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Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN
Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection. 2023, Modern Education and Computer Science Press. All rights reserved. -
Twitter sentiment analysis on online food services based on elephant herd optimization with hybrid deep learning technique
Twitter is a social media stage, making it a valuable resource for learning about peoples opinions, feelings, and thoughts. For this reason, experts came up with methods to analyse the tone of tweets and determine whether they were favourable or negative. This article aims to assist businesses, and especially app-based meal delivery businesses, in conducting competitive research on social broadcasting and transforming social broadcasting data into data production for decision-makers. In this analysis, we compared Swiggy, Zomato, and UberEats. Customers tweets about all these brands are obtained using R-Studio, and a deep learning-based sentiment examination approach is functional on the retrieved tweets. The pseudo-inverse learning autoencoder is able to provide feature extraction in the form of an analytic solution after pre-processing, without resorting to many iterations. In this research, we suggest framework for combining the Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) models. ConvBiLSTM is used, which is a word embedding model that uses numerical values to represent tweets. The CNN layer takes the feature implanting as input and outputs lower features. In this instance, elephant herd optimization is used to fine-tune the Bi-LSTM weights. Among the three firms, the results indicate that Zomato got the most positive feedback (29%), followed by Swiggy (26%), and UberEats (25%). Zomato also had fewer bad reviews than Swiggy and UberEats, with only 11% of users having a poor experience. In addition, tweets were evaluated for unfavourable views against all three meal delivery services, and suggestions for improvement were offered. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
An efficient clustering approach for optimized path selection and route maintenance in mobile ad hoc networks
Mobile ad hoc network (MANET) is arranged with multiple nodes that communicate wirelessly. However, MANET communication suffers from various issues such as inadequate security, low stability, high power consumption, and a lack of specific infrastructure of the network. Moreover, the route failure happened in the network due to the unrestricted node movement, which has increased energy utilization, delay, and reduced lifetime of the nodes. To overcome these issues, the novel Eagle Based Density Clustering (EBDC) approach is developed in this research that predicts the link failure and increased the lifetime of the nodes. Here, the developed EBDC approach is utilized for clustering and route maintenance in MANET for that it creates the nodes using the star topology. Initially, the developed approach selects the Cluster Head and transmits the message through the created path. Subsequently, the link failure is detected by the EBDC model, and it creates a new reference layer to replace the exhausted layer. Hence, the developed EBDC model has enhanced the network lifetime and reduced energy utilization. Furthermore, this model is implemented using Network Simulator 2, and the parameters like accuracy, energy consumption, Packet Delivery Ratio, network lifetime, end-to-end delay, and throughput are calculated. Additionally, the attained outcomes are compared with prevailing methods for evaluating the efficiency of the developed approach. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet
Integrating cutting-edge technology with conventional farming practices has been dubbed smart agriculture or the agricultural internet of things. Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks. 2023, Springer Nature Limited. -
Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges
Mental health issues, including anxiety, stress, and depression, may remain untreated until they escalate to a severe level. The issues significantly impact an individual's overall well-being and productivity. Timely identification is crucial for the effectiveness of both intervention and therapy. The application of machine learning techniques makes behavioral analytics a powerful tool for mental health disease prediction modeling. By analyzing behavioral data, this technology facilitates the early detection of various illnesses. This work aims to provide a thorough overview of the use of machine learning techniques, including models that employ Deep structured learning as well as both unsupervised as well as supervised learning, to behavioral data, including activity levels, speech patterns, and facial movements, in order to identify signs of mental health. The benefits and drawbacks of a broad range of machine learning algorithms are examined, with a focus on how these computer algorithms may be applied to identify patterns linked to illnesses like stress, anxiety, and emotional depression. This study looks into the problems that this business encounters as well. These difficulties include combining behavioral data with extra environmental issues and physiological features from the immediate surroundings, the necessity for large and diverse datasets, the need for security of information, and the capacity to understand models. 2025 IEEE. -
Exploring the Nexus of Deepfakes and VFX Technology: Unveiling Insights, Challenges, and Innovations
This research paper explores the intersection of Deepfakes and Visual Effects (VFX) technology, investigating their convergence, implications, and advancements. Deepfakes, driven by artificial intelligence algorithms, have revolutionized the creation of synthetic media, while VFX techniques have long been utilized in the film industry for various purposes. This paper delves into the technical underpinnings of both Deepfakes and traditional VFX, highlighting similarities, differences, and synergies. It examines the potential applications of Deep-fakes in VFX-driven storytelling, digital compositing, and character animation, while also addressing the ethical concerns and risks associated with their misuse. Furthermore, the paper discusses emerging trends and innovations that bridge the gap between Deepfakes and VFX technology, paving the way for new creative possibilities and challenges. Through a comprehensive analysis, this paper aims to provide valuable insights into the evolving landscape of synthetic media and its implications for the VFX industry. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Early detection of mental health disorders using machine learning models: An analysis based on behavioral and voice data
Mental illnesses are to be detected promptly and correctly to intervene effectively and in time. In this paper, a multi-stage NeuroVibeNet model of early mental disorders detection based on multimodal behavioral and voice data is proposed. It starts with the preprocessing of data that is high-quality and consistent, such as mean imputation, min-max normalization, outlier detection, noise reduction, and short-time energy extraction. The majority of the advanced methods employed in extracting temporal, spectral, and complex features include multiscale entropy, soft dynamic time warping, spectral contrast analysis, formant frequency analysis, and a one-dimensional convolutional neural network autoencoder. The feature selection is done via a sparse autoencoder that is used to maximize relevance and minimize redundancy. The chosen features are fed into the NeuroVibeNet architecture, where TabNet is used to process behavioral data, and Capsule Networks are used to process voice data to allow learning representations with attention and hierarchy. Lastly, a voting-based ensemble classifier uses the two modalities to combine the predictions to make strong classification decisions. The structure is coded in Python and tested on three benchmark datasets with the accuracy of 0.9839, 0.9856, and 0.9855, which is better than the current approaches. Copyright 2026. Published by Elsevier Ltd. -
Impacts of Cloud Computing in Digital Marketing
In modern day of digital marketing the cloud computing is proving extremely beneficial links for businesses. Moreover, it's characteristic to access the stored data from anywhere makes it more popular among the entrepreneurs. The present paper is an exploration of the cloud computing in respect of digital marketing. The paper defines and correlates the term cloud computing, digital marketing, as well as also elaborates about benefits that can be harvested by the integration of cloud computing in digital marketing strategy. 2021 IEEE. -
A study on the role of media in the promotion of the konkani language /
Traditions and language have become crucial aspects in keeping up the culture of a particular place. A language is one of the most important means through which a culture can be sustained and even prevented from dying in the light of the westernization and globalization of the society that we are living in today. One such language that has been facing immense threat against the growing strengths and forces of the Westernised world is that of Konkani, a language most typically spoken on the Western coast of India, also known as the Konkan coast. -
Impact of climate adaption and resilience on mental and social wellbeing
According to United Nations Climate Change, adaptation refers to adjustments in ecological, social, or economic systems in response to actual or expected climatic stimuli and their effects. In contrast, resilience is all about being able to cope with unexpected or difficult circumstances and being able to persevere in the face of challenges, overcoming barriers and bouncing back after setbacks. While adaptability involves changing to manage under new conditions, resilience, through bouncing back, implies the ability to revert to a previous, more positive state after experiencing some difficulty or challenge. Marianne Hrabok (2020) The pathways through which extreme climate events affect mental health are numerous and include direct (e.g., exposure to trauma) and indirect (social, economic disruptions) routes (Ramadan and Ataallah, 2021). Climate-related catastrophes have significant impacts on the mental well-being of the populations involved, causing surges in cases of depression, anxiety, and posttraumatic stress disorder (PTSD) primarily (Gina Martin, 2022). Studies suggest that the mental well-being impacts and negative emotions that stem from climate change awareness may be shared among child populations (Doherty & Clayton, 2011). In addition to direct and indirect psychological impacts, climate change is likely to impact social and community relationships. Some of these impacts may result directly from changes in climate, but most are likely to be indirect results of shifts in how people use and occupy territory. The response to climatic change by any living organism or system is to adapt or be resilient. This chapter discusses the different types of adaptation and resilience strategies theoretically and successfully adopted by various countries in the world. These strategies have a remarkable impact on the mental and social well-being of its stakeholders. With the discussion on the impacts, this chapter will also suggest strategies to be adopted at an individual level to either adapt or resilience toward climatic change to enhance mental and social well-being at the same time. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Mimicking technology in creating and optimising marketing value, customer experience, retention and loyalty
Marketing has evolved from 1.0 to 5.0, which emphasises the utilisation of technologies that mimic human behaviour to generate, convey, distribute and improve value throughout the customer experience. The primary technologies employed in Marketing 5.0 include Artificial Intelligence (AI), Natural Language Processing (NLP), mixed reality (MR, which encompasses augmented reality [AR] and virtual reality [VR]), Sensors and Robotics, Internet of Things (IoT) and blockchain. In today's technologically learnt marketplace, these technologies support firms to imitate the human interaction with customers. Technology plays a crucial role in enhancing marketing results and aids in various aspects. This chapter delves into the conceptual grasp of integrating technology into the realm of marketing, a concept coined by Philip Kotler as 'Marketing 5.0'. Additionally, it incorporates an in-depth review of the existing literature on the diverse tools and strategies employed by various organisations to navigate the ever-evolving and fiercely competitive marketplace. Furthermore, this section will be expanded to include real-world cases of both successes and failures, with thorough explanations, pertaining to the human-centric technological approaches embraced by firms. This chapter also includes a proposed model to understand the impact of usage of such technologies by marketers on the prospective customers and consumers. The outcomes of the research carry both theoretical implications for academicians and the managerial implications for marketers to incorporate mimicking technology for customer experience, loyalty and retention. 2025 Kiran Vazirani and Sunanda Vincent Jaiwant. All rights reserved. -
The Factors Impacting Parental Choice in Picking Non-public Schools for Their Children
The purpose of the study was to investigate the school related factors influencing parental choice of private schools in the city of Bangalore. The study intended to analyze factors affecting parents choice of private schools in Bangalore, to discuss the extent to which various factors influence parents choice of private schools. The study used descriptive survey design. The target population of this study consisted of all parents of students studying in private primary schools in the city of Bangalore. A total sample of 180 parents was drawn purposively from Bangalore. The tool used for collecting the data was a self-constructed questionnaire which included 32 statements were prepared on the basis of a 5-point Likert scale. The study identified seven distinct factors affecting the parents decision of choosing a private school. Among these the factor that was seen to have most significant influence on parents decision to choose a private was school environment. The second most important factor that parents considered was the School quality. Third, parents considered curricular activities offered by school. Next, parents considered Quality of instruction while choosing a school. However, student welfare, parental involvement and proximity to the area of residence were considered less important by parents when choosing a school. The Author(s) 2021. -
Understanding Teacher Work Motivation: Structural, Relational, and Emotional Predictors from India's Urban Classrooms
This study examined predictors of work task motivation among secondary-school teachers in Bengaluru, Indiaa rapidly urbanizing education context marked by diversity and workload challenges. Survey data from 756 teachers were analyzed using multiple regression models. Instructional design explained the largest variance in motivation (R2?=?25.1%), followed by affirming school environments (R2?=?23.2%) and teachers socialemotional competence (R2?=?15.2%). Key predictors included socialemotional instruction (??=?.312), cooperative learning (??=?.170), warmth and support (??=?.299), and social awareness (??=?.198). Female teachers showed stronger motivation in collaborative settings, and experienced educators benefited more from supportive climates. Findings align with OECD and UNESCO evidence emphasizing that supportive environments and structured pedagogy are central to sustaining teacher motivation and retention. The Author(s) 2025 -
What Keeps Secondary School Teachers Motivated? A Qualitative Study from Urban Indian Classrooms
Teacher motivation is widely recognized as central to instructional quality and teacher retention, yet limited research has examined how it is experienced and sustained within policy-driven, high-pressure school systems. This qualitative study explores how secondary school teachers in Bengaluru, India, understand and maintain motivation in their everyday professional practice. Drawing on six in-depth interviews and reflexive thematic analysis, five interrelated drivers were identified: student engagement, emotional connection, instructional autonomy, collegial support, and recognition. Teachers described motivation not as a stable trait, but as a dynamic process continually shaped through relationships, daily pedagogical decisions, mentoring roles, creative planning, and small acts of professional agency. By foregrounding teachers lived experiences, the findings complement large-scale motivation research and offer insight into how motivation is relationally constructed and negotiated within structural constraints. The study underscores the importance of school environments that protect autonomy, acknowledge emotional labour, and cultivate trust as conditions for sustaining long-term teacher engagement. The Author(s) 2026 -
Sustaining teacher social-emotional competence: a systematic review of implementation and retention strategies; [??????????? ?????????-????????????? ?????????????? ????????: ??????????????? ????? ????????? ?????????? ? ???????????]
Social-emotional competence (SEC) refers to educators capacity to regulate emotions, sustain psychological resilience, and cultivate constructive relationships with students, colleagues, and school leadership. Elevated levels of SEC among teachers are strongly associated with enhanced well-being, emotionally supportive classrooms, and improved student engagement and achievement. Despite growing attention to SEC development initiatives, critical gaps remain regarding demographic variability in outcomes, optimal implementation strategies, and enduring institutional barriers. This systematic review, conducted in accordance with PRISMA guidelines, screened 1519 studies published between 2012 and 2024, yielding 16 peer-reviewed articles that met the inclusion criteria. Findings demonstrate that SEC interventions reliably enhance educators emotional regulation, mindfulness, and overall psychological well-being, irrespective of gender, professional experience, or cultural context. However, the long-term sustainability of these benefits is contingent upon enabling school environments, strong leadership, continuous professional development, and adequate resource allocation. Implementation challenges including time constraints, inconsistent program fidelity, and varying levels of teacher readiness underscore the need for adaptive, context-sensitive models. This review provides evidence-based recommendations for the effective design, integration, and sustained impact of SEC programs across diverse educational settings. Ved A., Kareem J., 2026. -
Wireless Sensor Networks in Precision Monitoring of Crops
The sensor-based breadboard is rapidly covering almost every application from human health monitoring to prediction of diseases in accordance with the weather change. This paper presents a sensor based precision crop monitoring system for agriculture application and estimates the energy consumption of the sensor nodes. This high accuracy energy efficient system drastically reduces the damages to the crops and investment made to it. The main focus of the proposed research work is to reduce the energy consumption and minimize the traffic between the nodes of the sensor during the transmission of sensor information. The qualitative metrics has been carried to evaluate the performance of the proposed system which outperform the existing scenario. 2022 IEEE. -
Talent acquisition-artificial intelligence to manage recruitment
The research aims to examine the awareness of Artificial Intelligence among the HR managers and Talent Acquisition managers in the process of Talent Acquisition, Investigating the factors influencing the adoption and usage of Assisted Intelligence, and evaluating the impact of Artificial Intelligence on Talent Management. Multi-Stage sampling method was adopted to collect the responses from the 384 customers across the HR and TA managers working across the IT companies situated in Bangalore, Mysore, Pune, and Chennai & Hyderabad. SAS was applied to perform the Simple Percentage Analysis, Correlation Analysis, Multiple Linear Regression Analysis to validate the hypothesis. The demographic & construct variables considered were Adoption, Actual usage, Perceived usefulness, Perceived Ease of Use, & Talent Management. Awareness of the Artificial Intelligence technology and its adoption in managing Talent Acquisition has the positive and high correlation and followed by its actual usage. Candidate experience is the most influencing variable from the first factor, Competency and Easy to use is the most influencing variable from the second factor, Effectiveness in the adoption and actual usage of Artificial Intelligence in Talent Acquisition. Talent Management is the highest predictor of using the technology and its adoption is the most influencing predictor in the effective implementation of the technology among the Information Technology Companies. The Authors, published by EDP Sciences. -
Algorithm trading and its application in stock broking services
Purpose: Algorithmic trading provides a more systematic approach to active trading than methods based on trader intuition or instinct. The aim of the study is to examine the level of awareness among the brokers when integrated with technology for the purpose of executing the trades. Design/Methodology: A self-administered and structured 350 questionnaires were designed and circulated to collect the preliminary information from the stock brokers operating in NSE and BSE within the geographical limits of Bangalore district using the Systematic Sampling method to obtain a sample size of 235. Awareness, Automated trading, Elimination of human error, portfolio management, tracking order, order placement were the critical variables observed to validate the hypothesis using Simple Percentage Analysis & Chi-Square Analysis using Statistical Analysis Software (SAS). Findings: It was found that there is robust association between the level of awareness of the mentioned technology in its application by the stock brokers of NSE and BSE operating in Bangalore. Portfolio management and automated trading are the highly associated application of Algorithmic trading among the stock brokerage services. Originality: Algorithmic trading makes use of complex formulas, combined with mathematical models and human oversight, to make decisions to buy or sell financial securities on an exchange. It can be used in a wide variety of situations including order execution, arbitrage, and trend trading strategies. Algorithmic traders often make use of high-frequency trading technology, which can enable a firm to make tens of thousands of trades per second. The Authors, published by EDP Sciences. -
Prioritizing evaluation criteria of IoT-driven warehousing startups: asilver lining to the unorganized sector in food supply chain
Purpose: This research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain. Design/methodology/approach: A blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups. Findings: The critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups. Practical implications: Warehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience. Originality/value: Despite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work. 2024, Emerald Publishing Limited. -
Prioritizing evaluation criteria of IoT-driven warehousing startups: asilver lining to the unorganized sector in food supply chain
Purpose: This research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain. Design/methodology/approach: A blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups. Findings: The critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups. Practical implications: Warehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience. Originality/value: Despite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work. 2024, Emerald Publishing Limited.