This study presents an advanced system for detecting skin cancer using Convolutional Neural Networks (CNNs), enhanced by Batch Normalization to improve model stability during training. CNNs, widely recognized for their effectiveness in image analysis, form the foundation of this system, which is designed to address the global challenge of skin cancer detection. The model's capacity to manage a variety of datasets, providing enhanced adaptability, is one of its primary characteristics. It tackles the common issue of imbalanced skin cancer data by employing techniques such as SMOTE, undersampling, and oversampling, resulting in increased accuracy and sensitivity, particularly for less common cases. Comparative experiments demonstrate that this model surpasses previous benchmarks in identifying skin disorders. The integration of Group Normalization further boosts stability, and the combined methods for addressing data imbalances enhance the model's ability to generalize across varied data. This makes the system a highly valuable tool for healthcare professionals. Experimental evaluation on the HAM10000 dataset achieved a test accuracy of 96.4%, a training accuracy of 99.74%, and a validation accuracy of 96.35%, with a minimal loss of 0.0079. The adaptive data balancing strategy further enhanced classification, improving F1-scores by 12–15% for rare classes such as melanoma and dermatofibroma, while preserving 98.2% accuracy for majority classes. The study underscores the potential of modern deep learning techniques to transform the interpretation of medical images, setting a new standard to combating skin diseases in healthcare.

The main objective of this paper is to implement a transfer learning model for predicting anomalies in online streaming data. Streaming data is a continuous data generation and transmission model with a huge amount of data, enabling different kinds of vulnerable attacks in the network. It leads to negative impacts on the overall network performance. Several earlier methods have been proposed to improve anomaly detection accuracy in streaming data, whereas the false positive rate is high. This paper has aimed to increase the anomaly detection rate with a reduced false positive rate. Hence, it proposed a novel transfer learning method for designing an effective anomaly detection model in data streaming applications. It implements a long., short-term memory for managing the continuous generation and transfer of data called streaming data because it has multiple built-in features like forget gate., which operates the memory by eliminating unwanted and redundant data flows in the streaming process. The LSTM model is deployed in a kind of MANET called VANET, where it is applied to detect anomalies during vehicle communication. This paper provides high prediction accuracy since it integrates various data analytics tasks, like preprocessing, feature extraction, and classification, which feed quality data and perform fast analysis. The LSTM can detect anomalies, including DoS, DDoS, Sybil, Sinkhole, Wormhole, and blackhole. The simulation is carried out by implementing LSTM in Python and executed on a benchmark dataset to verify the efficacy of LSTM. The output shows that the model provides higher accuracy, low latency, and high throughput and is suitable for many real-time applications like IoT networks and cybersecurity.

With the rapid growth of Internet technology and social networks, the generation of text-based information on the web is increased. To ease the Natural Language Processing (NLP) tasks, analyzing the sentiments behind the provided input text is highly important. To effectively analyze the polarities of sentiments (positive, negative and neutral), categorizing the aspects in the text is an essential task. Several existing studies have attempted to accurately classify aspects based on sentiments in text inputs. However, the existing methods attained limited performance because of reduced aspect coverage, inefficiency in handling ambiguous language, inappropriate feature extraction, lack of contextual understanding and overfitting issues. Thus, the proposed study intends to develop an effective word embedding scheme with a novel hybrid deep learning technique for performing aspect-based sentimental analysis in a social media text. Initially, the collected raw input text data are pre-processed to reduce the undesirable data by initiating tokenization, stemming, lemmatization, duplicate removal, stop words removal, empty sets removal and empty rows removal. The required information from the pre-processed text is extracted using three varied word-level embedding methods: Scored-Lexicon based Word2Vec, Glove modelling and Extended Bidirectional Encoder Representation from Transformers (E-BERT). After extracting sufficient features, the aspects are analyzed, and the exact sentimental polarities are classified through a novel Positional-Attention-based Bidirectional Deep Stacked AutoEncoder (PA_BiDSAE) model. In this proposed classification, the BiLSTM network is hybridized with a deep stacked autoencoder (DSAE) model to categorize sentiment. The experimental analysis is done by using Python software, and the proposed model is simulated with three publicly available datasets: SemEval Challenge 2014 (Restaurant), SemEval Challenge 2014 (Laptop) and SemEval Challenge 2015 (Restaurant). The performance analysis proves that the proposed hybrid deep learning model obtains improved classification performance in accuracy, precision, recall, specificity, F1 score and kappa measure.

Face Recognition is one of the most advanced and drastically growing research areas because it helps identify people globally in various ethical and unethical applications. Face recognition needs face detection that can be compared with a list of available faces to predict the correct person. Face detection has become popular, easy, and fast since it follows the Viola-Jones FD method. Face comparison is obtained by comparing the internal and external information from the face images, like different features, face structure, key points, and patch-by-patch comparison. Earlier face recognition methods used separate algorithms for feature extraction from the face images, like color, shape, texture, histogram, and local and global binary pattern, to compare pairs of images where they provide more complexity regarding computation, cost, and time. After the evolution of artificial intelligence models, recent research has focused on using machine and deep learning algorithms for face detection and recognition. However, the accuracy of face recognition models needs to be improved under various conditions. Thus, this paper used a two-stage face comparison model to enhance face recognition efficiency. A consequence of three CNN models called CNN-1, CNN-2, and CNN-3 are used to detect the faces, detect the facial features, and recognize the faces, respectively. The CNN models are implemented in Python, and the results are verified by experimenting with multiple benchmark face datasets. The output accuracy obtained from the face detection and recognition is compared with the facial feature detection and recognition to choose the best to identify the criminals. From the comparison, both FDR and FFDR obtained 99.68% accuracy equally

The large-scale production of multimodal fake news, combining text and images, presents significant detection challenges due to distribution discrepancies. Traditional detectors struggle with open-world scenarios, while Large Vision-Language Models (LVLMs) lack specificity in identifying local forgeries. Existing methods often overestimate public opinion’s impact, failing to curb misinformation at early stages. This study introduces a Modified Transformer (MT) model, fine-tuned in three stages using fabricated news articles. The model is further optimized using PSODO, a hybrid Particle Swarm Optimization and Dandelion Optimization algorithm, addressing limitations such as slow convergence and local optima entrapment. PSODO enhances search efficiency by integrating global and local search strategies. Experimental results on benchmark datasets demonstrate that the proposed approach significantly improves fake news detection accuracy. The model effectively captures distribution inconsistencies and multimodal forgery details, outperforming conventional detectors and LVLMs. This research highlights the importance of integrating transformers and hybrid optimization to develop generalized, scalable, and accurate fake news detection systems.

The rapid growth of e-commerce has made product recommendation systems essential for enhancing customer experience and driving business success. This research proposes an advanced recommendation framework that integrates sentiment analysis (SA) and collaborative filtering (CF) to improve recommendation accuracy and user satisfaction. The methodology involves feature-level sentiment analysis with a multi-step pipeline: data preprocessing, feature extraction using a log-term frequency-based modified inverse class frequency (LFMI) algorithm, and sentiment classification using a Multi-Layer Attention-based Encoder-Decoder Temporal Convolution Neural Network (MLA-EDTCNet). To address class imbalance issues, a Modified Conditional Generative Adversarial Network (MCGAN) generates balanced oversamples. Furthermore, the Ocotillo Optimization Algorithm (OcOA) fine-tunes the model parameters to ensure optimal performance by balancing exploration and exploitation during training. The integrated system predicts sentiment polarity—positive, negative, or neutral—and combines these insights with CF to provide personalized product recommendations. Extensive experiments conducted on an Amazon product dataset demonstrate that the proposed approach outperforms state-of-the-art models in accuracy, precision, recall, F1-score, and AUC. By leveraging SA and CF, the framework delivers recommendations tailored to user preferences while enhancing engagement and satisfaction. This research highlights the potential of hybrid deep learning techniques to address critical challenges in recommendation systems, including class imbalance and feature extraction, offering a robust solution for modern e-commerce platforms.

Requirement engineering’s challenges become manageable when applied to the global advancement of programming. There are numerous reasons why something is difficult. Chances could be one of them since the global improvement perspective is more open to gambling. Therefore, it could be one of the main justifications for taking requirement engineering testing seriously. To begin with, it is necessary to identify the factors that genuinely result in these threats. This essay then separates the factors as well as the risks that these elements may bring about. In the context of the global programming improvement viewpoint, an orderly writing survey is completed for the observable evidence of these variables and the risks that may occur during the necessity designing cycle. The list suggests a moderate improvement in aiding exercises in necessity designing in a global programming advancement worldview. This work is very beneficial for those with less experience working in global programming advancement.

In agriculture, pests are the major reason that causes low yield and greatly affects the crop. Cotton plays a major role in the textile industry and due to a lack of pest identification more amount of cotton crops is getting damaged. To solve this problem, Convolutional neural networks along with MobilenetV2 is used to detect the pests in the cotton plant by passing an RGB image to the proposed model and the model detects whether the pest is present in the crop or not with the accuracy rate of 96.31%. If the pest is present, then the farmer has to be ready with pesticide and if the pest is not present, the farmer has to give fertilizer based on soil properties. This can be done by using the Random Forest (RF) algorithm with an accuracy of 97.95%. This can help farmers to produce more yield.

Precision agriculture aims to improve agricultural productivity by combining technology and farming. The productivity of the cotton is mostly effected by leaf diseases, if we predict these diseases at early stage it helps the farmers to improve productivity. We put forward a novel method for cotton leaf disease detection that works on hybrid dataset which compromises of images from the kaggle dataset and real-time images. Deep learning models VGG16 and VGG19 are applied on this hybrid dataset for disease detection of cotton leaves. This research will not only contribute to improve crop health but also be a valuable resource for farmers. A systematic comparison of the VGG16 and VGG19 models reveals their functional differences in disease detection. VGG16 and VGG19 has achieved accuracy of 94% and 95% in disease detection

Ad hoc network nodes are aggregate data packet from different environment; there is multiple path communication causing the sudden energy depletion in network. This type of energy loss can lead to failure of connectivity between the two intermediate nodes. If link gets failure, then it has frequent loss of data packets. Less energy nodes do not classify data from the network structure. It reduces packet delivery ratio and increases the energy consumption. The proposed cross-layer method for data agglomeration (CLA) is designed to organize the data packet frequently among the various communication routes; the nodes in the path can able to proceed packet organization for the support of cross-layer scheme. Magnificent path discovery algorithm is constructed to offer the better packet collection route to target node. This process uses multisource node with multiple path for packet transmission in network. It minimizes the energy consumption and increases the packet delivery ratio. The simulation parameters are delay, detection efficiency, energy consumption, network lifetime, and packet delivery ratio.

Identification of fresh air by predicting air quality Index is very important for providing better healthy environment to the society. Air pollution causes a severe health issues for the humans as well as threat to the environment. Air quality is measured by predicting air quality Index using some parameters. Based on air quality index value range it'll help to forecast the levels of human health concerns. This study proposes XGboost algorithm to forecast the air quality. When compared to other machine learning models, XGboost helps to predict the air quality with high accuracy rate.

Underwater mining of minerals and rocks is a highly challenging task before the discovery of SONAR (Sound Navigation and Ranging) system. Lately, the mine detection process was performed by the divers trained in the disposal of hazardous ordnance, marine mammals, video cameras mounted on mine-neutralization trucks, and laser systems. which leads to risk and loss to the marine life. SONAR system is capable of capturing Scan-side sonar images, but the model's accuracy is a concern. So Naval defense system need to use a much more accurate system as mines can be easily mistaken as rock, to obtain accurate results we will be working on the dataset of frequencies. Recently, this prediction system was constructed using many machine learning methodologies. This research study proposes to apply XGBoost algorithm to develop a prediction system to predict whether the object is rock or mine. Here, the accuracy of the proposed model is compared with the accuracy of the existing models.

In the current years, the Home Automation organizations takes to see a rapid changes due to introduction of many wireless technologies. The detonation in the wireless expertise has gotten the arrival of countless ethics, particularly in the ISM (engineering, science, and medicine) receiver band. Wi-Fi is an IEEE 802.11 standard. Protocol household usual for data roads with business and consumer devices. Wi-fi is besieged at bids that requires high data rate, wide-ranging mobile life, and locked interacting. Wi-fi has a defined rate of 11 Mbit/s (Megabits per subsequent), best suitable for periodic or discontinuous data from a sensor or input device, or a single signal driver. Wireless home automation systems are meant to be installed in existing homes atmospheres, lacking any changes in the infrastructure. The reconstruction centers on gratitude of voice orders and uses dynamic wi-fi wireless communiqué modules along with microcontroller. This plan is most fit for the elderly in addition the disabled persons specially those who live unaided and since find voice, so it is secure. The home-based computerization system is projected to control all lights and electrical engagements in a home or place of work using voice guidelines. So, in this communication, our aim is to calculate a voice gratitude wireless wi-fi built home computerization. To achieve this, the proposed research work has used ESP 8266 module along with Alexa and Google assistant to decrease power consumption and enhance the security.

The major contribution to Indian economy comes from agriculture which stands as the backbone and also it is the livelihood of many farmers. But now-a-days the crops are being infected by multiple diseases and causing widespread of the disease which in turn damages the entire crop fields if they are not noticed in prior. Crops get diseased by fungi, virus and bacteria and also by worms and insects that attack the crops. These crop diseases should be diagnosed with the help of emerging technologies like DL (Deep Learning) which provide plenty of techniques for disease detection. The proposed model which uses a Super Resolution Convolutional Neural Network (SRCNN) to improve the quality of the crop leaf images and also a CNN which acts as a classifier that helps to detect the crop disease. When the model is trained with SRCNN it improves the performance and illness of crops is also identified in an unerring way.

In the advanced innovative present reality, most of the public are depending on different surveys for different utilizations and different products. Thus, to examine these audits and comprehend whether they are supporting or refuting about, opinion investigation can be utilized. The days for the remark surveys are currently disappearing and everybody is keen on hearing the audits so as they need not stop their functions. Thus, a slant investigation that does both through brief snippet and text can be used to complete work in sound. Along these lines, in this exploration paper, different Machine Learning and Deep Learning Models like Naive Bayes, S upport Vector Machine (S VM), Random Forest, and Multi-Layer Perceptron are used. Additionally, as of to change over voice to a message, a google API and a deep learning technique are used and best of two is exhibited and afterward sentiment analysis on those texts is performed and finally the sentiment (i.e., positive, or negative) of the speech is obtained. All these processes are carried out in real-time.

A Classification method stands out as a reliable data mining technique applied in medical sciences. We observe multi-classification problem afflicting many recent applications that includes social network analysis, biology, anomaly detection and computer vision. However, such classification techniques usually struggle while dealing with features of data that is generated from multiple classes. Moreover, in case of large medical data, it is observed that the dimension of the data poses the biggest challenge while applying classification technique to them. In order to overcome such problems, we have proposed an adaptive random forest classifier method that uses ensemble feature selection technique for better information gain (IG), improved correlation (IC) and gain ratio (GR). Also, it seeks to solve the class imbalance problem by applying bootstrap resampling for medical data. The result of the proposed method proved that adaptive RF (Random Forest) classifier offers better accuracy, precision and F-score values than standard Random Forest and KNN classification algorithms. The overall performance of algorithms was tested over five real datasets. The result analysis shows performance of the proposed classifier is promising in all real datasets as compared to standard methods.

The success of a project depends on accurate effort estimations, managers are always under pressure to prepare accurate effort estimations, in COCOMO model to estimate the effort it requires project parameters. Identification of the type of the project and choosing the project parameters are very important aspect, accurate project parameters generation and estimations are coming from mature organizations others owing to lack of history databases. Estimations are based on lines of code (size of the project), functionality of the project. If the project estimations are based on size of the project the Constructive Cost Model plays vital role. This work explains an expert system that integrates conventional and Soft Computing techniques for dealing uncertainty i.e. virtual nodes generated in the decision paths at leaf node level and we will create an additional node, the main objective of the paper is how to generate suitable values for these nodes. For this purpose we propose the method to generate project parameters using fuzzy logic. In this work solid line indicates the project already done; dotted line indicates the project with uncertainties. After the project parameters are generated using Fuzzy Logic then effort estimations can be prepared.

Every educational system organizational goal is to provide a good and fruitful knowledge to the students. Now a days most of the educational institutions are spending most of their time and economy on finding out students' performance. By analyzing the performance, they identify certain cluster of the students for whom they must give extra bit of care and actions, so that they performance gets enhanced. Researchers have recently proposed several machine learning-based algorithms for predicting academic achievement. In this paper, Linear Regression algorithm and Random Forest algorithm are used to predict a student's academic achievement. On the basis of confusion matrix, accuracy, precision, recall, and F1 ranking, the performance of two algorithms was compared to that of existing algorithms. The Random Forest algorithm-based prediction is more accurate, according to the results report.

The major concern of WSN based application is life time of sensor nodes. Clustering is one of the energy efficient techniques for improving life time of WSN and mainly reduces communication overhead between nodes. Selecting an efficient CH plays imperative role in extending the lifetime of WSN. The trust concept plays an important role in WSN and it’s generally used for detecting malicious, selfish and faulty nodes in a network which increase communication overhead and energy consumption. To conquer these issues, trust based cluster head selection and routing method is proposed. A trust level is computed for each node based on communication, energy and neighbor nodes. This paper proposes an efficient method for cluster head selection and secures routing using two evolutionary algorithms. Cat Swarm Optimization (CSO) is used to select the cluster heads and Firefly algorithm is used for secure routing.Extensive simulations are conducted on various circumstances. The simulation results shows that the proposed trust based CSO finds the optimal cluster head and firefly algorithm discovers the optimal paths which improves the network lifetime and reduces end-to-end delay compared to other techniques.

Wireless Sensor Network (WSN) is a system with huge number of sensors connected to one another by placing them in a specific area. Different issues with WSN includes (but not limited to) the coverage, network lifetime and aggregation. The lifetime of a network can be improved by the clustering with the reduction of energy consumption. Clustering will group the related type of sensors into a single place with a head sensor node for message aggregation and transmission between other nodes and Base Station (BS). The cluster head (CH) consume more energy, when aggregating and transmitting the data. With the suitable identification of CH, there will be a reduction in the consumption of energy and improves the life of Wireless Sensor Network to be more. This paper modifies the meta-heuristic algorithms for improving the network lifetime by choosing appropriate cluster head and optimal path. K-Genetic Algorithm (K-GA) is proposed for efficient cluster head selection. Initially, the sensors are clustered using k-means clustering based on their location and Genetic Algorithm has been applied to detect the best cluster head. For secure optimal routing, Trust based Firefly (T-FA) path selection algorithm is used. Extensive simulations are conducted on various circumstances. The results obtained on the simulation indicates that the proposed K-GA helps in determining the optimized head of the cluster and T-FA discovers the optimal paths which enriches the life of the network by reducing end-to-end delay compared to other techniques.

Wireless sensor networks (WSNs) contain an excellent quantity of battery-driven small nodes that have sensing, computing and communication capabilities. Therefore, it's essential to design an energy-efficient routing protocol to cleverly use the limited energy of WSNs. One-way of managing the energy efficiency is grouping sensors to form a cluster and choose a node as lead to manage referred to as cluster head (CH). In case a malicious node or lower energy node is chosen as a cluster head, the throughput of the network is greatly affected. Thus selection of cluster heads with higher trust and residual energy becomes crucial for the overall network performance. To deal with this problem, this work proposes a Trust based Cluster Head Selection with Secure Routing (TCHS_SR) algorithm for wireless sensor network. The proposed method relies on an effective distributed trust model for cluster head selection and it also considers the secure route for data transfer. The experimental result shows that the proposed TCHS_SR algorithm reduces energy consumption and end-to-end delay, furthermore increases the throughput and packet delivery ratio efficiently.