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A Novel Back-Propagation Neural Network for Intelligent Cyber-Physical Systems for Wireless Communications
Wireless sensor networks, which play a significant role in monitoring complex environments that change rapidly over time, were used in the Artificial Intelligence method. External factors or the device designers themselves are both responsible for this complex behavior. Sensor networks often use machine learning techniques to adapt to such conditions, eliminating the need for excessive redesign. Cyber-physical systems (CPS) appeared as the promising option for improving physical-virtual interactions. The quality of the system containing processing information is primarily determined by the system function. There are many benefits obtained while combining Artificial Intelligence (AI) and Cyber-Physical Systems (CPSs) in buildings. In CPS-based indoor environment has various design schemes containing measurement and intelligent buildings in the control system consisting of detection, tracking, execution, and communication modules. The Multi-Agent System (MAS) is the smallest control unit that simulates among neurons and it flexibly provides the information. To mimic the interactions between human neurons, multi-agents are used. In this paper, the CPSs information world is built on the fundamental principle of granular formal concepts and the theory of granular computing is investigated. The calculation module is used by Back-Propagation Neural Network (BPNN) for pattern recognition and classification by environmental information. Various parameters namely the normalized root mean square error, peak signal-to-noise ratio, mean square error, and the mean absolute error are chosen as the objective assessment criteria to assess the benefits of the proposed method and the effectiveness of the proposed system is proven. 2024 IETE. -
A novel automated method for the detection of strangers at home using parrot sound
The sound produced by parrots is used to gather information about their behavior. The study of sound variation is important to obtain indirect information about the characteristics of birds. This paper is the first of a series in analyzing bird sounds, and establishing the adequate relation of bird's sound. The paper proposes a probabilistic method for audio feature classification in a short interval of time. It proposes an application of digital sound processing to check whether the parrots behave strangely when a stranger comes. The sound is classified into different classes and the emotions of the birds are analyzed. The time frequency of the signal is checked using spectrogram. It helps to analyze the parrot vocalization. The mechanical origin of the sound and the modulation are deduced from spectrogram. The spectrogram is also used to check the amplitude and frequency modulation of sound and the frequency of the sound are detected and analyzed. This research and its findings will help the bird lovers to know the bird behavior and plan according to that. The greater understanding of birds will help the bird lovers to feed and care for birds. BEIESP. -
A novel automated method for coconut grading based on audioception
The quality of the coconuts used for various purposes is of utmost importance. Demand for better quality products is constantly on the rise due to the improvements in the standard of living of people. There is a possibility that a bad coconut goes unnoticed by the traders, as it is hard to decide if a coconut is good or bad by relying only on its external appearance. Traditionally, quality assessment is carried out manually with the help of three senses; sight, hearing and smell. In the proposed work, a sound processing technique is used in an attempt to automate this process which overcomes the drawbacks of manual processing, which can be used in large godowns and warehouses. This proposed method provides the quality assessment of the coconut purely based on audioception. While creating the database, coconuts varying in size, shape, color and water content were taken from several places as a source for the dataset. Features are extracted from the sound pattern produced by the dropped coconut, which forms the basis for classification. Sequential Minimal Optimization (SMO), Dagging and Naive Bayes classifiers were used and the results obtained were found to be encouraging. 2005 ongoing JATIT & LLS. -
A Novel Auto Encoder- Network- Based Ensemble Technique for Sentiment Analysis Using Tweets on COVID- 19 Data
The advances in digitalization have resulted in social media sites like Twitter and Facebook becoming very popular. People are able to express their opinions on any subject matter freely across the social media networking sites. Sentiment analysis, also termed emotion artificial intelligence or opinion mining, can be considered a technique for analyzing the mood of the general public on any subject matter. Twitter sentiment analysis can be carried out by considering tweets on any subject matter. The objective of this research is to implement a novel algorithm to classify the tweets as positive or negative, based on machine learning, deep learning, the nature inspired algorithm and artificial neural networks. The proposed novel algorithm is an ensemble of the decision tree algorithm, gradient boosting, Logistic Regression and a genetic algorithm based on the auto-encoder technique. The dataset under consideration is tweets on COVID-19 in May 2021. 2024 Taylor & Francis Group, LLC. -
A Novel Assessment of Healthcare Waste Disposal Methods: Intuitionistic Hesitant Fuzzy MULTIMOORA Decision Making Approach
Waste produced from medical facilities systems incorporates a blend of dangerous waste which can posture dangers to humans and ecological receptors. Lacking administration of healthcare waste can prompt hazard to medicinal service specialists, patients, public health, communities and the wider environment. Hence, proper management of healthcare waste is imperative to reduce the associated health and environment risk. In this paper, we extend the MULTIMOORA decision making method with intuitionistic hesitant fuzzy set to evaluate the healthcare waste treatment methods. Intuitionistic hesitant fuzzy set is a generalized form of a hesitant fuzzy set. Intuitionistic hesitant fuzzy set considers the uncertainty of data in a single framework and take more information into account. The MULTIMOORA method consists of three parts namely the ratio system, reference point approach and the full multiplicative form. In the optimal ranking methods, the IHF-MULTIMOORA method is uncomplicated it is able to be used practically with high dimension intuitionistic hesitant fuzzy sets. For pathological, pharmaceutical, sharp, solid and chemical wastes, the preferred waste disposal methods are deep burial, incineration, autoclave, deep burial, and chemical disinfection, respectively. 2013 IEEE. -
A novel assessment of bio-medical waste disposal methods using integrating weighting approach and hesitant fuzzy MOOSRA
Bio-medical waste (BMW) management is highly important precaution for human health and environmental concern. There are several disposal treatment followed by medical practitioners in medical waste management. Here, a few disposal treatment is considered to be an alternatives. When assessing, it is necessary to evaluate and assume that all disposal treatment methods are safe and hygienic. In this way, every alternative assessment is evaluated based on the social acceptance, technology and operation, environmental protection, cost, noise and health risk. Finally the best alternative is chosen. When BMW is disposed and we select the best treatment method in BMW management, it can lead to multi-criteria decision making (MCDM) processes related to uncertain critical assessments. When making a decision, the decision makers having some hesitation to give their suggestions. Therefore, here we use hesitant MCDM method. In today's practice we have choose five methods of BMW disposal methods used in the medical world and we have its alternatives. One of these alternative is sorted by six criteria weights for selecting the best method. The main aim of this research paper is propose a new methodology of hesitant fuzzy weight finding technique, it is named as Hesitant Fuzzy Subjective and Objective Weight Integrated Approach (HF-SOWIA) and also propose a new hesitant fuzzy rank finding methodology, it is named as Hesitant Fuzzy Multi-Objective Optimization on the basis of Simple Ratio Analysis (HF-MOOSRA). After evaluation, the result shows that autoclaving is the best alternative for BMW disposal treatment methods. Furthermore, sensitivity analysis is make in order to observe the difference of alternative ranking when the importance of subjective and objective weights changes. 2020 Elsevier Ltd -
A Novel Artificial Intelligence System for the Prediction of Interstitial Lung Diseases
Interstitial lung disease (ILD) encompasses a spectrum of more than 200 fatal lung disorders affecting the interstitium, contributing to substantial mortality rates. The intricate process of diagnosing ILDs is compounded by their diverse symptomatology and resemblance to other pulmonary conditions. High-resolution computed tomography (HRCT) assumes the role of the primary diagnostic tool for ILD, playing a pivotal role in the medical landscape. In response, this study introduces a computational framework powered by artificial intelligence (AI) to support medical professionals in the identification and classification of ILD from HRCT images. Our dataset comprises 3045 HRCT images sourced from distinct patient cases. The proposed framework presents a novel approach to predicting ILD categories using a two-tier ensemble strategy that integrates outcomes from convolutional neural networks (CNNs), transfer learning, and machine learning (ML) models. This approach outperforms existing methods when evaluated on previously unseen data. Initially, ML models, including Logistic Regression, BayesNet, Stochastic Gradient Descent (SGD), RandomForest, and J48, are deployed to detect ILD based on statistical measures derived from HRCT images. Notably, the J48 model achieves a notable accuracy of 93.08%, with the diagnostic significance of diagonal-wise standard deviation emphasized through feature analysis. Further refinement is achieved through the application of Marker-controlled Watershed Transformation Segmentation and Morphological Masking techniques to HRCT images, elevating accuracy to 95.73% with the J48 model. The computational framework also embraces deep learning techniques, introducing three innovative CNN models that achieve test accuracies of 94.08%, 92.04%, and 93.72%. Additionally, we evaluate five full-training and transfer learning models (InceptionV3, VGG16, MobileNetV2, VGG19, and ResNet50), with the InceptionV3 model achieving peak accuracy at 78.41% for full training and 92.48% for transfer learning. In the concluding phase, a soft-voting ensemble mechanism amplifies training outcomes, yielding ensemble test accuracies of 76.56% for full-training models and 92.81% for transfer learning models. Notably, the ensemble comprising the three newly introduced CNN models attains the pinnacle of test accuracy at 97.42%. This research is poised to drive advancements in ILD diagnosis, presenting a resilient computational framework that enhances accuracy and ultimately betters patient outcomes within the medical domain. 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
A Novel Architecture for a Medical Image Recognition System Using Deep Learning-Based Multiple Regression Evaluation
Models based on machine learning are optimization models that collect data, assess it, and deliver the reports required by specialists and management to make the best decisions. The application of contemporary machine learning allows the organization to quickly analyze photographs, differentiate voices assist in providing customer service, assess the information that is at hand, and uncover connections to aid in decision-making processes. The results of this investigation use quantitative methodologies to collect data and analyze it using mathematical procedures such as regression modeling as well as analysis of variance. Deep learning techniques applied to digital imaging, particularly in medical treatment, can increase picture quality, aid in modeling, aid in making the best possible diagnosis, and successfully address demands from patients. To analyze the hypothesis, investigators intend to utilize statistical approaches such as descriptive data analysis, regression evaluation, and analysis of variance (ANOVA). The authors employ the purposive sample approach to choose respondents from the healthcare industry. Purpose sampling is a non-probability sampling approach. Researchers collected data from 193 respondents working at hospitals that are privately owned in Southern Asia. As stated by the study, all factors, including efficiently meeting patient needs, have a probability value of under 0.05, indicating that they are statistically noteworthy. Following the study, the coefficient of variance (R squared) is 0.744, or 74.4%. According to the study, there is a high association between better image quality and ML-based digital picture identification systems. The recognition of patterns and the application of artificial intelligence to computerized recognition of pictures also have a close link. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A novel approach with matrix based public key crypto systems
Here in this model, a new mechanism is used for Public Key Cryptography. A generator matrix is used to generate a field with a large prime number. The generator matrix, prime number and quaternary vector are used as global variables. The Generator Matrix is powered by a private key to generate Public Key. Since the model is based on Discrete Logarithm Problem, which is Hard problem, the proposed algorithm supports the features like Authenticity of users, Security & Confidentiality of data transmitted. Going by the construction of the algorithm, Encryption is being done on blocks of data for which it consumes less computing resources. Going by complexity of the algorithm, the key length needed is about 72 bit lengths to provide sufficient strengths against crypto analysis. 2017 Taru Publications. -
A novel approach using steganography and cryptography in business intelligence
In the information technology community, communication is a vital issue. And image transfer creates a major role in the communication of data through various insecure channels. Security concerns may forestall the direct sharing of information and how these different gatherings cooperatively direct data mining without penetrating information security presents a challenge. Cryptography includes changing over a message text into an unintelligible figure and steganography inserts message into a spread media and shroud its reality. Both these plans are successfully actualized in images. To facilitate a safer transfer of image, many cryptosystems have been proposed for the image encryption scheme. This chapter proposes an innovative image encryption method that is quicker than the current researches. The secret key is encrypted using an asymmetric cryptographic algorithm and it is embedded in the ciphered image using the LSB technique. Statistical analysis of the proposed approach shows that the researcher's approach is faster and has optimal accuracy. 2021, IGI Global. -
A Novel Approach towards Key-point based Real-time Children Emotion Prediction
Emotion prediction is crucial in mental healthcare. It is vital in children as it aids in managing behavioral issues and early identification of emotional distress that can lead to helpful mental health support. The research uniquely centres on children's emotional expressions, addressing a gap in existing emotion detection studies, which often focus on adults. By specifically tailoring the model to recognize subtle expressions unique to children, the study contributes valuable insights into child psychology and emotion recognition. To address this gap, this work attempts to establish a comprehensive children's emotion dataset that can facilitate the study of emotions across various pose orientation. The approach introduces advanced key-point detection techniques that capture a higher density of facial landmarks, allowing for more nuanced analysis of emotional expressions. This fine-grained detection enables the identification of subtle changes that are critical in interpreting children's emotions. An effective face detector with deep architecture is designed to handle all pose orientations from key image frames. Optimal features are then chosen by re-ranking the features using a hybrid feature selection mechanism. The emotion category is revealed by careful analysis of sequences of emotion identification from these features and is not based on a single frame. This framework holds promise for educational institutions and healthcare facilities, offering insights into children's behavior through emotion analysis. Through experimental analysis and comparisons with three existing SOTA emotion prediction models, it is observed that the proposed system consistently outperforms existing models by exhibiting an accuracy of 77.7 on average. Overall, this study recommended that the proposed model is suitable for children's emotion prediction. 2025 The Author(s). -
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 -
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 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 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 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 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 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 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 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.