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A Multi Objective Artificial Eco-System Based Optimization Technique Integrating Solar Photovoltaic System In Distribution Network
Agricultural sector contributes 6.4% of total economic generation across the world. Notably, the utilization of technology to improve the yield and economy is rapidly increasing. To provide continuous supply to the residential customers, the agricultural feeder grid-dependency has to be integrated with Solar Photo Voltaic (SPV) systems. In this paper, an Artificial Eco-System based Optimization (AEO) algorithm is proposed for simultaneously identifying the locations and quantifying the sizes of SPV systems. A practical distribution system feeder 'Racheruvu 11kV agricultural feeder' Andhra Pradesh, India is considered for simulation purpose and the performance is compared with the standard IEEE-33 radial distribution system. 2022 IEEE. -
A Multi-Modal Approach to Digital Document Stream Segmentation for Title Insurance Domain
In the twenty-first century, storing and managing digital documents has become commonplace for all corporate and public sectors around the world. Physical documents are scanned in batches and stored in a digital archive as a heterogeneous document stream, referred to as a digital package. To make Robotic Process Automation (RPA) easier, it's necessary to automatically segment the document stream into a subset of independent, coherent multi-page documents by detecting the appropriate document boundary. It's a common requirement of a TI company's Automated Document Management Systems (ADMS), where business operations are automated using RPA and the goal is to extract information from digital documents with minimal user intervention. The current study proposes, evaluates, and compares a multi-modal binary classification network incorporating text and picture aspects of digital document pages to state-of-the-art baseline methodologies. Image and textual features are extracted simultaneously from the input document image by passing them through Visual Geometry Group 16 - Convolutional Neural Network (VGG16-CNN) and pre-trained Bidirectional Encoder Representations from Transformers (Legal-BERT {}_{base} ) model through transfer learning respectively. Both features are finally fused and passed through a fully connected layer of Multi Layered Perceptron (MLP) to obtain the binary classification of the pages as the First Page (FP) and Other Page (OP). Real-time document image streams from production business process archive were obtained from a reputed Title Insurance (TI) company for the study. The obtained F_{1} score of 97.37% and 97.15% are significantly higher than the accuracies of the considered two baseline models and well above the expected Straight Through Pass (STP) threshold defined by the process admin. 2013 IEEE. -
A multi-model unified disease diagnosis framework for cyber healthcare using IoMT-cloud computing networks
The past several decades of research into machine learning have been of great assistance to humanity in the diagnosis of a variety of ailments using various forms of automated diagnostic procedures. Machine learning, combined with smart health devices, has improved health monitoring, timely diagnoses, and treatment. This paper introduces a unified disease diagnosis framework, integrating cloud computing, machine learning, and IoT. The framework has three layers: physical (collects patient data), fog (intermediate layer with a domain identification unit to determine input and diagnosis type), and transmission (cloud server with a disease detection unit). The performance evaluation shows the robustness and efficiency of the model as compared to state-of-art models. 2023, Taru Publications. All rights reserved. -
A MULTI-OBJECTIVE HUNTER-PREY OPTIMIZATION FOR OPTIMAL INTEGRATION OF CAPACITOR BANKS AND PHOTOVOLTAIC DISTRIBUTION GENERATION UNITS IN RADIAL DISTRIBUTION SYSTEMS
This article put forward the determination of the optimal siting and sizing of capacitor banks and PV-DG (Photo-Voltaic Distribution Generation) units in a radial distribution system. A modern population-based optimization algorithm, Hunter-Prey Optimization (HPO), is applied to determine the optimal capacitor bank and PV-DG placement. This algorithm, HPO, got its motivation from the trapping behaviour of the carnivore (predator/hunter) like lions and wolves towards their target animal like deer. The typical IEEE-33 & 69 test bus systems are scrutinized for validating the effectiveness of the suggested algorithm using MATLAB software R2021b version. The acquired results are collated with the existing heuristic algorithms for the active power loss criterion. The nominal or base values for system losses and voltage profile were considered for the comparison, with the results from HPO. The HPO application has an efficient performance in figuring out the most favourable location and capacity of the capacitor banks and PV DGs compared with the other techniques. 2023 by authors and Galileo Institute of Technology and Education of the Amazon (ITEGAM). -
A multi-preference integrated algorithm for deep learning based recommender framework
Nowadays, the online recommender systems based collaborative filtering methods are widely employed to model long term user preferences (LTUP). The deep learning methods, like recurrent neural networks (RNN) have the potential to model short-term user preferences (STUP). There is no dynamic integration of these two models in the existing recommender systems. Therefore, in this article, a multi-preference integrated algorithm (MPIA) for deep learning based recommender framework (DLRF) is proposed to perform the dynamic integration of these two models. Moreover, the MPIA addresses improper data and to improve the performance for creating recommendations. This algorithm is depending on an enhanced long short term memory (LSTM) with additional controllers to consider relative information. Here, experiments are carried out by Amazon benchmark datasets, then obtained outcomes are compared with other existing recommender systems. From the comparison, the experimental outcomes show that the proposed MPIA outperforms existing systems under performance metrics, like area under curve, F1-score. Consequently, the MPIA can be integrated with real time recommender systems. 2022 John Wiley & Sons, Ltd. -
A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification
In facial expression recognition applications, the classification accuracy decreases because of the blur, illumination and localization problems in images. Therefore, a robust emotion recognition technique is needed. In this work, a Multi-scale and Rotation-Invariant Phase Pattern (MRIPP) is proposed. The MRIPP extracts the features from facial images, and the extracted patterns are blur-insensitive, rotation-invariant and robust. The performance of classification algorithms like Fisher faces, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are analyzed. In order to reduce the time for classification, an OPTICS-based pre-processing of the features is proposed that creates a non-redundant and compressed training set to classify the test set. Ten-fold cross validation is used in experimental analysis and the performance metric classification accuracy is used. The proposed approach has been evaluated with six datasets Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK +), Multi- media Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and ManMachine Interaction (MMI) datasets to meet a classification accuracy of 98.2%, 97.5%, 95.6%, 35.5%, 87.7% and 82.4% for seven class emotion detection using a stack of Restricted Boltzmann Machines(RBM), which is high when compared to other latest methods. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
A Multi-Stimuli responsive organic luminogen with aggregation induced emission for the selective detection of Zn2+ ions in solution and solid state
Organic luminogens capable of excited state intramolecular electron transfer (ESIPT) have drawn prodigious attraction due to their enhanced emission in solid-state. A novel Schiff base molecule, 3,5-dibromo-2-hydroxybenzylidenenicotinohydrazide (DHN) exhibited stimuli-induced reversible fluorescence switching and selective binding propensity towards zinc in aqueous media, and the concentration-dependent studies showed a limit of detection of 9.135 nM. DHN was found to be weakly fluorescent in polar solvents with a quantum yield ranging between 0.0365 and 0.0789 but exhibited a very strong fluorescence in solid state (?exc = 370 nm) due to aggregation induced emission (AIE). The ESIPT fluorophore renders significant reversible halochromic properties in solution and solid-state. In addition, utilizing the solid-state fluorescence, we have prepared PVA-probe green-emitting composite films, which can be used for the on-site detection of Zn2+ in aqueous media. The practical applicability of DHN was proven by detecting Zn2+ in real drug samples. Finally, the ESIPT fluorophore was used for fluorescent imaging of intracellular zinc in the cells acquired from the nervous tissue of rats (N2a). The investigations carried out highlight the versatility of ESIPT Schiff bases used for the development of multi-responsive fluorescent materials for selective sensing of metal ions in both solid and solution states. 2022 Elsevier B.V. -
A multi-Threshold triggering and QoS aware vertical handover management in heterogeneous wireless networks
Vertical handover management provides seamless connectivity in heterogeneous wireless networks. But still there are different challenges that need to be addressed. These challenges include the inappropriate network selection, wrong cell handover, etc. Therefore, in this article, we proposed a handover management scheme based on the data rate and QoS of available networks. The handover triggering is performed on the data rate requires by different applications. Similarly, the network selection is performed by considering the cost, data rate of available networks and energy consumption by the mobile interface. The proposed scheme is simulated in different mobility scenarios with a random number of applications running on various numbers of mobile nodes. The simulation results show that the proposed scheme requires less energy during the scanning and selection of available networks. 2015 IEEE. -
A Multifaceted Approach at Discerning Redditors Feelings Towards ChatGPT
Generative AI platforms like ChatGPT have leapfrogged in terms of technological advancements. Traditional methods of scrutiny are not enough for assessing their technological efficacy. Understanding public sentiment and feelings towards ChatGPT is crucial for pre-empting the technologys longevity and impact while also providing a silhouette of human psychology. Social media platforms have seen tremendous growth in recent years, resulting in a surge of user-generated content. Among these platforms, Reddit stands out as a forum for users to engage in discussions on various topics, including Generative Artificial Intelligence (GAI) and chatbots. Traditional pedagogy for social media sentiment analysis and opinion mining are time consuming and resource heavy, while lacking representation. This paper provides a novice multifrontal approach that utilises and integrates various techniques for better results. The data collection and preparation are done through the Reddit API in tandem with multi-stage weighted and stratified sampling. NLP (Natural Language processing) techniques encompassing LDA (Latent Dirichlet Allocation), Topic modelling, STM (Structured Topic Modelling), sentiment analysis and emotional analysis using RoBERTa are deployed for opinion mining. To verify, substantiate and scrutinise all variables in the dataset, multiple hypothesises are tested using ANOVA, T-tests, KruskalWallis test, Chi-Square Test and MannWhitney U test. The study provides a novel contribution to the growing literature on social media sentiment analysis and has significant new implications for discerning user experience and engagement with AI chatbots like ChatGPT. 2024 Padarha et al., licensed to EAI. -
A Multilayered Feed-Forward Neural Network Architecture for Rainfall Forecasting
The amount of rain received in a particular demographic region in a given time interval is called the rainfall. Rainfall is a natural and complex process and has significance in different domains including agriculture, transport, disaster management, and natural calamities resilience [1]. Abnormal rainfall affects every facet of humans and all other living beings of the world and also has a great impact in wellbeing and financial disruptions of a country. Accurate rainfall predictions at regular time intervals are always important to issue warnings about likelihood of any disaster about to happen. This also provides people a time for strategic planning in their work and precautions at time of adversity [2]. It is worth noting that rainfall forecasting does not only have an impact in day-to-day life, but more importantly for tropical countries like India where the chief occupation being agriculture and also for various other industries. It largely helps in disaster management and recovery process as well. The rainfall being a variable over time, geography and atmospheric conditions makes the forecasting considerably difficult [3]. Rainfall forecasting keeps a person informed about the likelihood of rainfall the forthcoming day, week, or month which enable long-time planning and on the other way; hourly prediction helps for shortterm planning such as enforcing traffic measures. Literature has seen various studies in this domain using predictive machine learning (ML) algorithms such as neural networks (NNs), Genetic algorithms, and Fuzzy-based systems [4]. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
A multilevel analysis of hiv1-miR-H1 miRNA using KPCA, K-means, Random Forest and online target tools
The goal of this study was to propose a workflow using machine learning to identify and predict the miRNA targets of Human Immunodeficiency virus 1. miRNAs which is ~21 nt long are attained from larger hairpin RNA precursors and is maintained in the secondary structure of their precursor relatively than in primary chain of successions. The proposition approach for identification and prediction of miRNA targets in hiv1-miR-H1is based on secondary structure and E-value through machine learning. Data Linearity of Length and e-value for sequence match with hiv1-mir-H1 is verified using Kernel PCA. miRNA targets were grouped into clusters thereby indicating similar targets using K-means algorithm. Classification model using Random Forest was implemented regards to each secondary features variable considering feature relevance. A learning methodology is put forward that assimilate and integrate the score returned by various machine learning algorithms to predict cellular hiv1-miR-H1 targets. Gene targets results using TargetScan, miRanda, PITA, DIANA microT and RNAhybrid are also explored for multiple parameters. 2021 Inderscience Enterprises Ltd. -
A Multiplier-Less FRM-Based Reconfigurable Regulated Bank of Filter for Spectrum Hole Detection in IoT
A promising solution for the detection of spectrum holes in the Internet of Things networks is the cognitive radio (CR) system, which is used to identify spectrum holes effectively. The intention of this work is to design a low-complexity Reconfigurable Regulated Bank of Filter (RRBF) structure for spectrum hole detection in IoT networks. The RRBF structure is designed by utilizing the Frequency Response Masking (FRM) approach and the Cosine Modulation Technique (CMT). Using the RRBF structure, multiple sharp non-uniform channels are generated for efficient spectrum hole detection in IoT networks. With the aid of an example, the performance and computational complexity of the RRBF structure are demonstrated. The result shows that the RRBF structure has a fewer multipliers than other existing methods. To obtain hardware-efficient realization, the RRBF structure is made of multiplier-less by incorporating Canonical Signed Digit (CSD), Multi-Objective Artificial Bee Colony (MOABC), and Shift Inclusive Differential Coefficients (SIDC) with Common Sub-expression Elimination (CSE) optimization techniques. 2024 IETE. -
A Narrative Review on Experience and Expression of Anger Among Infertile Women
Infertility is stressful among women though there are several technological advancements in treating infertility. Anger is a powerful emotion resulting due to stigma and oppression due to infertility, especially among women. Studies have also proven that women have a poor quality of life in the context of infertility. Women are prone to suppressing anger rather than dealing with anger in the present. Psychosocial intervention and psychoeducation help women manage anger and maintain healthy quality of life. Springer Nature Switzerland AG 2023. -
A Narrative Synthesis on the Role of Affective Computing in Fostering Workplace Well-Being Using a Deep Learning Model
Emotional information is more valued in the modern workplaces with increased focus on the need for sensing, recognizing and responding to human emotions. Integrating human emotions as information for communication and decision-making is possible through the computer-based solution called as affective computing. Affective computing is a relatively less explored AI platform though the notion is more than two decades old. The cognitive algorithms employed in affective computing operates in three key areas, viz. context sensitivity, augmented reality, and proactiveness, with outcomes in the fields of emotion management, health, and productivity. Affective computing promises better management of organizational outcomes such as fostering workplace well-being, promoting happiness, productivity, engagement levels, and communication. Further, affective computing can play vital roles in an employees life cycle with applications in functional areas of HRM like employee selection, training and development, and performance management. Even as workplaces are increasingly adopting affective computing, an analysis of its positive effects can help practitioners take informed decisions about its implementation. This paper outlines the theoretical underpinnings of affective computing, discusses the relevance of ResNet50 in image analysis, and proposes a step-by-step methodology for implementing affective computing techniques in the workplace. The potential benefits and challenges of adopting affective computing in fostering workplace well-being are also discussed. Thus, this chapter investigates the role of affective computing in fostering well-being in the workplace usinga deep learning model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Neural Network Based Customer Churn Prediction Algorithm for Telecom Sector
For telecommunication service providers, a key method for decreasing costs and making revenue is to focus on retaining existing subscribers rather than obtaining new customers. To support this strategy, it is significant to understand customer concerns as early as possible to avoid churn. When customers switch to another competitive service provider, it results in the instant loss of business. This work focuses on building a classification model for predicting customer churn. Four different deep learning models are designed by applying different activation functions on different layers for classifying the customers into two different categories. A comparison of the performance of the different models is done by using various performance measures such as accuracy, precision, recall, and area under the curve (AUC) to determine the best activation function for the model among tanh, ReLU, ELU, and SELU. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
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. -
A new algorithm with its randomness and effectiveness against statistical tests in data encryption
In the world where security is one of the main concern, we are still not able to make our data secure. Privacy is one of the major concerns in todays world, where all the organization are dealing with data leak problem, data theft, data intrusion. We came up with a mathematical model to encrypt and decrypt data securely. In this paper we have came up with a technique to encrypt and decrypt data using non-deterministic random numbers and generating two cipher text for each data unit (character) and verified the randomness of our cipher text using chi-square test, Gaps test. IJSTR 2020. -
A new assessment of quantum key distribution, attenuation and data loss over foggy, misty and humid environment
Quantum encryption is a method of key transfer in cryptography by using quantum entanglement of photons. The real power of quantum entanglement is instantaneous communication that is non intercept able. The advantage of quantum encryption method is, it can be incorporated with conventional encryption methods safely. The quantum cryptography can replace conventional key exchange mechanism with the polarized photons using channels like optic fiber cables. Quantum cryptographic can also provide far and secure data communication. The present day experiments clearly proved that the quantum cryptography can be implemented through medium like optic fiber cable or air. But the distance of transmission through the air is limited by rule of line of sight propagation. The quantum key distribution will have uses in different types of communication between distant parts of earth. So this paper discussing various aspects of Quantum key distribution and successfully calculated polarized photon loss during transmission of Quantum cryptography link, while using in various type of atmospheric conditions like Mist Fog Haze. Also successfully calculated probability of single polarized photon missing by successfully utilizing the Light transmission characteristics and power measurements in various Atmospheric conditions. 2018, UK Simulation Society. All rights reserved. -
A new combinational technique in image steganography
Internet is used for exchanging information. Sometimes it is needed to transmit confidential data via internet. Here the authors use image steganography to pass confidential data within a cover image. To construct the algorithm, they take the combinational help of particle swarm optimization (PSO), bi-orthogonal wavelet transform (BWT), and genetic algorithm (GA). They use PSO to take the enhanced version of cover image. They use BWT to choose the selective sub bands of cover image and we utilize GA to select a particular stego image among a set of stego images. Thus, an innovative technique of image steganography has been made to transmit confidential data via cover image generating stego image. This combinational approach of image steganography is quite safe for confidential data transmission and makes it hard for the attackers to retrieve the confidential data. 2021 IGI Global. All rights reserved. -
A new computational technique for the analytic treatment of time-fractional EmdenFowler equations
This paper presents the study of fractional EmdenFowler (FEF) equations by utilizinga new adequate procedure, specifically the q-homotopy analysis transform method (q-HATM). The EF equation has got greater significance in both physical and mathematical investigation of capillary and nonlinear dispersive gravity waves. The projected technique is tested by considering four illustrations of the time-fractional EF equations. The q-HATM furnish ?, known as an auxiliary parameter, by the support of ? we can modulate the various stages of convergence of the series solution. Additionally, to certify the resolution and accurateness of the proposed method we fitted the suitable numerical simulations. The redeem results guarantee that the proposed process is more convincing and scrutinizes the extremely nonlinear issues emerging in the field of science and engineering. 2021 International Association for Mathematics and Computers in Simulation (IMACS)