International Journal of Computer Science Research (IJCSR)
International Journal of Computer Science Research (IJCSR) is a peer-reviewed journal with HIGH IMPACT FACTOR aiming to publish current and relevant findings from cutting edge research in AI, IOT, big data, data science, data mining, cloud computing, information security and innovation in computer technology & programming language. Journal Title: International Journal of Computer Science Research (IJCSR) ISSN: 3048-9385 (Online) Subject: Computer Science Publisher: FOREX Publication Dates of publication: 2022-Present (online) Frequency: Quarterly Language: English Country: India Editor in Chief: Dr. Yu-Chen Hu Download Journal's Template
Mushroom Classifier System Using Machine Learning Algorithm
This paper studies the classification method for mushrooms based on Machine Learning algorithms and Graphic Processors. As mushrooms have many species, classification of mushrooms turns out to be difficult. These large numbers of species contain some edible and some poisonous or deadly poisonous mushrooms. Sometimes mushroom recognition is difficult through our naked eyes and due to a lack of knowledge of the identification of edible mushrooms, recognition turns out to be complicated. Although there are experts in distinguishing poisonous mushrooms from the list of edible mushrooms, where 70-80 species are reported as poisonous, occasional cases occur of misidentification of fatal mushrooms. Also, mushroom collectors have no formal discipline for a testimony of mushrooms, due to which people consume such wild mushrooms misidentified as nutritious mushrooms resulting in life-threatening Categorical features, support vector machine, convolution neural network, graphics processor, machine learning g disease or death-causing illnesses. The main aim of this project was to apply a method to detect whether the mushrooms fit human consumption or not. This paper proposes a method to determine the quality of the mushrooms using a categorical dataset that has 23 distinct characteristics. To solve the complication of the classification of mushrooms, a supervised learning model with the associated learning model is used. This method achieves a good result through the comparison of total time and speed between GPU and CPU.
Copyright (c) 2024 M.Jasmine Pemeena Priyadarsini, S.Rajalakshmi, ShabareeshParlakot, Naveen Kumar Reddy, Ernest Bravin Clinton S, Rizee P.J and Ganesan Subramanian https://creativecommons.org/licenses/by/4.0
Ransomware Detection: Techniques, Challenges, and Future Directions
The ongoing development of ransomware threats calls for sophisticated detection methods that can lessen these widespread and damaging assaults. This survey report offers a thorough analysis of current ransomware detection methods, assesses their efficacy, points out problems, and considers potential directions for future investigation. We address the use of hybrid strategies, anomaly detection system integration, and machine learning algorithms in thwarting ransomware attacks by examining more than forty cutting-edge academic publications. Our goal is to offer a thorough resource that will direct future advancements in cybersecurity defenses against ransomware.
Copyright (c) 2024 Vishalkumar Andodariya, Nishidh Chavda https://creativecommons.org/licenses/by/4.0
A Robust Approach for Hair Contaminant Detection in Papadam Using Transfer Learning
Hair contamination in food products is a serious challenge that impacts quality, safety, and consumer trust. Conventional hair detection techniques often fall short in effectively identifying hair particles, demanding more advanced solutions. This research aims to design and develop a robust framework to correctly detect hair in papadam using image processing and deep learning techniques. The approach starts with an extensive preprocessing pipeline, including grayscale conversion, Wiener filtering for noise reduction, Canny edge detection to highlight edges, and contour detection to extract borderline details, followed by image masking to isolate areas of interest. Pre-trained InceptionV3 transfer learning model is applied for classification, where the initial layers are frozen to preserve learned features, and custom layers are fine-tuned specifically for hair detection. The model incorporates GlobalAveragePooling2D and dense layers with ReLU activation. The proposed approach attained an accuracy of 97.18% in detecting hair contaminants in papadam, validated through real-world datasets. This research highlights the effectiveness of combining comprehensive preprocessing with deep learning to enhance quality, particularly for automatic hair detection in papadam.
Copyright (c) 2024 Sarika Panwar, Shravani Doke, Prathamesh Mankar, Yash Sakat and Shruti Sancheti https://creativecommons.org/licenses/by/4.0
Early Melanoma Detection and Classification Using CNN and Confusion Matrix Analysis
This article brings up a new method for detecting and classifying skin cancers using Convolutional Neural Networks (CNN) and Confusion Matrix analysis. The main work of this work evolves around detection of Melanoma and Non-Melanoma cells. The large dataset on skin cancers is used to teach the CNN model to accurately classify different types of skin lesions into different malignant and non-cancerous groups. The integration of Confusion Matrix, in this case, allows for accurate identification of the classification errors made by the model as well as possible areas for improvement hence making it possible to comprehensively evaluate how well the model performs. Therefore, early recognition is indispensable to successful treatment as skin cancer is a typical yet fatal condition. Results shows that the suggested proposed model achieves 95.5% accuracy and performance in comparison with other methods found in literature. Additionally, Modified DenseNet201 model has a sensitivity of 95.96% as well as a specificity of 98.03%.
Copyright (c) 2024 Dinesh Kumar K, Vennila G, Shanthi H J, Balamurugan S, Govindharaj I and Manikandan K https://creativecommons.org/licenses/by/4.0
AI-Driven Multilingual Document Analysis and Interaction System
This research paper introduces a pioneering application that integrates the capabilities of Artificial Intelligence (AI) with document-based interactions. Users can effortlessly upload their preferred documents to the system, enabling AI analysis of the document's content. Following this analysis, users can pose questions related to the document's content. The application harnesses natural language understanding and AI-driven processing to provide comprehensive and insightful answers, effectively transforming static documents into dynamic repositories of interactive knowledge. By seamlessly integrating document processing and natural language comprehension, this paper aims to redefine and elevate the user experience of interacting with information. Whether utilized for research, learning endeavors, or exploration, this application stands out to enhance user engagement and, facilitating smarter and more interactive interactions with documents.
Copyright (c) 2024 Mohan B A, Basavaraj G N, Karthik S A and Rakesh N https://creativecommons.org/licenses/by/4.0
Predicting Breast Cancer Relapse Images Employing Integrated Machine Learning and Deep Transfer Learning Approaches
Breast cancer is a significant contributor to the increasing global mortality rate, especially among women. The problem of delayed cancer diagnosis is a major concern as it leads to reduced effectiveness of treatment and higher mortality rates. This research aims to create a predictive model for breast cancer relapse. Various machine learning methods were used to achieve accurate predictions of cancer relapse. In order to achieve enhanced predictive outcomes in the case of breast cancer relapse images, the hybrid approach of machine learning approaches, including complex decision tree, quadratic support vector machine, Gaussian medium support vector machine, ensemble subspace k nearest neighbor, and extreme learning machine, are used as classifiers and deep transfer learning approaches including Alexnet, GoogleNet, EfficientNet, VGG-19, and ResNet-18 are used as feature extraction techniques, is proposed. The proposed hybrid approaches were evaluated using various performance metrics. It was found that the ELM classifier applied to the featured extracted dataset is the most suitable for predicting cancer relapse with enhanced achievements with 97.08% accuracy, 97.41% precision, 98.26% sensitivity, 94.64% specificity, 97.83% f-score, 96.45% balanced accuracy and 0.986 AUC. This proposed hybrid approach has the potential to improve cancer relapse prediction.
Copyright (c) 2024 Ghanashyam Sahoo, Ajit Kumar Nayak, Pradyumna Kumar Tripathy, Abhilash Pati, and Amrutanshu Panigrahi https://creativecommons.org/licenses/by/4.0
Combating fraud: dynamic and advanced Techniques for unveiling false reviews and deceiving text on e-commerce website
E-commerce has widely grown among people in recent years and has been used for purchasing products and services on the Internet. E-commerce faces more challenges due to the growing amount of deceptive and fake products online. The purpose of this research is to combat this fraud using dynamic and advanced techniques for unveiling false reviews and deceiving product descriptions. This research employs the DistilBERT model for detecting fake reviews and the BERT base model for identifying misleading product descriptions. This research aims to bring down false information, stop defying products backed up by their false reviews and report them. In this study, we create a FRD Algorithm and DTD Algorithm that solve the problem of Combating fraud: dynamic and advanced Techniques for unveiling false reviews and deceiving text on e-commerce website. The model is achieving an accuracy of 95.6 %. It helps customers save more time and focus on purchasing the product rather than figuring out whether the reviews are true or false. Future research is focusing on a more accurate, more dynamic and efficient way to execute the AI models.
Copyright (c) 2024 Dr. P. Nagaraj and Rajasri Mamidala https://creativecommons.org/licenses/by/4.0
Enhanced Application of Deep Learning Techniques in Smart Power Systems
The researchers argue that better deep learning methods need to be applied in smart power systems because of the smart grid's inability to effectively plan the generation, transmission, and distribution of electrical power in the coming years. Convolutional neural networks (CNNs) are used to set up the energy forecasting estimation approach; CNNs with flexible data features are used to mine characteristics; power ambiguity is quantified; drop regularization is used to optimize the deep network structure; data features are learned using a deep forest; and a model for prediction is constructed. The results showed that the root mean square errors (RMSE) for the weekend power load forecast were 17.3 for the random forest and 17.1 for the Long Short-Term Memory (LSTM) algorithm, while 27.5 was predicted by the Support Vector Machine (SVM) algorithm. The authors' approach provides the most accurate forecast (14.8). After being validated using real-world load data, this technique provides reliable power load predictions even when load oscillations are present. Because of its superior accuracy compared to currently used approaches, it is seen as crucial technical assistance in resolving the fundamental issues associated with smart power systems.
Copyright (c) 2024 Swarupa Rani Bondalapati, Samanthaka Mani Kuchibhatla, Tirumalasetti Lakshmi Narayana and Madhava Rao Chunduru https://creativecommons.org/licenses/by/4.0
Deep Deterministic Policy Gradient Algorithm Dynamic Task Scheduling in Edge-Cloud Environment Using Reinforcement Learning
In contemporary era, cloud computing is helping high performance computing applications by providing scalable and affordable computing resources. However, the latency of cloud resources is relatively less when compared with edge computing. In this context, usage of edge cloud for task scheduling became indispensable to reap benefits of latency performance with edge cloud. However, it is not possible to assign every task to edge cloud and resource-intensive tasks should be scheduled to cloud. With edge-cloud environment, it becomes very complex and scheduling is a NP-hard problem. Many existing methods based on reinforcement learning are found to have shortcoming in dealing with large action-space in presence of state-space. In this paper we proposed an algorithm known as Deep Deterministic Policy Gradient Algorithm for Dynamic Task Scheduling (DDPGA-TS). Our algorithm has a novel pruning strategy that continuously monitors action space and reduces it to improve overall performance in task scheduling. We used three scales of environments with our experiments. Number of performance indicators are used to evaluate performance of the proposed algorithm. Experimental results revealed that the proposed algorithm outperforms existing methods such as DDPG-NN and DDPG-CNN.
Copyright (c) 2024 D Mamatha Rani, Supreethi K P and Bipin Bihari Jayasingh https://creativecommons.org/licenses/by/4.0
Video Anomaly Detection using Likelihood Statistical Texture Feature Representation in Surveillance Video
An anomaly detection system is challenging to develop due to the non-deterministic nature and lack of a clear definition of abnormal events. To address this issue, this paper introduces the Likelihood Statistical Texture Feature Representation (LSTFR) method, which uses CSR (Co-occurrence with Stationary occurrences Representation) to construct spatial activity patterns using gray-level co-occurrence matrices with likelihood estimation. Additionally, LSTFR constructs a composition histogram representation to model normal behavior. The occurrence rate in LSTFR is characterized by a histogram representation that depends on the spatio-temporal information of the frame sequence. To efficiently classify events using LSTFR, this paper employs Convolutional Long Short-Term Memory (conv-LSTM), where the histogram representation of LSTFR is automatically modeled through training with normal events. The efficiency of the proposed approach is evaluated using three datasets: UMN, Subway, and Avenue. Finally, the results of the proposed method are compared with several existing algorithms.
Copyright (c) 2024 Sujith Kumar P. S, Manish T I, Vince Paul and Sanaj M S https://creativecommons.org/licenses/by/4.0
Deep Learning-Driven Precision Agriculture: Enhancing Crop Recommendation and Soil Analysis through Advanced IoT Sensor Data
This research investigates the optimization of crop recommendation and soil type analysis through a fusion of cutting-edge deep learning algorithms, encompassing Convolutional Neural Networks (CNNs), Capsule Networks (CapsNets), Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM) networks, and an innovative AdaBoostClassifier integrated with GRU. Leveraging an extensive dataset collected from IoT sensors measuring critical agricultural parameters such as soil moisture, temperature, pH levels, and nutrient content, this study explores the intricate relationships between these factors. The CNN extracts spatial features, CapsNets unravel complex soil patterns, while GRUs and LSTMs capture temporal dynamics and sequential dependencies within the data. The proposed AdaBoostClassifier coupled with GRU attains a remarkable 99% accuracy in crop recommendation, showcasing its effectiveness. These deep learning architectures, integrated with IoT sensor data, offer a robust framework for precision agriculture, empowering farmers with accurate crop suggestions based on soil conditions, thereby fostering enhanced agricultural productivity and sustainability.
Copyright (c) 2024 M. Priya, A. Jaya and C.D. NandaKumar https://creativecommons.org/licenses/by/4.0
Efficient Net B7 Convolutional Neural Network-Based Security and Privacy Preserving Method for Social IOT Environments
This year, one of the most widely used technical frameworks lacks a specific Internet of Things (IoT). Focusing on communication reliability and dependability on IPv6 standards and internet communication technology, the EfficientNet b7 Social IoT network satisfies care and adaptability needs. Despite the high-quality photographs this effort produced, there was some loss during the system's training, which takes time. This work suggested using evolution deep learning to automatically generate EfficientNet b7 feature frameworks for text classification tasks. The proposed approach is tested in the context of an EfficientNet b7-based language similarity analysis model to see if it works. While character-level EfficientNet b7 algorithms have not received much attention for text classification problems, the EfficientNet b7 structures proposed in this research have demonstrated exceptional performance in data classification tasks. A great deal of testing has shown that they are more resilient to disruptions and that they can impact numerous organizations that implement language and information usage policies regarding user privacy protection, framework implications, and legal requirements.
Copyright (c) 2024 Maniveena. C , Kalaiselvi. R https://creativecommons.org/licenses/by/4.0
YOLOv8-Based Custom Object Detection System for Field Hockey Analysis
The objective of this study is to create custom-trained models utilizing the YOLOv8 architecture to distinguish players in fast-paced field hockey environments. The research aims to enhance performance analysis and strategic decision-making in field hockey by introducing new insights and methodologies to optimize gameplay dynamics. The methodology involved training YOLOv8 models of varying sizes (Nano, Small, Medium, Large, Extra Large) on a self-annotated dataset using a GPU, with training conducted over multiple epochs at an image size of 640. Performance metrics such as precision, recall, F-1 score, and overall accuracy were evaluated for each model variant at 100 epochs. Results indicated that YOLOv8x models achieved high precision (0.832), recall (0.861), and an overall accuracy (mAP@0.5) of 85.70% after 100 epochs, with performance varying based on model size. Additionally, confusion matrices provided detailed insights into the classification performance of YOLOv8 models, highlighting areas for improvement and strengths in object detection. This study's innovation lies in the unique application of YOLOv8-based models for object detection in field hockey, contributing to a deeper understanding of their capabilities and limitations in sports analytics, particularly in player tracking, performance analysis, and strategic decision-making during field hockey matches.
Copyright (c) 2024 Dr. Suhas Patel, Dr. Dipesh Kamdar, Prof. Nilesh kumar Bankar, Prof. Vijay Chavda and Prof. Niraj kumar Chaudhari https://creativecommons.org/licenses/by/4.0
Automated Detection of Cybersecurity Intrusions in Healthcare Systems Using Several Approaches
To ensure that patients are receiving the proper care, the healthcare data must be improved, real-time monitored, and accurate in illness detection. Thus, machine learning techniques are widely employed in Smart Healthcare Systems (SHS) to extract valuable features for tracking patient behaviors and forecasting various diseases from diverse and high-dimensional healthcare data. The kidneys gradually lose their functionality as a result of chronic kidney disease (CKD). It talks about a medical condition that damages the kidneys and has an impact on a person's overall health. In this study, recursive feature elimination (RFE) and multilayer perceptron’s are used to develop a model for identifying anomalies and cyber-attacks (MLP). Experimental data are used to evaluate the suggested MLP model's performance. Recall, precision, accuracy, and F1-score are only a few of the performance metrics used to forecast patient activities. When compared to the RFE technique, the recommended strategy provides the highest levels of accuracy, precision, recall, and F1-score. Specifically, 98.56% recall, 98.13% F1-score, 98.76 accuracy, and 98.93% precision are obtained using the proposed MLP technique. When the outcomes were compared to recent state-of-the-art and machine learning algorithms from recent times, they performed better.
Copyright (c) 2024 Shamija Sherryl. R.M.R, Maria Sheeba. M https://creativecommons.org/licenses/by/4.0
Simulating Hybrid Deep learning mechanism for security enhancement during Blockchain based WSN authentication
In wireless sensor networks, sensor nodes have constrained resources including processing speed, memory, and battery life. WSNs are susceptible to a broad range of vulnerabilities since they are often used in untrusted environments. Given the difficulties of securing a WSN, the reliability of the information it collects is also put into question. WSNs' authentication procedure permits checks on the legitimacy of both resources and data. Data in WSNs is protected against tampering thanks to authentication, which checks the data's provenance and allows only authorized changes. However, current authentication methods have certain security holes, such as those that may be exploited by ID spoofing attacks. When it comes to cyber security, blockchain is another example of a promising new technology. All transactions on the blockchain are cryptographically protected and cannot be altered once they have been made. The goal of this study is to one day use blockchain technology in wireless sensor networks (WSNs). In this research, we created a novel authentication procedure for WSNs that relies on blockchain technology. Users and a private blockchain were important in integrating sensor nodes and the blockchain into the study's system architecture. The study's data was subjected to a thorough examination for security. Deep learning model has been used to classify secure and unsecure record over blockchain based WSN. Classification of data stored on blockchain is made after performing filtering using optimizer.
Copyright (c) 2024 Tejbir Singh, Rohit Vaid https://creativecommons.org/licenses/by/4.0
Data-Driven Network Graph Theory for Controlling Dynamic Systematic Data Perspective of Smart Power
To attain sustainable energy in the increasing climatic adaption and environmental defence, the predominant factor to attain is smart power generation which ensures the secured operation economically. The dynamic nature of the power in the system scale explicitly shows the limitations of the conventional first principle model. To tackle this issue, we propose a Network Graph theory (NGT)-based mathematical modelling incorporated with the data-driven control (DDC) and to attain the optimal output the NGT is optimized using the proposed Mayfly optimization algorithm (MO). This proposed technique is utilized to analyze the uncertainty with fault detection and diagnosis. The factors for controlling the smart power system are discussed and we presented the technique for the fault detection and diagnosis. Simulation demonstrates and analyses the different factors such as detection accuracy. Recall, and precision with the state-of-art techniques. The proposed techniques pave the way for protecting smart power generation along with dynamically controlled systematic data.
Copyright (c) 2024 K. Basari Kodi, M. Mano Raja Paul, M. Geetha, G. Nallasivan, S. Jeevitha and A. Bhuvanesh https://creativecommons.org/licenses/by/4.0
Deep Learning based Robust Food Supply Chain Enabled Effective Management with Blockchain
Agriculture supply chain plays a predominant role in everyday life. The safety of the products is more significantly considered while the farmers targeted in the improvising of their profit. Moreover, the productions of agri products are dynamic and tracing the production and distribution are challenging and time-consuming process. In concern with these, we propose a blockchain based secured framework for tracing and managing the supply of pulses incorporated with the storage capacity of factories. The cost of production, transportation, penalty, and storage are analyzed. For the enhancement of profitability, and managing the production to improve the storage AlexNet framework is proposed. This effectively classifies the need of the retailers and aids in supplying the products. Simulation is conducted and analyzed the robustness of the proposed work with the comparison of state-of-art works. The outcomes show that the profitability and production increases with the proposed work.
Copyright (c) 2024 Ramya Thatikonda, Ragupathi Thota, Reshmi Tatikonda and Bhuvanesh. A https://creativecommons.org/licenses/by/4.0
A Comparative Analysis of Support Vector Machine and Decision Tree Algorithm for Predicting Fault in Technical Data Schedule Uninterruptible Power Supply System for Ghana Gas Limited
Power supply systems can have problems, and Ghana Gas Limited is not an exception. Ghana Gas Limited uses an intricate Uninterruptible Power Supply (UPS) system called the Technical Data Schedule (TDS), which is made up of several parts such as electromechanical components, PCB boards, and electrolytic capacitors.The majority of components have technical lifespans that are governed by usage, operational environment, and working conditions, such as electrical stress, working hours, and working cycles. Most of the time, these errors affect the integrity and power supply after manufacture. The issue is that it takes longer for the professionals who operate on this machine to recognize these flaws, which makes it difficult for them to predict errors quickly or anticipate the likelihood of faults happening in the system components at an early stage for effective corrective action to be performed. Support vector machines (SVM) and decision trees were used in this study to anticipate faults for technical data scheduling of uninterruptible power supply systems for Ghana Gas Limited in an efficient manner. Based on a comparative analysis using these two techniques, faults in Ghana Gas Limited's power supply system were predicted using a four-hour daily interval dataset on TDS UPS recordings, including input voltage, battery voltage, battery current, and alarm, spanning from August 2017 to October 2023. The findings depicted that the support vector machine was more efficient in detecting the fault locations in the power supply system with an accuracy of 96.80%, recall of 99.80%, precision of 100 %, F1-score of 93.15%.The results from the error metrics also validate the measures in assessing the predictive ability of the model with MAE of 0.42%, MSE of 1.18%, RMSE of 4.45%, R2 of 99.97%, RMSLE of 0.036%, and MAPE of 0.21%.
Copyright (c) 2024 Isaac M. Doe, J. K. Annan and B. Odo https://creativecommons.org/licenses/by/4.0
A Privacy Preserving Data Mining through Comprehensive GNIPP Approach in Sensitive Data Sets
The quick growth of methods for analysing data and the availability of easily available datasets have made it possible to build a thorough analytics model that can help with support decision-making. In the meantime, protecting personal privacy is crucial. A popular technique for medical evaluation and prediction, decision trees are easy to comprehend and interpret. However, the decision tree construction procedure may reveal personal information about an individual. By keeping the statistical properties intact and limiting the chance of privacy leaking within a reasonable bound, differential privacy offers a formal mathematical definition of privacy. To construct a boosting random forest that preserves privacy, we propose in this study a Gaussian Noise Integrated Privacy Preservation (GNIPP). To address the issue of personal information breach, we have designed a unique Gaussian distribution mechanism in GNIPP that enables the nodes with deeper depth to obtain more privacy during the decision tree construction process. We propose a comprehensive boosting technique based on the decision forest's prediction accuracy for assembling multiple decision trees into a forest. Furthermore, we propose an iterative technique to accelerate the assembly of decision trees. After all, we demonstrate through experimentation that the suggested GNIPP outperforms alternative algorithms on two real-world datasets.
Copyright (c) 2023 Shailesh Kumar Vyas and Swapnili Karmore https://creativecommons.org/licenses/by/4.0
Cybersecurity uses Stacking Ensemble Learning for Darknet Classification
Cyber intelligence services sometimes refer to the "darknet," the part of the internet that consumers don't often anticipate to be accessible for machine-to-machine communication. Before building security, it is important to analyze the network's risks. To examine and classify darknet data, this study suggests new classification methods for machine learning called stacking ensemble learning. This study used ensembles of machine learning methods on the newly published CIC-Darknet2020 dataset to accurately distinguish between Darknet and Benign traffic, achieving a 98% accuracy rate. Furthermore, it successfully identified the specific kind of application running behind the Darknet traffic with a 97% accuracy rate. In addition, we used an approach based on game theory to assess the output of the models developed using machine learning and showcase the impact of the features, intending to gain a deeper understanding of the Darknet traffic behavior. To the best of our knowledge, this research is the first one conducted on this dataset, as confirmed by the dataset producers.
Copyright (c) 2023 Amutha S, Dr.G.Uma Maheswari and A. Anna Lakshmi https://creativecommons.org/licenses/by/4.0
Data Mining Solutions towards Bank Telemarketing Predicting
Lending in international markets has become more restricted, and the focus of funding has shifted to domestic customers and their deposits. This desire has created a demand for knowledge about customer deposit behavior, especially customer reactions to telemarketing campaigns. Therefore, promotional transaction banking strategies shifted from traditional methods to advanced data analytics and machine learning techniques to keep pace with the growth of the banking industry. In a telemarketing business, the ability to select potential customers for purchase is important, because it reduces processing time and operational costs. This paper presents a data mining solution using machine learning techniques to predict customer response to a bank's telemarketing campaign through clustering similar groups by the data analysis. Whereas, the dataset groups are pre-processed and using three types of algorithms: k-nearest neighbor (KNN), support vector machines (SVM), and decision trees (DTs), for modeling. The tests were conducted on a real dataset that resembles the actual information associated with the bank client, telemarketing calls, and the results of the customer's bank time deposits as a result of the call. The experimental results show that DTs has superior performance in some settings with a true positive rate of over 90%, outperforming their equivalent methods.
Copyright (c) 2023 Ahmed Adeeb Jalal, Wasseem N. Ibrahem Al-Obaydy and Abdulhakeem Qusay Albayati https://creativecommons.org/licenses/by/4.0
Plant Disease Detection From Images Using Deep Learning Techniques based on the Internet Of Things
Plant disease identification and evaluation in a timely and accurate manner is crucial for efficient farming operations for crop yield optimization. Employing the most recent advances in technology, specifically the combination of deep learning and the Internet of Things (IoT), this paper offers an efficient approach to identifying plant diseases. We propose a transfer learning-based deep learning classification model which makes use of pre-trained models including CNN, AlexNet, ResNet, InceptionV3, and VGG-16. To provide wider accessibility, high-resolution images of tomato plant leaves displaying disease symptoms are gathered from a dataset and saved in cloud storage using Internet of Things devices. Following the image extraction from the cloud, images are preprocessed using data argumentation, normalization, color space conversion, background removal, and noise removal. Different plant disease classes are classified using the pre-trained models CNN, AlexNet, ResNet, InceptionV3, and VGG-16. The deep learning models' accuracy is increased using the transfer learning technique, which also cuts down on the workout duration. The VGG-16 model outperforms other models in the experiment, recognizing plant illnesses with an astounding accuracy of 93.7% on average, proving the efficacy of the suggested approach. This novel method has the potential to transform the identification of plant diseases and support sustainable farming methods.
Copyright (c) 2023 Ujai B C, Bala Murugan V V, Siddharth V, Appana Venkata Raju Sai, Ssathyan S R and Angulakshmi M https://creativecommons.org/licenses/by/4.0
Impact of 5G network on Internet of Things (IOT)
Now a days IOT (Internet of Things) is very common. IOT is that which is used to do some work with the help of sensing devices. In this we simply do any work with senser devices. We do any communication with sensors we need IOT devices. 5G network is the mobile network. It is the combination of different services which comes from different mobile network like 1G, 2G, 3G and 4G. In this paper I tell you about how 5G mobile network effect on IOT.
Copyright (c) 2023 Sadhana Singh https://creativecommons.org/licenses/by/4.0
Multi-modal Fake News Detection using Multimodal Approach of BERT and ResNet110
Globally, the usage of social media has significantly increased and has become the most common way for people to deplete news. The easy sharing of multimedia content on social media has caused the fake news dimension, which threatens the stability as well as security of the society. Fake news detection (FND) in social media becomes challenging, because of which various tools are developed to detect them. Multi-modal FND aims to determine fake data by text as well as images. Most commonly, researchers identify fake news only as text, but not as images. This research proposes a multimodal approach for detecting fake news in the formats of both text and image, and for classifying news as real or fake. The proposed multimodal-based convolutional neural network (CNN) combines the designs of both text and image of fake news. This method utilizes two classification methods named bidirectional encoder representations from transformers (BERT) for text, and ResNet110 for images. This method uses the Fakeddit dataset to estimate and evaluate the performance. The experimental results of the proposed ResNet110+BERT model achieves respective accuracy, precision, recall and F1-score values of about 0.931, 0.944, 0.942, and 0.946, which is superior when compared to the existing methods, recurrent CNN (RCNN) and fine-grained multimodal fusion network (FMFN). From the analysis, it is proven that the proposed method ResNet110-BERT achieves an accuracy of 0.931, and hence shows better results for overall metrics when compared to the existing methods of RCNN and FMFN.
bidirectional encoder representations from transformers
multimodal fake news detection; ResNet110
Copyright (c) 2023 Chetan Agrawal, Anjana Pandey and Sachin Goyal https://creativecommons.org/licenses/by/4.0
Multi-Objective Optimization and its Application in Cloud Computing
As an emerging technology, cloud computing which processes a huge amount of data as well as requires a large amount of resource cost and a large part of the user’s budget cost. Job arrangement is significant in cloud computing, since it openly disturbs a systems burden and enactment. An actual job scheduling technique needs not only conference the user’s wants but also improves the output of the entire system. So, scheduling mechanism works as a dynamic part in the cloud computing. Thus, my protocol is planned to minimize the switching time, increase the resources’ utilization and also increase the server performance and throughput. Here we allocate the import to the job which gives better performance to the computer and try the best to reduce the waiting and switching time. The cloud services comprise of altered functionalities at variable costs, and changeable reliability. So the customer’s main purposes are to maximize their usefulness, and decrease their costs and risks. For job scheduling problems in cloud computing, a multi objective optimization technique is required to be projected here. An effective job scheduling not only minimizes the load of the resources but also eliminates the budget cost constraints of the users completing multi objective optimization of both performance and cost. We proposed ant colony optimization algorithm here to resolve this tricky. The ant colony algorithm is a probabilistic and indeterminate world-wide optimization algorithm, so it’s easy to find an over-all optimal solution. Moreover, it does not depend on accurate optimization and organizational features of the tricky itself. Two constraints roles were used to calculate and view the utility performance and cost. Based on the feedback these two restriction functions ready the algorithm correct the scheduling to process in a timely manner.
Copyright (c) 2023 Sigma Nayak, Subhashis Mishra, Debashis Mishra and Utpal De https://creativecommons.org/licenses/by/4.0
A Survey on Evolutionary Optimization Approaches towards Medical Image Segmentation using Thresholding
In medical image processing, image segmentation has strong role to play. What image segmentation does that it splits medical image into finite number of parts such that it is easier to analysis the region we are looking for in the medical image that is Region of Interest (ROI). Image segmentation in medical image processing is basically needed for identifying abnormalities in biomedical anatomy and help doctor and physician for diagnose. Image thresholding is an elementary method for digital image segmentation based on optimum intensity or threshold value. A computer aided autonomous process where threshold value can be obtained through a fitness function such as maximum entropy method, k-means clustering and so on. Now all it needs to optimise the fitness function using any optimisation method.
Copyright (c) 2023 Gokulananda Dash, Subhashis Mishra, Debashis Mishra and Utpal De https://creativecommons.org/licenses/by/4.0
Modelling of Trajectory Moving Data with use of Social Media: A Review
From last decades, there has been more research on mobile phone network data. Usually, mobile phone user mobility can be track from GIS of device or online social media information with up gradation of location enabled services. Tracking using GIS system is much costlier, time consuming and requires more man power while social media used by smart phones and location enabled devices can easily track the location and time, which can be further utilized by researcher in spatiotemporal domain along with other application related attribute or information. User mobility pattern and prediction are recent interesting area in data and pattern mining. There are many applications for trajectory data modelling such as transportation, tourist place recommendation, land use application, spread of diseases tracking etc. As information in social media data is not dense and difficult to analysis, this article proposes group methodology of user textual data with respect to location and time of user post. In this, user clustering and moving pattern design are made alternatively with each other so that efficiency of method will increases in each stage of method. User clustering is based on Hidden Markov Models (HMMs) which uses definite number of unrecognizable latent states where each record keep up a correspondence to each latent state which has a probabilistic distribution that governs the generation of records as well as text augmentation for proper distribution of textual data with respect to location and time, which is also used to reduce text complexity and not dense data [2]. This method is used to increase prediction accuracy of trajectory moving data.
Copyright (c) 2023 Prachi Junwale https://creativecommons.org/licenses/by/4.0
Storage-Effective Deduplication by Improved Hashing Hybrid Computations SHA3 and Firefly
Today’s modern generation is the world of digitization and cloud assesses; it begins to be exceedingly paramount to bestow prodigious specifics. Prolong excessive traffic of raw material which upload to storage space upshots data deduplication in cloud storage and also responsible for network congestion many times. Necessity comes with a new invention to fulfill the future requirement of high-definition raw material which continued inject to cloud. To overcome data redundancy and security issuance, the number of techniques instigates which helps to get the traits of same raw material, but dwindled and becomes obsolete against the new obstacle of server-storage and data delicacy, so necessitate of up-to-date competence inevitably inflated. To engineer this target in this paper I arbitrate to club together the consequence of different hashing, compression computations, so that performance, accuracy, and throughput to be revamped, I have achieved with the help of SHA family and firefly algorithms.
deduplication secure optimized algorithms
Copyright (c) 2023 Karmjeet Singh and Ramanjot Kaur https://creativecommons.org/licenses/by/4.0
Assessment and filtering of mental state Image using Avalanche Oozing (A-O) Algorithm
Image filtering plays very important role in every field, for correct assessment of any condition. Condition may be related with medical field, satellite data or any written data evaluation. Noise reduction is a process through which a received image visibility can be enhanced by removing unwanted effect from that image by using various algorithms. This paper encompasses the filtering of biomedical image using Avalanche Oozing (AO) algorithm. First section of this work includes the general introduction of image analysis, after that the various types of image noises was described. Then problem formulation and objectives of this paper have been listed. Further results of predefined and A-O method have been compared by using various image.
Copyright (c) 2023 Jatinder Kaur, Sharnjeet Kaur and Dr. Sajjan Singh https://creativecommons.org/licenses/by/4.0
MATLAB Implementation for Relative Assay of Compression Techniques
With the advancement of technology, the throughput during data transfer, becomes major concern for all. In most of the system, data transfer ability is fixed so for improvement in throughput, the various compression strategies came into light. These all techniques help to increase data rate by using fixed ability. At the same time compression techniques helps to accommodate purely binary data in every system. This paper delineates the conceptual and practical approach of compression strategies. Moreover, it also scrutinizes the future prospect of research work in the field of compression.