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Enhancing Retailer Auctions and Analyzing the Impact of Coupon Offers on Customer Engagement and Sales Through Machine Learning
Systems that use coupons have been used extensively to boost customer interaction on platforms having a digital component. We use causal machine learning techniques to determine the effect of an advertising intervention, especially a discount offer, on the bids of a shop. Discount shopping coupons are a popular tactic for increasing sales. The largest challenge for dealers is accurately anticipating the wants of their customers, and here is where they always struggle. Machine learning algorithms have been utilized by researchers to address a variety of problems. Selecting the right coupon is a challenging undertaking because every customer's behavior differs depending on the deal. Due to categorical data adjustments being necessary due to the majority of characteristics having missing values, the situation is made more difficult. The dataset is used to classify the dataset, and machine learning algorithms like logistic regression, random forest and SVM model, decision tree and naive bayes models are used to determine the correctness of the classification. 2023 IEEE. -
On C-Perfection of Tensor Product of Graphs
A graph G is C-perfect if, for each induced subgraph H in G, the induced cycle independence number of H is equal to its induced cycle covering number. Here, the induced cycle independence number of a graph G is the cardinality of the largest vertex subset of G, whose elements do not share a common induced cycle, and induced cycle covering number is the minimum number of induced cycles in G that covers the vertex set of G. C-perfect graphs are characterized as series-parallel graphs that do not contain any induced subdivisions of K2,3, in literature. They are also isomorphic to the class of graphs that has an IC-tree. In this article, we examine the C-perfection of tensor product of graphs, also called direct product or Kronecker product. The structural properties of C-perfect tensor product of graphs are studied. Further, a characterization for C-perfect tensor product of graphs is obtained. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Utilizing Artificial Intelligence-Powered Chatbots for Enhanced Customer Support in Online Retail
In many e-commerce contexts, live chat interfaces have become popular as a way to communicate with consumers and provide real-time customer support. Conversational software agents, commonly known as Chatbots, are systems created to converse with users in natural language and are often based on artificial intelligence (AI). These systems have replaced human chat service agents in many cases. Although AI -based Chatbots have been widely used due to their time and cost savings, they have not yet met consumer expectations, which may make users less likely to comply with chatbot requests. We empirically study, through a randomized online experiment, the impact of verbal humanoid design cues and a direct approach on compliance with user requirements, based on Social Reactions and Attachment Commitment Theory. Our results show that consumers are more likely to cooperate with chatbot service response requests when there is humanity and consistency. Furthermore, the results demonstrate that social presence plays a mediating role between humanoid design cues and user compliance. 2024 IEEE. -
Impact of Variable Viscosity and Gravity Variations on Rayleigh-Bard Instabilities of Viscoelastic Liquids in Energy Sustainable System
Energy sustainability systems are vital for transitioning to a low-carbon economy, addressing climate change, and ensuring a sustainable future for all. Rayleigh-Bard convection (RBC) in viscoelastic liquids is a crucial phenomenon in various industrial and environmental applications, including energy sustainability systems where fluid dynamics play a pivotal role in optimizing heat transfer and system efficiency. The study deals with the combined influence of variable viscosity and variable gravity on RBC in viscoelastic liquids. The influence of space-dependent gravity on the onset of convection is considered. The results are analyzed against the background of constant gravity RBC in viscoelastic/Newtonian liquids with constant/variable viscosity. The possibility of variable gravity accelerating/decelerating the onset of convective instability is examined in this paper. 2024 IEEE. -
Cloud Enabled Smart Firefighting Drone Using Internet of Things
Internet of Things is fasted booming sector. This technology is evolved in various fields. The frequent updates in concerning the progress of Skyscraper fire or high-rise fire it is essential for us to ensure effective and safe firefighting. Since high-rise fire is typically inaccessible by ground vehicles due to some constraints or parameters. Due to less advancement in technology most skyscrapers are not furnished with proper fire monitoring and prevention system. To solve this issue this article is propose Unmanned Air Vehicles (UAVs) are making an appearance and making promises to prevent such kind of incidents. In this system, UAV can be launched from the Fire Control Unit (FCU). The proposed methodology is implemented with the help of Internet of Things (IoT). Sensors which are installed at the skyscraper detects the presence of fire and immediately send stress signals to the command and control unit from where further possible action can be taken. The pilot at the fire control unit continuously monitors the flight path and receives the video and fire scan information from the UAV. Upon detection of a stress signal or fire signal the Skyscraper position is determined with the help of Global Positioning System (GPS) and permission is requested from the applicable security agency to launch the extinguisher vehicle. The permission is granted, the coordinates of the location are filled in the system and the nearest station sends the UAV to the location. The fire suppressant are deployed it comes back to the nearest landing location and re-loaded with another fire suppressant to be carried to the fire location. The proposed methodology should improve the Quality of Service. 2019 IEEE. -
Deep Learning Algorithms Comparison forMultiple Biological Sequences Alignment
In this paper, deep learning algorithms are compared for aligning multiple biological molecular sequences such as DNA, RNA, and protein. Efficient algorithms are necessary for sequence alignment to identify significant insights, but there is a trade-off between time and accuracy. This study compares deep learning algorithms for multiple sequence alignment with better accuracy, using a new similarity measure to choose the best resemblance sequences in a set. Using a benchmark dataset, the algorithms compared include CNN, VAE, MLPNN, DBNs, Deep Boltzmann Machine, and GAN. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Bioprospecting of Fungal Endophytes in Hulimavu Lake for Their Repertoire of Bioactive Compounds
Fungal endophytes hold a prominent position in the research world, in part due to the rich repertoire of bioactive compounds useful for industrial and environmental applications. The present study aims at bioprospecting few endophytic fungi isolated from Hulimavu lake flora (Bengaluru) for characterization of biological applications of their bioactive compounds. Among the lake plants screened, Alternanthera philoxeroides, Ricinus communis and Persicaria glabra were taken forward for isolation of fungal endophytes. Subsequent biochemical analyses were performed to quantify few fungal enzymes and bioactive compounds, followed by antimicrobial and cytotoxic assays. In conclusion, this pilot study aims to probe the plethora of bioactive compounds present in fungal endophytes that possess wide ranging biological properties. Due to the species richness and diversity of fungal endophytes across different host plants and habitats, bioprospecting fungal endophytes remains a very extensive yet promising topic for research, representing broad ranging environmental and industrial applications. The Electrochemical Society -
On near-perfect numbers with five prime factors
Let n be a positive integer and ?(n) the sum of all the positive divisors of n. We call n a near-perfect number with redundant divisor d if ?(n) = 2n + d. Let n be an odd near-perfect number of the form n = pa11 ? pa22 ? pa33 ? pa44 ? pa55 where pis are odd primes and ais (1 ? i ? 5) are positive integers. In this article, we prove that 3 | n and one of 5, 7, 11 | n. We also show that there exists no odd near-perfect number when n = 3a1 ? 7a2 ? pa33 ? pa44 ? pa55 with p3 ? {17, 19} and when n = 3a1 ? 11a2 ? pa33 ? pa44 ? pa55 Mathematical and Computational Sciences - Proceedings of the ICRTMPCS International Conference 2023.All rights reserved. -
A Systematic Survey of Happiness from an Analytical Perspective
The paper is a survey paper that talks about studies around happiness. We have surveyed papers about the scales of measuring happiness, in which the scales are proposed, demonstrated and examined. Happiness is affected by various factors, which can be called indicators of happiness. Some of the papers we reviewed validate the significance of such indicators with applications. The indicators include inflation, unemployment, health, loneliness, and sports. Modern technology helps researchers estimate and forecast happiness and effectively find the relation between factors affecting happiness. Researchers use different methodologies to study happiness. The data used in the papers were retrieved from surveys and existing Happiness Report, designed surveys appropriate for the study. Models were proposed for forecasting happiness using Machine Learning and Neural Networks. From the reviews, we identify research gaps in the area for future work. This paper gives an overview of the studies around the area of happiness from an analytical approach. 2022 IEEE. -
Secure Identity Based Authentication for Emergency Communications
The Vehicular Ad Hoc Network (VANET) offers secure data transmission between vehicles with the support of reliable authorities and RSUs. RSUs are fully damaged in emergency scenarios like natural catastrophes and are unable to provide the needed services. Vehicles in this scenario must communicate safely without RSUs. Hence, this study suggests a secure and reliable identity-based authentication technique for emergency scenarios. To provide secure vehicle-to-vehicle communication without RSUs, ECC-based IBS is utilized. Additionally, it offers security features like message integrity, privacy protection, and authentication. It is also resistant to attacks depending on authentication and privacy. The proposed technique performs efficiently with less communication and computing cost when its performance is compared with recent schemes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Secure IBS Scheme for Vehicular Ad Hoc Networks
Vehicular Ad hoc Networks (VANET) havedrastically grown in recent years since they provide a better and more secure driving experience. Due to its characteristics, it is vulnerable to many security attacks. Even though many authentication schemes are proposed, their overheads are high. Hence, this study proposes a new Identity-Based Signature (IBS) for authentication with privacy-preservation. It supports secure communications with additional security features. It requires less overhead since it uses XOR operations and one-way hash functions for the signing and verification process. When the proposed schemes performance is compared to the recent schemes, it is observed that the proposed approach is more efficient in computation and communication. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Intelligent approach to automate a system for simulation of nanomaterials
Nanomaterial composites are generally found to have great thermal properties and hence have witnessed an increasing demand in the recent years for manufacturing of efficient miniature electronic devices. The process of finding the right composites that exhibit the desired properties is a rather tedious task involving a lot of trial and error in the current scenario. This paper proposes a methodology to digitize and automate this entire process by administering certain efficient practices of assessing the properties of nanomaterial like Coarse Grained Molecular Dynamics thus resulting in faster simulations. 2023 Author(s). -
Multimodal Emotion Recognition Using Deep Learning Techniques
Humans have the ability to perceive and depict a wide range of emotions. There are various models that can recognize seven primary emotions from facial expressions (joyful, gloomy, annoyed, dreadful, wonder, antipathy, and impartial). This can be accomplished by observing various activities such as facial muscle movements, speech, hand gestures, and so forth. Automatic emotion recognition is a significant issue that has been a hotly debated research topic in recent years. At the moment, several research people have taken a component in inheriting or extra multimodal for higher understanding. This paper indicates a method for emotion recognition that makes use of 3 modalities: facial images, audio indicators, and text detection from FER and CK+, RAVDESS, and Twitter tweets datasets, respectively. The CNN model achieved 66.67 percent on the FER-2013 dataset of labeled headshots while on the CK+ dataset, 98.4 percent accuracy was obtained. Finally, diverse fusion strategies had been approached, and each of those fusion techniques gave distinctive results. This project is a step towards the sense of interaction between human emotional aspects and the growing technology that is the future of development in today's world. 2022 IEEE. -
Deep Convolutional Neural Networks Network with Transfer Learning for Image-Based Malware Analysis
The complexity of classifying malware is high since it may take many forms and is constantly changing. With the help of transfer learning and easy access to massive data, neural networks may be able to easily manage this problem. This exploratory work aspires to swiftly and precisely classify malware shown as grayscale images into their various families. The VGG-16 model, which had already been trained, was used together with a learning algorithm, and the resulting accuracy was 88.40%. Additionally, the Inception-V3 algorithm for classifying malicious images into family members did significantly improve their unique approach when compared with the ResNet-50. The proposed model developed using a convolution neural network outperformed the others and correctly identified malware classification 94.7% of the time. Obtaining an F1-score of 0.93, our model outperformed the industry-standard VGG-16, ResNet-50, and Inception-V3. When VGG-16 was tuned incorrectly, however, it lost many of its parameters and performed poorly. Overall, the malware classification problem is eased by the approach of converting it to images and then classifying the generated images. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Low cost ANN based MPPT for the mismatched PV modules
Due to manufacturing dispersal, the photovoltaic (PV) panels of similar rating and manufacturer have distinctive characteristics in practical. As the maximum power point tracking (MPPT) becomes essential to optimally utilize the solar PV panel, distributed maximum power point tracking (DMPPT) is considered in this paper to follow the MPP of each panel. As the common MPP value is used in the existing DMPPT method to control all the panels, it fails to consider the uniqueness of each panel. By considering the uniqueness of each panel, the ANN based MPPT is implemented in this paper. As the ANN is trained using the actual characteristics of each panel based on the operating current, voltage and temperature, it is able to track the actual MPP. Due to the solar irradiance free MPPT, the costly pyranometer is not required in the actual PV system for MPPT. It reduces the cost of the system and also provides the interruption free tracking due to its independent nature on Voc and Isc values. Also, because of the looping free behaviour of the proposed algorithm, it is capable of following the MPP at rapidly varying condition. The proposed technique and the verified outcomes are discussed here in detail. Published under licence by IOP Publishing Ltd. -
Model Selection Procedure in Alleviating Drawbacks of the Electronic Whiteboard
Deep learning has paved the way for critical and revolutionary applications in almost every field of life in general. Ranging from engineering to healthcare, machine learning and deep learning has left its mark as the state-of-the-art technology application which holds the epitome of a reasonable high benchmarked solution. Incorporating neural network architectures into applications has become a common part of any software development process. In this paper, we perform a comparative analysis on the different transfer learning approaches in the domain of hand-written digit recognition. We use two performance measures, loss and accuracy. We later visualize the different results for the training and validation datasets and reach to a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with simultaneous focus on the suitable model selection procedure for the digit recognition problem. 2021 IEEE. -
Blockchain-Enabled Smart Contracts in Agriculture: Enhancing Trust and Efficiency
This study explores the important role of blockchain technology in the transformation of agriculture and presents a new way to integrate chatbots and smart contracts to solve the problem of persistence. Leverage the decentralized structure and security of the blockchain to increase traceability, transparency and fairness in agricultural product prices. A user-friendly chatbot built in Python using Tkinter that acts as a bridge between farmers and the Ethereum-based blockchain pricing algorithm. Smart contracts used in Solidity dynamically adjust crop prices based on the weather in real time, making it possible for prices to react and adjust. Simulations and tests in Ganache validate the proposed method, confirming its economic value and effectiveness in many agricultural cultures. This study delves into analytics, including latency and production time, to demonstrate the benefits of the blockchain model in creating transparent, farmer-centric and region-specific crop prices. The importance of this research is to support continuous change in agricultural technology, paving the way for the introduction of appropriate and fair prices. According to the amendment, the integration of advanced machine learning, further integration and collaboration with agricultural stakeholders should be developed in the future. This work sets a good path for agriculture, promoting transparency, fairness and quick access to the best crop prices, thus ensuring security and agricultural technology. 2024 IEEE. -
Accessing Accurate Documents by Mining Auxiliary Document Information
Earlier techniques of text mining included algorithms like k-means, Nae Bayes, SVM which classify and cluster the text document for mining relevant information about the documents. The need for improving the mining techniques has us searching for techniques using the available algorithms. This paper proposes one technique which uses the auxiliary information that is present inside the text documents to improve the mining. This auxiliary information can be a description to the content. This information can be either useful or completely useless for mining. The user should assess the worth of the auxiliary information before considering this technique for text mining. In this paper, a combination of classical clustering algorithms is used to mine the datasets. The algorithm runs in two stages which carry out mining at different levels of abstraction. The clustered documents would then be classified based on the necessary groups. The proposed technique is aimed at improved results of document clustering. 2015 IEEE. -
Integrating AI Tools into HRM to Promote Green HRM Practices
The image of Human Resource Management (HRM) is undergoing a drastic transformation. The conventional methods are evolving due to the emergence of technology, especially with the integration of Artificial Intelligence (AI) and data analytics into the HR processes. With the rapidly changing concept of the overall growth of an organization, AI is becoming a vital stimulant for sustainable growth. AI-powered tools promote data-driven decision-making for talent acquisition, performance management, workforce training and development, optimization of energy consumption and waste reduction. Green HRM aligns these efforts by integrating sustainability considerations into talent management strategies, nurturing employees eco-engagement, and promoting environmentally responsible practices within the workforce. This research paper aims to explore the synergies between AI tools and Green HRM practices, investigating how the integration of AI technologies into HR processes can contribute to the promotion of environmental sustainability. By examining real-world case studies, this study aims to investigate the potential of AI-powered solutions in shaping the future of HRM through the lens of sustainability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
On Two-Dimensional Approximate Pattern Matching Using Fuzzy Automata
Pattern matching has been extensively studied in the last few decades, owing to its great contribution in various fields such as search engines, computational biology, etc. Several real-life situations require patterns that allow ambiguity in specified positions. In this paper, one-dimensional and two-dimensional approximate pattern matching models have been constructed using fuzzy automata. The similarity function used in fuzzy automata enables the occurrence of all exact and similar one-dimensional and two-dimensional patterns. This kind of searching approximate patterns is not possible with regular search models. The time complexity of the proposed algorithm has also been analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.