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A Novel AI-Based Stock Market Prediction Using Machine Learning Algorithm
The time series forecasting system can be used for investments in a safe environment with minimized chances of loss. The Holt-Winters algorithm followed various procedures and observed the multiple factors applied to the neural network. The final module helps filter the system to predict the various factors and provides a rating for the system. This research work uses real-time dataset of fifteen stocks as input into the system and, based on the data, predicts or forecasts future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting will give an accurate result for the customer investments. 2022 Iyyappan. M et al. -
A Novel and Efficient Deep Learning Models for Assessing AIs Impact on Disease Diagnosis in Agriculture
Background: Agriculture sustains human life by providing food, raw materials and employment opportunities. However, climate change and resource limitations pose significant challenges to crop production. AI-driven smart farming has emerged as a solution to enhance agricultural efficiency, with Explainable AI (XAI) improving transparency in decision-making. Innovations such as smart sensors and automated systems have benefited key agricultural sectors, including crops, forestry, livestock and aquaculture. Turmeric, valued for its medicinal and economic significance, requires careful monitoring to combat diseases like leaf spot and leaf blotch, which can impact yield and quality. Methods: This study introduces turmeric net, a Convolutional Neural Network (CNN)-based model leveraging transfer learning to detect and classify turmeric leaf diseases. The dataset used consists of 791 original images and 3,702 augmented images obtained from mendeley data, categorized into four classes: healthy leaf, dry leaf, leaf blotch and rhizome rot. The model development was carried out using TensorFlow, with ResNet50V2 as a baseline for comparison. The models were trained on processed image data, incorporating augmentation techniques to improve robustness and generalizability. Result: The accuracy of both models was evaluated. ResNet50V2 achieved an accuracy exceeding 99%, demonstrating high effectiveness in disease classification. Meanwhile, TurmericNet attained a competitive accuracy of 98%, making it a reliable alternative for turmeric disease identification. These results indicate that deep learning-based models can significantly aid in early disease detection, providing farmers with a valuable tool to enhance crop management and productivity. 2025 , Agricultural Research Communication Centre. All rigths reserved. -
A novel and secured bitcoin method for identification of counterfeit goods in logistics supply management within online shopping
Counterfeit merchandise poses significant challenges for both consumers and retailers. When counterfeit goods infiltrate the market, they damage the trustworthiness and reputation of legitimate companies, leading to negative publicity. Furthermore, these imitations can be harmful, especially in critical sectors like food and pharmaceuticals. To address this issue, it is essential to identify and prevent counterfeit products from reaching consumers. Our proposed solution leverages blockchain technology to authenticate products. Blockchains decentralized database securely stores all transaction data, ensuring transparency and traceability. Additionally, we introduce a tool that records ownership and product details. By utilizing a Quick Response (QR) code, consumers can easily verify the authenticity of a product, thus accessing its manufacturing and ownership information. This approach not only safeguards consumer safety but also protects the reputation and financial performance of legitimate business. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
A Novel Approach for Detection and Recognition of Traffic Signs for Automatic Driver Assistance System Under Cluttered Background
Traffic sign detection and recognition is a core phase of Driver Assistance and Monitoring System. This paper focuses on the development of an intelligent driver assistance system there by achieving road safty. In this paper a novel system is proposed to detect and classify traffic signs such as warning and compulsory signs even for occluded and angular tilt images using Support Vector Machines. Exhaustive experiments are performed in order to demonstrate the efficiency of proposed method. 2019, Springer Nature Singapore Pte Ltd. -
A Novel Approach for Fractional (1 + 1 ) -Dimensional BiswasMilovic Equation
In this paper, we find the solution for (1 + 1 ) -dimensional fractional Biswas-Milovic (FBM) equation using the q-homotopy analysis transform method (q-HATM). The Biswas-Milovic equation is a generalization of the nonlinear Schringer (NLS) equation. The future scheme is the elegant mixture of q-homotopy analysis scheme with Laplace transform technique and fractional derivative considered in Caputo sense. To validate and illustrate the competence of the method, we examine the projected model in terms of arbitrary order. Moreover, the nature of the attained results have been presented in 3D plots and contour plots for different value of the order. The gained consequences show that, the hired algorithm is highly accurate, easy to implement, and very operative to investigate the nature of complex nonlinear models ascended in science and engineering. 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
A Novel Approach for Implementing Conventional LBIST by High Execution Microprocessors
The major VLS I circuits like sequential circuits, linear chips and op amps are very important elements to provide many logic functions. Today's competitive devices like cell phone, tabs and note pads are most prominent and those are used to get function the 5G related operations. In this work lower built-in self-test (LBIS T) mechanism is used to designing a microprocessor. The proposed methodology is giving performance measure like power efficiency 97.5%, improvement of delay is 2.5% and 32% development of area had been attained. This methodology attains more out performance and compete with present technology. The proposed equipment and execution for our approach requiring a constrained range overhead (lower than 3% power) over conventional LBIS T. 2022 IEEE -
A novel approach for integrating cryptography and blockchain into IoT system
The quick advancement of Internet of Things (IoT) emphasizes the significance of cryptography and blockchain ensuring the security of sensitive data and connected devices. Blockchain technology and encryption play key roles in ensuring the security of the expansive IoT network. Blockchain offers decentralized trust, immutability, and transparency to IoT networks and transactions, while encryption serves to protect IoT data from unauthorized access. It is a novel approach for integrating cryptography and blockchain into IoT System, cryptography and blockchain stand out as robust technologies that enhance the security of IoT systems. The implementation of an integrated architecture, along with a strategic integration approach, further strengthens the security measures. This methodology proves valuable for managing and validating digital transactions on decentralized, immutable networks. This work also explores the potential significance of integrating cryptography and blockchain into IoT System, this functions and applications in enhancing IoT security. This methodology introduces encryption techniques tailored for resource-constrained IoT devices, which are essential for ensuring end-to-end security. 2024, Taru Publications. All rights reserved. -
A Novel Approach for Linguistic Steganography Evaluation Based on Artificial Neural Networks
Increasing prevalence and simplicity of using Artificial Intelligence (AI) techniques, Steganography is shifting from conventional model building to AI model building. AI enables computers to learn from their mistakes, adapt to emerging inputs, and carry out human-like activities. Traditional Linguistic Steganographic approaches lack automation, analysis of Cover text and hidden text volume and accuracy. A formal methodology is used in only a few Steganographic approaches. In the vast majority of situations, traditional approaches fail to survive third-party vulnerability. This study looks at evaluation of an AI-based statistical language model for text Steganography. Since the advent of Natural Language Processing (NLP) into the research field, linguistic Steganography has superseded other types of Steganography. This paper proposes the positive aspects of NLP-based Markov chain model for an auto-generative cover text. The embedding rate, volume, and other attributes of Recurrent Neural Networks (RNN) Steganographic schemes are contrasted in this article between RNN-Stega and RNN-generated Lyrics, two RNN methods. Here the RNN model follows Long Short Term Memory (LSTM) neural network. The paper also includes a case study on Artificial Intelligence and Information Security, which discusses history, applications, AI challenges, and how AI can help with security threats and vulnerabilities. The final portion is dedicated to the study's shortcomings, which may be the subject of future research. 2013 IEEE. -
A Novel Approach for Machine Reading Comprehension using BERT-based Large Language Models
Teaching machines to learn the information from the natural language documents remains an arduous task because it involves natural language understanding of contexts, excerpting the meaningful insights, and deliver the answer to the questions. Machine Reading Comprehension (MRC) tasks can identify and understand the content from the natural language documents by asking the model to answer questions. The model can extract the answer from the given context or other external repositories. This article proposes a novel Context Minimization method for MRC Span Extraction tasks to improve the accuracy of the models. The Context Minimization method constitutes two subprocedures, Context Reduction and Sentence Aggregation. The proposed model reduced the context with the most relevant sequences for answering by estimating the sentence embedding between the question and the sequences involved in the context. The Context Reduction facilitates the model to retrieve the answer efficiently from the minimal context. The Sentence Aggregation improves the quality of answers by aggregating the most relevant shreds of evidence from the context. Both methods have been developed from the two notable observations from the empirical analysis of existing models. First, the models with minimal context with efficient masking can improve the accuracy and the second is the issue with the scatted sequences on the context that may lead to partial or incomplete answering. The Context Minimization method with Fine-Tuned BERT model compared with the ALBERT, DistilBERT, and Longformer models and the experiments with these models have shown significant improvement in results. 2024 IEEE. -
A Novel Approach for Segmenting Coronary Artery from Angiogram Videos
This paper addresses the research focuses on coronary artery disease; it is one of the major heart diseases affecting the people all around the world in the recent era. This heart disease is primarily diagnosed using a medical test called angiogram test. During the angiogram procedure the cardiologist often physically selects the frame from the angiogram video to diagnose the coronary artery disease. Due to the waning and waxing changeover in the angiogram video, its hard for the cardiologist to identify the artery structure from the frame. So, finding the keyframe which has a complete artery structure is difficult for the cardiologist. To help the cardiologist a method is proposed, to detect the keyframe which has segmented artery from the angiogram video. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A novel approach for the synthesis of functionalized hydroxylamino derivative of dihydroquinazolinones
A new metal-free and modular approach for the synthesis of various functionalized dihydroquinazolinones has been developed from isatoic anhydride, amines, 4-chloro-N-hydroxybenzimidoylchloride to yield up to 71%. The reaction has been screened in various bases, solvents at different temperatures. The substrate scope of the reaction has been studied with various amines and the possible reaction mechanism for this reaction has also been proposed. 2020, 2020 Taylor & Francis Group, LLC. -
A Novel Approach for Web Mining Taxonomy for High-Performance Computing
Web mining is a central part of data analysis. The fetching and discovering knowledge from the different web data in data mining mechanism is more important nowadays. Web usage mining customs data mining practice for the investigation of custom decoration from different data storages. In this article paper, introducing a new approach for web mining taxonomy for high-performance computing. The primary motivation of this research is on the data collection in different real-time web servers for implementation and analysis. This article is focussed the WebLog Expert lite 9.3 tools for our study. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A novel approach in prediction of crop production using recurrent cuckoo search optimization neural networks
Data mining is an information exploration methodology with fascinating and understand-able patterns and informative models for vast volumes of data. Agricultural productivity growth is the key to poverty alleviation. However, due to a lack of proper technical guidance in the agriculture field, crop yield differs over different years. Mining techniques were implemented in different applications, such as soil classification, rainfall prediction, and weather forecast, separately. It is proposed that an Artificial Intelligence system can combine the mined extracts of various factors such as soil, rainfall, and crop production to predict the market value to be developed. Smart analysis and a comprehensive prediction model in agriculture helps the farmer to yield the right crops at the right time. The main benefits of the proposed system are as follows: Yielding the right crop at the right time, balancing crop production, economy growth, and planning to reduce crop scarcity. Initially, the database is collected, and the input dataset is preprocessed. Feature selection is carried out followed by feature extraction techniques. The best features were then optimized using the recurrent cuckoo search optimization algorithm, then the optimized output can be given as an input for the process of classification. The classification process is conducted using the Discrete DBN? VGGNet classifier. The performance estimation is made to prove the effectiveness of the proposed scheme. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
A Novel Approach to Automatic Ear Detection Using Banana Wavelets and Circular Hough Transform
Ear is an attractive biometric trait that maintain their structure with increasing age. Because of the complex geometry of ear, its detection is very difficult. This paper proposes a modified algorithm for automatic detection of 2D ear images using Banana wavelets and Hough transform. Banana wavelets derived from bank of stretched and curved Gabor wavelets are used to identify curvilinear ear structure. Addition of a preprocessing stage, prior to application of banana wavelets is found to improve the detection results further. The proposed algorithm is brought in to comparison with three existing algorithms and evaluated on standard databases. In addition to manual detection accuracy, this paper also calculates the efficiency of the proposed method using automatic classification techniques. The features like LBP and Gabor extracted from segmented ear image is used by different classifiers to determine whether the segmented portion of the image is class Ear or Non ear. 2019 IEEE. -
A Novel Approach to Enhance Influencer Marketing in E-commerce: A Cross-A-Siamese Perspective
One of the most notable aspects of the Internet is the fact that the cost of (global) communication has been drastically decreased. Individuals may potentially reach massive audiences with their messages over the Internet due to its widespread use. With the rise of blog services, social networking platforms, etc., people's technological talents are no longer a limiting factor. Data preprocessing, feature selection, and model training should all be done in this sequence of significance. Applying fundamental data preparation techniques guaranteed the data's accuracy and relevancy. Feature selection includes the computation of an influencer's overall rank based on six important criteria, which are used for influencer identification and ranking. Feature retrieval is the first step in training Unified Cross-A-Siamese models. The proposed method outperforms two cutting-edge methods: Attention module and siamese. Accuracy increased by 95.70 percent once the approach was used. 2024 IEEE. -
A novel approach to optimize power utilization and scheduling in dynamic networks through generative adversarial network-based prediction of network parameters
The infrastructure-less network communication has been in an ever-increasing demand to cater to the needs of effective communication while the network dynamism exists. The quality of service (QoS)quality of service (QoS) demands increasing the efficiency of network by reducing the time taken for a data packet to reach the destination, increasing the probability of successful data transmissiondata transmission, minimizing packet loss,packet loss and optimizing power utilizationpower utilization. In this study, a generative adversarial network-based learning modelgenerative adversarial network-based learning model has been developed that considers the previous network statistics, as realized data, to predict future network patterns by the generatorgenerator to make such predictions, called as unrealized data, as near to the realized data. Further, the proposed model uses penalty-award criteria by the discriminatordiscriminator, to fine-tune the predicted network parameters. Now, having the set of realized and unrealized data, the model uses Markov decision processMarkov decision process to perform power scheduling and effective utilization of buffer space. The buffer utilization in the intermediate nodes necessitates the model to stochastically schedule the data transmission, depending on the percentage of utilization of buffer. Simulation results denote the effective utilization of buffer that makes continued transmission of data, whenever possible, without having data packet lossdata packet loss. Also, power scheduling, by the use of goodput function and increased transmission probability improves the power utilization that ultimately increases the lifetime of the network. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin. -
A Novel Approach to Optimizing Third-Party Logistics Growth through IT, Big Data, and Machine Learning for Superior Supply Chain Management
The purpose of this research is to explore proper development prospects of 3PL business, particularly focusing on the utilization of Information Technology and big data technologies for improving the solidity of supply chains. The change of the industry that started from the provision of services and then became an integrated solution implies the rising role of IT as one of the means for supply chain improvement. Based on the market study and investigation of customers' behaviors, as well as the contexts of the 3PL industry of the India, this research outlines how the IT and big data analytics can contribute to the operations improvement and innovation of the 3PL. Besides, this paper aims at finding out whether the Big Data analytics can enhance the competitiveness of third party logistics providers in a volatile market. 2024 IEEE. -
A Novel Approach to Packet Dropping and Malicious Attack Detection using Ensemble Techniques
Packet-dropping attacks interrupt data transfer while damaging security protocols, which create a threat to wireless Sensor Networks and Mobile Ad Hoc networks. This paper examines packet-dropping detection methods as well as security attack identification since these threats represent significant risks to networks such as Wireless sensor networks and Mobile Ad Hoc Networks. The research paper utilized a dataset from Kaggle for network traffic analysis, which classified packets through their behaviors as either abnormal or normal. The detection employed a stacking classifier with logistic regression as the meta-classifier and Support Vector Machine, Gradient Boosting, and K-Nearest Neighbour as its main constituents. The analysis model showed high detection rates for packet-dropping incidents, reaching 93.5%, and for malicious attacks, reaching 98.2%, based on the experimental test results. The obtained data shows that stacking models show stable reliability levels above traditional approaches. Ensemble learning proves effective for discovering cyber threats through results that reduce the number of incorrect detections. The stacking classifier functions as a dependable framework for developing security measures required to protect computer networks from modern-day threats. 2025 IEEE. -
A Novel Approach to Predicting the Risk of Illegal Activity and Evaluating Law Enforcement Using WideDeep SGRU Model
The main reaction to the illicit extraction of natural resources in protected areas around the world is law enforcement patrols headed by rangers. On the other hand, research on patrols' efficacy in reducing criminal behavior is lacking. Similarly, tactics to enhance the effectiveness of patrol organization and monitoring have received very little attention. Sequencing is crucial for model training, feature selection, and preprocessing. Preprocessing steps include cleaning, discretizing, duplicating, and normalizing data. Feature selection makes use of genetic algorithms, which are basically search algorithms with an evolutionary bent that factor in natural selection and genetics. Training stacked GRU models necessitates meticulous feature management. Even the most cutting-edge algorithms, GRU and BiGRU, are no match for the suggested technique. An astounding 97.24% accuracy grade was disclosed by the results, showcasing exceptional growth. 2024 IEEE. -
A novel approach to study generalized coupled cubic SchringerKorteweg-de Vries equations
The Kortewegde Vries (KdV) equation represents the propagation of long waves in dispersive media, whereas the cubic nonlinear Schringer (CNLS) equation depicts the dynamics of narrow-bandwidth wave packets consisting of short dispersive waves. A model that couples these two equations seems intriguing for simulating the interaction of long and short waves, which is important in many domains of applied sciences and engineering, and such a system has been investigated in recent decades. This work uses a modified Sardar sub-equation procedure to secure the soliton-type solutions of the generalized cubic nonlinear SchringerKorteweg-de Vries system of equations. For various selections of arbitrary parameters in these solutions, the dynamic properties of some acquired solutions are represented graphically and analyzed. In particular, the dynamics of the bright solitons, dark solitons, mixed bright-dark solitons, W-shaped solitons, M-shaped solitons, periodic waves, and other soliton-type solutions. Our results demonstrated that the proposed technique is highly efficient and effective for the aforementioned problems, as well as other nonlinear problems that may arise in the fields of mathematical physics and engineering. 2022