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Cloud and IoT-Driven Smart Irrigation: A Modern Approach to Water Management in Agriculture
Agriculture faces the dual challenge of meeting global food demand while conserving scarce water resources under climate change. Conventional irrigation systems often result in water wastage and high manual intervention. This study proposes a Cloud- and IoT-driven smart irrigation framework that integrates Wireless Sensor Networks (WSNs), an NI CompactRIO controller, renewable energy, and real-time weather forecasting services. The system collects and analyzes heterogeneous data streams (soil moisture, humidity, temperature, and water levels) to dynamically control irrigation schedules. Experimental validation on a prototype farm in Morocco demonstrates that the proposed system reduces weekly water consumption by 26, lowers irrigation events from 10 to 6, eliminates manual interventions, and achieves 13.6 energy savings compared with traditional methods. The integration of predictive weather data prevents over-irrigation during rainfall, while cloud-based analytics enhance scalability and monitoring. These results highlight the system's potential for resource-efficient, autonomous, and sustainable agriculture, particularly in water-scarce regions. Future work will focus on extending the system with LoRa-based sensor networks and machine learning-based solar energy forecasting to further improve adaptability and scalability. 2026, International Association of Engineers. All right reserved. -
A hybrid approach for COVID-19 detection using biogeography-based optimization and deep learning
The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron emission tomography, lung ultrasound, and magnetic resonance imaging, as inputs. In the comparative analysis, the proposed deep learning model CNN is compared with other existing models, namely, VGG16, InceptionV3, ResNet50, and MobileNet. In the fitness function formation, classification accuracy is considered to enhance the prediction capability of the proposed model. Experimental results demonstrate that the proposed model outperforms InceptionV3 and ResNet50. 2022 Tech Science Press. All rights reserved. -
Cross-layer hidden Markov analysis for intrusion detection
Ad hoc mobile cloud computing networks are affected by various issues, like delay, energy consumption, flexibility, infrastructure, network lifetime, security, stability, data transition, and link accomplishment. Given the issues above, route failure is prevalent in ad hoc mobile cloud computing networks, which increases energy consumption and delay and reduces stability. These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network. To address these weaknesses, which raise many concerns about privacy and security, this study formulated clustering-based storage and search optimization approaches using cross-layer analysis. The proposed approaches were formed by cross-layer analysis based on intrusion detection methods. First, the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing networks. Moreover, delay, energy consumption, network lifetime, and link accomplishment are highly addressed by the proposed algorithm. The hidden Markov model is used to maintain the data transition and distributions in the network. Every data communication network, like ad hoc mobile cloud computing, faces security and confidentiality issues. However, the main security issues in this article are addressed using the storage and search optimization approach. Hence, the new algorithm developed helps detect intruders through intelligent cross layer analysis with the Markov model. The proposed model was simulated in Network Simulator 3, and the outcomes were compared with those of prevailing methods for evaluating parameters, like accuracy, end-to-end delay, energy consumption, network lifetime, packet delivery ratio, and throughput. 2022 Tech Science Press. All rights reserved. -
Deep Belief Neural Network for 5G Diabetes Monitoring in Big Data on Edge IoT
The diabetes is a critical disease from the small children to old age people. Due to improper diet and physical activities of the living population, obesity becomes prevalent in young generation. If we analyze self care of individual life, no man or women ready to spend their time for health care. It leads to problem like diabetes, blood pressure etc. Today is a busy world were robots and artificial machines ready to take care of human personal needs. Automatic systems help humans to manage their busy schedule. It motivates us to develop a diabetes motoring system for patients using IoT device in their body which monitors their blood sugar level, blood pressure, sport activities, diet plan, oxygen level, ECG data. The data are processed using feature selection algorithm called as particle swarm optimization and transmitted to nearest edge node for processing in 5G networks. Secondly, data are processed using DBN Layer. Thirdly, we share the diagnosed data output through the wireless communication such as LTE/5G to the patients connected through the edge nodes for further medical assistance. The patient wearable devices are connected to the social network. The Result of our proposed system is evaluated with some existing system. Time and Performance outperform than other techniques. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Effective Tensor Based PCA Machine Learning Techniques for Glaucoma Detection and ASPP EffUnet Classification
Main problem in current research area focused on generating automatic AI technique to detect bio medical images by slimming the dataset. Reducing the original dataset with actual unwanted noises can accelerate new data which helps to detect diseases with high accuracy. Highest level of accuracy can be achieved only by ensuring accuracy at each level of processing steps. Dataset slimming or reduction is NP hard problems due its resembling variants. In this research work we ensure high accuracy in two phases. In phase one feature selection using Normalized Tensor Tubal PCA (NTT-PCA) method is used. This method is based on tensor with single value decomposition (SVD) for accurate dimensionality reduction problems. The dimensionality reduced output from phase one is further processed for accurate classification in phase two. The classification of affected images is detected using ASPP EffUnet. The atrous spatial pyramid pooling (ASPP) with efficient convolutional block in Unet is combined to provide ASPP EffUnet CNN architecture for accurate classification. This two phase model is designed and implemented on benchmark datasets of glaucoma detection. It is processed efficiently by exploiting fundus image in the dataset. We propose novel AI techniques for segmenting the eye discs using EffUnet and perform classification using ASPP-EffUnet techniques. Highest accuracy is achieved by NTT-PCA dimensionality reduction process and ASPP-EffUnet based classification which detects the boundaries of eye cup and optical discs very curiously. Our resulting algorithm NTT-PCA with ASPP-EffUnet for dimensionality reduction and classification process which is optimized for reducing computational complexity with existing detection algorithms like PCA-LA-SVM,PCA-ResNet ASPP Unet. We choose benchmark datasets ORIGA for our experimental analysis. The crucial areas in clinical setup are examined and implemented successfully. The prediction and classification accuracy of proposed technique is achieved nearly 100%. 2021, Springer Nature Switzerland AG. -
Exploring the Impact of Personalized Customer Experiences in Modern Business Strategies
In todays dynamic business landscape, individual customer experiences (CEs) play a crucial role in shaping the strategies and operations of modern organizations. Companies increasingly prioritize customer interactions, using insights from these experiences to refine their services, enhance engagement, and drive long-term success. This chapter explores how personal CEs shape the strategies and operations of modern organizations. It focuses on integrating customer-centric tools and strategies that enhance customer relationships, product and service models, and operational frameworks. The chapter employs a qualitative analysis of contemporary business practices, examining the role of CE in various industries. It incorporates insights from recent research and case studies to highlight the evolution of branding, advertising, and customer engagement strategies in response to technological advancements and changing consumer behaviors. The findings indicate that great CE is crucial for customer retention and competitive advantage.organizations that effectively leverage technology and advanced customer knowledge are better positioned to create meaningful customer relationships. The chapter also identifies gaps in traditional customer relationship management (CRM) systems, emphasizing the need for a more holistic, customer-centric approach to marketing and operations. Businesses must prioritize enhancing CEs through tailored strategies and operational models. 2026 Aarti Saini and Vikas Garg. -
Optimizing Interpretability in Recommender Systems using a Hybrid Model based on Matrix Factorization and Neural Networks
Recommender systems play a crucial role in the direction of user choices in e-commerce, media, and online services, clearly, there is a trade-off between predictive accuracy and interpretability. In this paper, a new hybrid model that combines Matrix Factorization and a Neural Network framework to maximize the performance of recommendation as well as explainability has been suggested. The model uses Latent factor representation of Matrix Factorization to provide the global user item interactions, and the Neural Network component finds nonlinear interaction and contextual patterns in the data. The hybrid architecture is trained and tested on a Kaggle dataset of 100,000 user-item interactions with several numerical and categorical characteristics. It compares to standalone methods in that the system is more superior with an accuracy of 94.5, F1-score of 0.945, mean absolute error (MAE) of 0.087 and root mean squared error (RMSE) of 0.112. It is proven by computational analysis to have efficient training convergence and low inference latency, allowing real-time recommendations on Google Colab. The proposed solution bridges the gap between performance and transparency since it can be applied and is credible by being predictive and understandable at the same time. The study has implications in intelligent, explainable and scalable recommenders systems in diverse areas of application. 2025 IEEE. -
Wheat Disease Diagnosis using Transfer Learning on Convolutional Neural Networks
Wheat disease identification is essential for agricultural output and food security. Traditional diagnostic approaches are slow, ineffective, and need expert assistance, restricting their ability to grow in agriculture. Suggested innovative diagnostic method uses transfer learning on convolutional neural networks (CNNs) to effectively identify and classify wheat leaf diseases. To increase model predictions, high-quality image datasets from open-access platforms are normalised, resized, and augmented. The proposed CNN model performed best with 98.90% accuracy, 98.87% precision, and 98.80% recall. Transfer learning improved model performance by recycling knowledge from pre-trained CNN architectures, reducing training time and enhancing feature extraction. The results show improved precision as well as strength over standard methods and before. This technology helps farmers and agricultural professionals make timely disease management and crop management decisions. To improve disease recognition, future study may use a wider dataset range and other CNN designs. 2025 IEEE. -
Physical Co-location: an intersection of problem-solving and vicarious learning
Scholars have examined Revans' problem-solving praxeology in many contexts but have not fully explored the concept in the case of physical co-location. Hence, we focussed on investigating Revans' conceptualisation in a co-located context by paying particular attention to the different forms of learning' that emerged from it. The research setting for this study involved two coworking spaces in Bangalore, India, whose constituents were co-located start-ups and established enterprises. Held from January to March 2020, the study involved conducting exploratory, semi-structured interviews with twelve firms. The findings suggested that in a co-located environment, a) firms learnt vicariously' from a rich, external knowledge base during the enquiry-led Alpha phase b) firms learnt experientially', through learning by doing and reflecting in the implementation-focussed Beta phase c) firms learnt through the process of emergence that resulted from personal reflection and team interaction, in the revelatory Gamma phase. This study lends a novel direction in acknowledging that vicarious learning, that is, learning through the experience of others, serves as a starting point for problem-solving in a co-located context. We demonstrate that firms gain familiarity with the problem through vicarious sources, that is, from those experienced co-located firms who had journeyed on a similar path. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Value Addition for Technology Start-Ups Through Physical Co-Location
Numerous economic theories, knowledge, social, and communication theories have extensively explored the phenomenon of physical co-location in various contexts. However, limited scholarly attention has been given to co-location in emerging contexts such as co-working spaces, predominantly used by start-ups. One of the critical questions examined is how co-location adds value to technology start-ups in the early and growth stages of their development. We chose a premium coworking space in Bangalore, Indias start-up capital, as the studys research setting during January March 2020. The qualitative research employed semi-structured interviews to explore the phenomenon. Our findings revealed that start-ups actively used co-located resources to explore, experiment, and validate new business ideas in the early stage. As they transitioned into the growth phase, they exploited co-located industry networks to expand into new markets. They also learned vicariously from other co-located resources and used them to solve complex problems and refined their processes and routines. As start-ups begin to grow and expand, co-location infrastructure-related costs are not justifiable, operations are less secure, and the meta culture of the co-located environment is in conflict with the firms operating culture. The results of this study have the potential to be significant for technology start-ups that are exploring new ways of working and addressing uncertainties during the early and growth stages of their development. 2021, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Educational technology at pivotal crossroads
Educational technology startups, commonly referred to as EdTech, combine education and innovative technology to transform school environments and improve student learning outcomes. Set against the backdrop of primary and secondary schools, this exploratory study uncovers the most important factors affecting the growth of EdTech startups in Bengaluru, India. Drawing on Isenberg's Entrepreneurship Ecosystem Model (2010, 2011) this exploratory, qualitative study concludes that "lack of conducive culture, infrastructure support, and finance as well as inadequacies in entrepreneurial approach and value addition" affect the growth of startups in EdTech Entrepreneurial landscape. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Efficient Brain Tumor Identification Based on Optimal Support Scaling Vector Feature Selection (OSSCV) Using Stochastic Spin-Glass Model Classification
Brain tumor detection is a developing defect finding task in medical imaging, as premature and early identification is a critical once for recommending early treatment. The tumor are identified by the laboratory through MRI images by finding the tumor regions. The Artificial intelligence play a vital role for finding, analyzing, the image data to attain the target results in medical image using various learning methodologies. Most of the existing system failed to find the find the feature dimension leads poor accuracy for identifying tumor regions due to low precision, recall rate, lower intensity in image coverage region. To resolve this problem, to propose an Optimal Support Scaling Vector Based Feature Selection (OSSCV) brain tumor identification using Stochastic Spin-Glass Model Classification (SSGM). Initially the preprocessing is done by bilateral filter and segmentation is applied by suing Active Region Slice Window Segmentation (ARSWS). To separate the tumor entity feature projection using Histogram color quantization and the features process are carried by Optimal Support Scaling Vector Based Feature Selection (OSSCV). The selected features get trained using Stochastic Spin-Glass Model Classification (SSGM) to find the tumor region. The proposed system outperforms traditional machine learning methods in brain tumor detection. Finally proposed system of Stochastic Spin-Glass Model (SSGM) performance of recall is 95.5%, the performance of F1-score is 96.1% and the performance of the 96.5%. The proposed approach has the potential to assist radiologists in diagnosing brain tumors more accurately and efficiently, leading to improved patient outcomes. 2024, Ismail Saritas. All rights reserved. -
Enhancing Prognostication in Colorectal Cancer with Integrated Machine Learning for Improved Survival Prediction
Machine learning methods are recently used to predict patient survival in colorectal cancer using such models as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), VGG16, and Support Vector Machines (SVM). Taking advantage of a combination of CT, MRI scan images, and clinical records with drug recommendations, the study also checks to see how these models compare for distinguishing between patients in terms of their illness course-whether they are going to get better or worse over time. The results reveal VGG16 has better accuracy than CNN, RNN and SVM; as the highest-performing model tested, it also demonstrates superior precision, recall and F1-score. The research findings also validate these proposed models as they compare favorably with existing literature. This presents a promising proposition: a new, revolutionary approach to using artificial intelligence to boost prognostic accuracy. 2025 IEEE. -
Factors influencing job satisfaction of migrant workers in Coimbatore district
Recent years have seen a rise in the phenomenon of migration for employment, which has resulted in a more diversified and dynamic global workforce. Migrant workers contribute to a variety of businesses and sectors, which is essential to the economic growth of host nations. Their job satisfaction, though, continues to be a major source of worry. This summary gives a general overview of the variables affecting migrant workers job satisfaction, highlighting significant aspects like pay, working conditions, social support, and cultural assimilation. This study intends to shed light on the complex interactions between these variables and their effects on migrant workers overall well-being and job satisfaction through an examination of the literature already in existence and empirical data. The results of this study can help policymakers, employers, and other stakeholders implement initiatives that improve migrant employees job satisfaction, creating more effective, peaceful, and inclusive work environments. 2025 selection and editorial matter, Hafinaz, Hariharan R and R. Senthil Kumar. -
A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN
Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested methods purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy Logic (ANFL) technique is then used to calculate the channel weight value and the channel with the highest weight is selected for transmission. To compute the channel weight, the proposed ANFIGA-CS model uses three fuzzy input parameters: Primary User (PU) utilization, Cognitive Radio (CR) count and channel capacity. To improve the channel selection process in CRN, the rules in the ANFL scheme are optimized using an updated genetic algorithm to increase overall efficiency. The suggested ANFIGA-CS model is simulated using the NS2 simulator and the results are investigated in terms of average interference ratio, spectrum opportunity utilization, average throughput, Packet Delivery Ratio (PDR) and End to End (ETE) delay in a network with a variable number of CRs. 2022, Tech Science Press. All rights reserved. -
Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks
Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network. Copyright 2023 KSII. -
Solvent free microwave assisted synthesis and evaluation of potent antimicrobial activity of 1,11H-pyrimido[4,5-a]carbazol-2-ones, 1,11H-pyrimido [4,5-a]carbazol-2-thiones and pyrazolo[3,4-a]carbazoles
Microwave assisted condensation of urea, thiourea and hydrazine hydrate with 1-chloro-2-formyl carbazoles in the presence of PTSA as catalyst yields 1,11H-pyrimido[4,5-a]carbazol-2-ones, 1,11H-pyrimido[4,5-a]carbazol-2-thiones and pyrazolo[3,4-a]carbazoles, respectively. The structures of the synthesized compounds have been confirmed on the basis of elemental analysis and spectral data. All the synthesized compounds have been evaluated for their antibacterial and antifungal activities. Some of the synthesized compounds 2a-g and 3a-g exhibit significant antibacterial activity against Escherichia coli and Pseudomonas aeruginosa. The compounds 2a-g and 3a-g exhibit good antifungal activity against Candida albicans, Aspergillus flavus. Pyrazolo[3,4-a]carbazoles 4a-g register good antibacterial activity against Escherichia coli and Pseudomonas aeruginosa. The compound 4e indicate maximum activity of 20 and 24 mm at 500 and 1000?g/disc, respectively, against Lipomyces lopofera fungi. -
6-Bromo-2-(3-phenylallylidene)-2,3,4,9-tetrahydro-1H-carbazol-1-one
molecules of the title compound, C21H16BrNO, are linked through pairs of N-H?O intermolecular hydrogen bonds into centrosymmetric R2 2(10) dimers. One of the C atoms of the cyclohex-2-enone ring is disordered with refined occupancies of 0.61 (2) and 0.39 (2). -
6-Bromo-2-[(E)-thiophen-2-ylmethylidene]-2,3,4,9-tetrahydro-1H-carbazol-1- one
In the title compound, C17H12BrNOS, the cyclohexene ring deviates only slightly from planarity (r.m.s. deviation for non-H atoms = 0.047 . In the crystal, the molecules are linked into centrosymmetric R2 2(10) dimers via pairs of N-H?O hydrogen bonds. The thio-phene ring is disordered over two positions rotated by 180and with a site-occupation factor of 0.843 (4) for the major occupied site. -
Information extraction and text mining of Ancient Vattezhuthu characters in historical documents using image zoning
The aim of this paper is to develop a system that involves character recognition of Brahmi, Grantha and Vattezuthu characters from palm manuscripts of historical Tamil ancient documents, analyzed the text and machine translated the present Tamil digital text format. Though many researchers have implemented various algorithms and techniques for character recognition in different languages, ancient characters conversion still poses a big challenge. Because image recognition technology has reached near-perfection when it comes to scanning English and other language text. But optical character recognition (OCR) software capable of digitizing printed Tamil text with high levels of accuracy is still elusive. Only a few people are familiar with the ancient characters and make attempts to convert them into written documents manually. The proposed system overcomes such a situation by converting all the ancient historical documents from inscriptions and palm manuscripts into Tamil digital text format. It converts the digital text format using Tamil unicode. Our algorithm comprises different stages: i) image preprocessing, ii) feature extraction, iii) character recognition and iv) digital text conversion. The first phase conversion accuracy of the Brahmi script rate of our algorithm is 91.57% using the neural network and image zoning method. The second phase of the Vattezhuthu character set is to be implemented. Conversion accuracy of Vattezhuthu is 89.75%. 2016 IEEE.