain Management in the Construction IndustryA Systematic Review
The construction industry is project-based and requires the cooperation of several stakeholders, mainly architects, contractors, and suppliers, to ensure that materials, finance and information move through the construction supply chain with minimal hurdles. The fragmented nature of the industry creates obstacles to integrating green practices into the supply chain to reduce the industrys negative environmental effects. This study aims to review the literature on barriers to adopting green supply chain management practices in the construction industry, pinpoint research gaps and suggest directions for further research in the domain. The articles for the literature review were retrieved from the Scopus database from 2014 to 2024. The search was refined using PRISMA guidelines. 18 relevant empirical studies were reviewed for this purpose. The findings reveal that the major impediments were high costs of implementing green practices across different phases, namely, the design procurement, construction, operation and demolition phases, inadequate knowledge and awareness of green procurement, design and construction, insufficient technical expertise, lack of government incentives for adopting green practices, lack of availability of green building materials and technologies, lack of top management commitment and inadequate policies and regulations on green construction. Extant literature indicates that past studies have examined the most significant barriers in developed and developing countries, mostly using quantitative surveys. It is suggested that future researchers conduct an in-depth analysis of the barriers in different contexts by gathering qualitative data from the construction industry stakeholders. This would support the creation of policies and strategies by practitioners and policymakers to address the issues of incorporating environmental concerns into the building supply chain. It will, therefore, encourage the sector to strive for environmental sustainability. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Barriers to Sustainable Practices in the Construction IndustryA Bibliometric Analysis and Thematic Classification
Several studies explored the challenges involved in adopting sustainable practices in the construction industry from the perspective of different stakeholders, mainly developers, architects, consultants, and contractors. These challenges include financial constraints, lack of awareness, human resource issues, government policies, and market dynamics. However, a comprehensive bibliometric analysis and thematic classification of these barriers covering different aspects of sustainable construction is scarce owing to the fragmented nature of the literature. The objectives of the study are(1). To examine the literature on barriers to incorporating sustainable practices in building construction, operation and demolition (2). To provide a thematic classification of the barriers (3). To identify research gaps and suggest avenues for further research. The articles were retrieved from the Scopus database and refined using PRISMA guidelines. 221 studies were included in the bibliometric analysis. Biblioshiny was used to identify the publication trends, most relevant countries, authors, publications, and highly cited articles. Furthermore, 30 empirical studies were analysed using NVivo 12 software to classify the barriers into the following themes: finance-related, attitude and behaviour-related, knowledge and awareness-related, government-related and market-related. The sub-themes cover various issues related to the development of sustainable buildings: prefabrication, Building Information Modelling, Cloud Computing, procurement of sustainable materials, energy management, managing sustainable projects and construction and demolition waste management. These comprehensive insights could help practitioners and policy-makers develop strategies to drive the construction industry towards achieving its sustainability goals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Parametric investigation on the tool wear while performing face milling on inconel 718 using round insert
Machining of Inconel 718 has become a material of great purpose in the machining industry. Since Inconel is considered to be a super alloy which possess high material property such as high thermal fatigue, high strength and high resistance to corrosion it is thus considered to be a material which is hard to machine. This paper gives an overview on the tool wear that takes place while machining. Machining was carried out in a CNC milling machine with the help of a tungsten carbide circular insert. The cutting parameters taken into consideration were cutting speed, feed rate and depth of cut. Tool wear was analysed with the help of tool makers microscope. Statistical analysis was done on the MINITAB 18 software by using regression analysis. The regression analysis was carried out by using Response Surface Methodology (RSM) the mathematical model for each individual response has been developed from regression equations considering analysis of cutting parameters as independent variables which was found to be significantly accurate. BEIESP. -
Smart Phone based Fundus Imaging for Diabetic Retinopathy Detection
INTRODUCTION: Diabetic retinopathy (DR) is one of the consequences of diabetes which if untreated may lead to loss of vision. Generally, for DR detection, retinal images are obtained using a traditional fundus camera. A recent trend in the acquisition of eye fundus images is the usage of smartphones to acquire images. OBJECTIVES: This paper focuses on the study of existing works which incorporated smartphones for obtaining fundus images and various devices available in the market. Also, the common datasets used for carrying out DR detection using smartphone-based fundus images as well as the classification models used for the diagnosis of DR are explored. METHODS: A search of information was carried out on articles based on DR detection from fundus images published in the state-of-the-art literatures. RESULTS: Majority of the works uses SBFI devices like 20D lens, EyeExaminer etc. to obtain fundus image. The common databases used for the study are EyePACS, Messidor, etc. and the classification models mostly rely on deep learning frameworks. CONCLUSION: The use of smartphones for capturing fundus images for DR detection are explored. Smartphone devices, datasets used for the study and currently available classification models for SBFI based DR detection are discussed in detail. This paper portrays various approaches currently being employed in SBFI based DR detection. 2023 A. Benjamin et al. -
Polyurethane nanocomposites for food packaging applications
Progress in food packaging technology improves modern food trade by simplifying transportation and sales, while offering enhanced protection. Traditional food packaging uses petroleum-based and nonbiodegradable packaging materials, leading to severe environmental and health-related issues. Polymer nanocomposites can reduce traditional plastic consumption and achieve a high recycling efficiency while maintaining the desired barrier and mechanical properties. Polyurethane, a polymer with good mechanical properties, is characterized by low pollution, easy modification, and environmental protection. Innovations in its structure and functionality through the incorporation of nanoscale fillers have led to enhanced barrier properties, thermal stability, and biodegradability. Polyurethane nanocomposites (PUNCs) exhibit multifunctional properties owing to the synergistic effect of polymers and nanofillers, and are used to increase the shelf life of food products, reducing food waste and foodborne illness, thereby contributing to sustainable development goals. This chapter begins by elucidating the fundamental properties of PU and the significance of various nanocomposites in tailoring these properties to fit packaging requirements. This chapter also describes the role of PUNCs in extending the shelf life of food, providing ultraviolet light protection, barrier, antimicrobial, and antioxidant properties, and enhancing the mechanical strength of packaging materials. The recent advancements in food packaging applications of PUNCs and the challenges and future perspectives related to large-scale production, consumer acceptability, recyclability, and potential health implication aspects are discussed in this chapter. 2026 Elsevier Ltd. All rights reserved. -
The neuropsychology of leadership
In today's rapidly evolving workplace, understanding leadership and management from cognitive, affective, and social neuroscience perspectives contributes greatly to organizational success. This proposed chapter will delve into the specific relevance of the neuropsychology of leadership, highlighting executive control, decision-making, problem-solving, inhibitory control, emotional regulation, and stress management as crucial components of effective leadership. The chapter will also highlight the importance of cognitive profiling, mental health assessments, neuroplasticity, cognitive enhancement, and neurofeedback in the context of leadership and management. This proposed chapter aims to provide a comprehensive overview of the interplay between neuropsychology and leadership, emphasizing its potential to enhance individual and collective success in the modern workplace. 2024 by IGI Global. All rights reserved. -
EEG NEUROFEEDBACK: The Brains Capacity for Change and Growth
Electroencephalography (EEG) neurofeedback is a neoteric tool that uses principles of operant conditioning to modify brain function. This chapter introduces the reader to the foundations of EEG neurofeedback training, highlighting critical studies that led to its development. Neurofeedback research has primarily focused on clinical conditions such as attention deficit hyperactivity disorder (ADHD). The standard protocols of neurofeedback for ADHD are considered to be Efficacious and Specific, Level V in keeping with guidelines of the Association for Applied Psychophysiology and Biofeedback. However, research in neurofeedback in other domains is still in its infancy. This chapter attempts to familiarize the reader with concepts of EEG that are relevant to neurofeedback. These concepts would include a review of standard band frequencies and electrode placement using the international 1020 system. Neurofeedback has a wide range of applications, from the enhancement of performance to the treatment of severe clinical symptoms. Research pertaining to these applications will be discussed. The evolution of protocols and mechanisms over time in the history of neurofeedback will also be reflected. Outcome measures in neurofeedback have typically focused on EEG and clinical changes following the intervention. The need for other measures will be highlighted, offering implications for both basic and applied research. The chapter will conclude with deliberations on critical issues in the practice and research of neurofeedback. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
Metal organic frameworks in biomedicine: Innovations in drug delivery
Metal-organic frameworks (MOFs) have emerged as a class of versatile materials, finding extensive applications in drug delivery because of their unique properties and flexible design. This comprehensive review aims to give a broad perspective on the recent advancements in the area of drug delivery applications using MOFs. The fundamental characteristics of MOFs, highlighting their exceptional porosity, high surface area, and tuneable framework structures, enable MOFs to serve as ideal drug carriers, allowing efficient drug loading and controlled release. The review delves into the various ligands and metal ions employed for drug encapsulation. These include physical encapsulation, covalent bonding, and host-guest interactions, each offering distinct advantages for diverse types of drugs and therapeutic applications. The importance of tailoring MOF properties to optimize drug loading capacity, stability, and release kinetics has been emphasized. Additionally, the explorations involve delving into the mechanisms of drug release from MOFs, with factors such as pH, temperature, and external stimuli that can be harnessed to trigger controlled drug release. The utilization of MOFs in combination therapies, such as co-delivery of multiple drugs or integrating imaging agents, has also been examined. Numerous examples of MOFs used for drug delivery, encompassing both in-vitro and in-vivo studies, covering a wide range of therapeutic areas, including cancer treatment, antimicrobial therapy, and targeted drug delivery, are included. Additionally, the review addresses the challenges and future perspectives in the development of MOFs for drug delivery. Strategies to improve MOF stability, biocompatibility, and scalability are discussed, along with the understanding of MOF-drug interaction and potential toxicity concerns. With their tuneable properties, high loading capacities, and controlled release capabilities, MOFs hold exceptional capabilities that promise to enhance the efficacy of therapeutic interventions. Continued research and development in this area can pave way for the translation of MOFs into clinical applications in the near future. 2024 The Author(s) -
Role of Memoirs in Reducing the Stigma of Mental Illness in India
How reading about mental illness in the form of memoirs encourages us to reimagine our understanding and get past the popular stigmatised depictions of mental illness in India is explored in this article. This information can come to the aid of medical enthusiasts, psychologists, psychoanalysts, and even educators in considering the subjective dimensions of the experience of mental illness apart from the results of scientifi c inquiry and reducing the stigma of mental illness in India. 2023 Economic and Political Weekly. All rights reserved.