Improved deep learning model for detection and classification of pneumonia from x ray images /
Improved Deep Learning Model for Detection and Classification of Pneumonia from X-Ray Images
Pneumonia is a severe respiratory disease that can lead to inflammation, fluid accumulation in lungs and breathing difficulties, which needs immediate and accurate diagnosis. Chest X-Ray images are a necessary tool to diagnose pneumonia because manual interpretation poses challenges, particularly for radiologists with less expertise. Artificial intelligence (AI), specifically Convolutional Neural Networks (CNNs), has become a significant in the field of pneumonia detection within chest X-Ray images in recent years. This research presents SarNet, a neural network model developed for the identification of pneumonia in chest X-Ray images. The study involved the compilation of dataset containing chest X-Ray images categorized as normal, pneumonia, and COVID-19 pneumonia cases, each accompanied by appropriate annotations. This dataset was employed as the basis for training and assessing SarNet's performance, underscoring its promise in transforming the diagnosis of pneumonia. SarNet proved highly effective, achieving good accuracy, sensitivity, and specificity compared to traditional diagnostic methods. The model's simplicity, with 41 layers, strikes a balance between depth and computational complexity, enhancing efficiency and ensuring accurate pneumonia detection. Furthermore, the study expanded its scope to include COVID-19 pneumonia detection. SarNet achieved an accuracy of 99.15% in binary classification and 94.9% in multiclass classification, including healthy, pneumonia, and COVID-19 pneumonia cases.
Improved dhoa-fuzzy based load scheduling in iot cloud environment
Cloud computing; Fuzzy logic; Internet of things; Load scheduling; Metaheuristics
Internet of things (IoT) has been significantly raised owing to the development of broadband access network, machine learning (ML), big data analytics (BDA), cloud computing (CC), and so on. The development of IoT technologies has resulted in a massive quantity of data due to the existence of several people linking through distinct physical components, indicating the status of the CC environment. In the IoT, load scheduling is realistic technique in distinct data center to guarantee the network suitability by falling the computer hardware and software catastrophe and with right utilize of resource. The ideal load balancer improves many factors of Quality of Service (QoS) like resource performance, scalability, response time, error tolerance, and efficiency. The scholar is assumed as load scheduling a vital problem in IoT environment. There are many techniques accessible to load scheduling in IoT environments.With this motivation, this paper presents an improved deer hunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) model for load scheduling in IoT environment. The goal of the IDHOA-T2F is to diminish the energy utilization of integrated circuit of IoT node and enhance the load scheduling in IoT environments. The IDHOA technique is derived by integrating the concepts of Nelder Mead (NM) with the DHOA. The proposed model also synthesized the T2L based on fuzzy logic (FL) systems to counterbalance the load distribution. The proposed model finds useful to improve the efficiency of IoT system. For validating the enhanced load scheduling performance of the IDHOA-T2F technique, a series of simulations take place to highlight the improved performance. The experimental outcomes demonstrate the capable outcome of the IDHOA-T2F technique over the recent techniques. 2022 Tech Science Press. All rights reserved.
Joshua Samuel Raj R.; Ilango V.; Thomas P.; Uma V.R.; Al-Wesabi F.N.; Marzouk R.; Hilal A.M.
Computers, Materials and Continua, Vol-71, No. 2, pp. 4101-4114.
https://doi.org/10.32604/cmc.2022.022063 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120801301&doi=10.32604%2fcmc.2022.022063&partnerID=40&md5=0a6c802914a3da1d57738db9a03f8aed
Joshua Samuel Raj R., Department of Information Science and Engineering, CMR Institute of Technology, Bengaluru, 560037, India; Ilango V., Department of Computer Application, CMR Institute of Technology, Bangalore, 560037, India; Thomas P., School of Computing, Woldia Institute of Technology, Woldia University, Ethiopia, Ethiopia; Uma V.R., Department of Commerce, School of Commerce. Finance and Accountancy, Christ University, Bangalore, 560029, India; Al-Wesabi F.N., Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Saudi Arabia, Faculty of Computer and IT, Sana'a University, Yemen, Yemen; Marzouk R., Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia; Hilal A.M., Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia
Improved diabetes disease prediction IWFO model using machine learning algorithms
Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level. 2024 Author(s).
Gunavathi R.; Sivakumar V.; Kumar B.S.; Vijayalakshmi S.
AIP Conference Proceedings, Vol-3161, No. 1
https://doi.org/10.1063/5.0229494 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203957539&doi=10.1063%2f5.0229494&partnerID=40&md5=72023ea6c153c360ab409219e1ca248a
Gunavathi R., School of Science, Department of Data Science, Christ University, Pune, India; Sivakumar V., School of Computing, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Kumar B.S., Department of Computer Science, Sree Narayana Guru College, Coimbatore, India; Vijayalakshmi S., School of Science, Department of Data Science, Christ University, Pune, India
Improved dragonfly optimizer for intrusion detection using deep clustering CNN-PSO classifier
Binning normalization; Convolutional neural network; Deep clustering; Dragonfly optimizer; Information gain; Intrusion detection system
With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information. Based on the characteristics of these intruders, many researchers attempted to aim to detect the intrusion with the help of automating process. Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue. IDS (Intrusion Detection System) was developed to detect and prevent the intruders and secure the network systems. The performance and loss are still an issue because of the features space grows while detecting the intruders. In this paper, deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and preprocessing. The proposed system includes three phases such as preprocessing, feature selection and classification. In the first phase, KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization method. In second phase, feature selection is performed by using Information Gain based Dragonfly Optimizer (IGDFO). Finally, Deep clustering based Convolutional Neural Network (CCNN) classifier optimized with Particle Swarm Optimization (PSO) identifies intrusion attacks efficiently. The clustering loss and network loss can be reduced with the optimization algorithm. We evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation metrics. The experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy, precision, recall, f-measure and false detection rate. 2022 Tech Science Press. All rights reserved.
Bhuvaneshwari K.S.; Venkatachalam K.; HubovskS.; TrojovskP.; Prabu P.
Computers, Materials and Continua, Vol-70, No. 3, pp. 5949-5965.
https://doi.org/10.32604/cmc.2022.020769 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116961790&doi=10.32604%2fcmc.2022.020769&partnerID=40&md5=4d98f0abf4c09995af53fc9293f2ae63
Bhuvaneshwari K.S., Department of Computer Sceince and Engineering, Karpagam College of Engineering, Coimbatore, 641032, India; Venkatachalam K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; HubovskS., Department of Applied Cybernetics, Faculty of Science, University of Hradec Krov Hradec Krov 50003, Czech Republic; TrojovskP., Department of Mathematics, Faculty of Science, University of Hradec Krov Hradec Krov 50003, Czech Republic; Prabu P., Department of Computer Sceince, CHRIST(Deemed to be University), Bangalore, 560074, India
Improved Feature Selection Method for the Identification of Soil Images Using Oscillating Spider Monkey Optimization
Feature selection; Perturbation rate; smart agriculture; Soil prediction; Spider monkey optimization
Precision agriculture is the process that uses information and communication technology for farming and cultivation to improve overall productivity, efficient utilization of resources. Soil prediction is one of the primary phases in precision agriculture, resulting in good quality crops. In general, farmers perform the soil prediction manually. However, the efficiency of soil prediction may be enhanced by using current digital technologies. One effective way to automate soil prediction is image processing techniques in which soil images may be analyzed to determine the soil. This paper presents an efficient image analysis technique to predict the soil. For the same, a robust feature selection technique has been incorporated in the image analysis of soil images. The developed feature selection technique uses a new oscillating spider monkey optimization algorithm (OSMO) for the selection of features that are relevant and non-redundant. The new oscillating spider monkey optimization algorithm increases precision and convergence behavior by using an oscillating perturbation rate. A set of standard benchmark functions was deployed to visualize the performance of the new optimization technique (OSMO), and results were compared based on mean and standard deviation. Furthermore, the soil prediction approach is validated on a soil dataset, having seven categories. The proposed feature selection method selects the 41% relevant features, which provide the highest accuracy of 82.25% with 2.85% increase. 2013 IEEE.
Agarwal R.; Shekhawat N.S.; Kumar S.; Nayyar A.; Qureshi B.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ACCESS.2021.3135536 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121809681&doi=10.1109%2fACCESS.2021.3135536&partnerID=40&md5=14b4ba5a8369d9e11cbc03c17ad96887
Agarwal R., Department of Computer Science and Engineering, Engineering College Bikaner, Bikaner, 334004, India; Shekhawat N.S., Department of Computer Science and Engineering, Engineering College Bikaner, Bikaner, 334004, India; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560074, India; Nayyar A., Graduate School, Duy Tan University, Da Nang, 550000, Viet Nam, Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Viet Nam; Qureshi B., Department of Computer Science, Prince Sultan University, Riyadh, 11586, Saudi Arabia
Improved File Security System Using Multiple Image Steganography
Batch Steganography; File security; Image Steganography; Multiple image steganography
Steganography is the process of hiding a secret message within an ordinary message extracting it at its destination. Image steganography is one of the most common and secure forms of steganography available today. Traditional steganography techniques use a single cover image to embed the secret data which has few security shortcomings. Therefore, batch steganography has been adopted which stores data on multiple images. In this paper, a novel approach is proposed for slicing the secret data and storing it on multiple cover images. In addition, retrieval of this secret data from the cover images on the destination side has also been discussed. The data slicing ensures secure transmission of the vital data making it merely impossible for the intruder to decrypt the data without the encrypting details. 2019 IEEE.
2019 International Conference on Data Science and Communication, IconDSC 2019
https://doi.org/10.1109/IconDSC.2019.8816946 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072780086&doi=10.1109%2fIconDSC.2019.8816946&partnerID=40&md5=24495efc3be314480bb76fd623b0da6f
Benedict A.G., Computer Science and Engineering, Faculty of Engineering, Christ, Deemed to be University, Bengaluru, India
Improved Henon Chaotic Map-based Progressive Block-based visual cryptography strategy for securing sensitive data in a cloud EHR system
Henon maps; Improved henon chaotic map; Lorentz maps; Progressive visual cryptography; Secret sharing
The core objective of secret sharing concentrates on developing a novel technique that prevents the destruction and leakage of original data during the distribution and encoding processes. Progressive Visual Cryptography (VC) is considered for the potential over the traditional VC schemes since the former does not require and does not suffer from the limitations of requiring a minimum number of participants during the process of encryption and sharing. The chaotic map-based Progressive VC is superior in facilitating predominant secrecy under sharing and encryption. In this paper, an Improved Henon Chaotic Map-based Progressive Block-based VC (IHCMPBVC) scheme is proposed to prevent the leakage and destruction of sensitive information during an exchange and encryption. This proposed IHCMPBVC technique uses the merits of Henon and Lorentz maps for effective encryption since it introduces the option of deriving non-linear behavior that results in sequence generation that covers the complete range with proper distribution in order to minimize the degree of leaks in sharing. The simulation results of the proposed IHCMPBVC technique investigated using entropy, PSNR, and Mean Square Error were improved at an average rate of 27%, 23%, and 31%, predominant to the baseline VC approaches considered in the comparison. 2022 The Authors
International Journal of Intelligent Networks, Vol-3, pp. 109-112.
https://doi.org/10.1016/j.ijin.2022.08.004 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138500541&doi=10.1016%2fj.ijin.2022.08.004&partnerID=40&md5=c06307b2ae517d72b0f57d8278b1b671
Mohanasundaram A., Department of Applied Computer Science, Vrije Universiteit Brussel, Belgium; Aruna S.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), India
Improved image denoising with the integrated model of Gaussian filter and neighshrink SURE
Gaussian filter; Image denoising; NeighShrink SURE me; Wavelet thresholding
Image denoising, being an important preprocessing stage in image processing, minimizes the noise interfering with the information content of the image. The denoising problems are addressed by various techniques starting from the Fourier transforms to wavelets. Because of the localized time frequency features and advantages of multi resolution capabilities, the wavelets have been extensively used in the denoising process. The development of algorithms for the wavelet thresholding or shrinkage strategies along with different filters have resulted in the betterment of image quality after the denoising. Even though the image denoising algorithm based on a combination of Gaussian and Bilateral filters, shows good performance but lacks in consistency with respect to the noise levels and also the type of images used. This paper discusses the advantages of NeighShrink SURE rule in developing an effective thresholding strategy, thereby proposing a improved denoising method incorporating the NeighShrink SURE rule along with combination of Gaussian filter model. The methodology employs the use of subband thresholding derived from the NeighShrink SURE rule. The outcome of the proposed method exhibits a comparatively improved performance in Peak Signal to Ratio (PSNR) and Image Quality Index (IQI) values of the test images. BEIESP.
Naragund M.N.; Jagadale B.N.; Priya B.S.; Panchaxri; Hegde V.
International Journal of Engineering and Advanced Technology, Vol-8, No. 6, pp. 3010-3015.
Blue Eyes Intelligence Engineering and Sciences Publication
https://doi.org/10.35940/ijeat.F9020.088619 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072009890&doi=10.35940%2fijeat.F9020.088619&partnerID=40&md5=fa0103ba38db746ba41967c59b2e0051
Naragund M.N., Department of Physics and Electronics, CHRIST (Deemed to be University), Bengaluru, India; Jagadale B.N., Department of PG studies and research in Electronics, Kuvempu University, Shimoga, India; Priya B.S., Department of Electronics, Kuvempu University, Shimoga, India; Panchaxri, Department of Electronics, SSA Govt. First grade College, Ballari, India; Hegde V., Department of Electronics, MESMM Arts and Science College, Sirsi, India
Improved Photocatalytic Activity of g-C3N4/ZnO: A PotentialDirect Z-Scheme Nanocomposite
Energy conversion; g-C3N4/ZnO; Malachite green degradation; Nanocomposite; Z-scheme photocatalysis
In this study, a Z-scheme g-C3N4/ZnO nanocomposite was synthesized using exfoliation process, which was further characterized using XRD, FT-IR, UV-DRS, SEM-EDAX, PL, EIS, and TGA techniques. The properties of g-C3N4 were enhanced when fabricated with ZnO resulting in a better electron mobility, high redox potential, and excellent semiconducting properties. The performance of this heterostructure was evaluated by photocatalytic degradation of malachite green (MG) under visible light irradiation. The g-C3N4/ZnO heterostructure achieved a degradation of 84.3 % within 60 min under visible light irradiation. The degradation reaction follows a pseudo first-order kinetic model with a reaction rate constant of 0.0329 min?1. The nanocomposite demonstrated outstanding stability and recyclability. 2020 Wiley-VCH GmbH
Ramachandra M.; Devi Kalathiparambil Rajendra Pai S.; Resnik Jaleel UC J.; Pinheiro D.
ChemistrySelect, Vol-5, No. 38, pp. 11986-11995.
https://doi.org/10.1002/slct.202003166 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092563140&doi=10.1002%2fslct.202003166&partnerID=40&md5=1c0d9a1fdc2cb4cdd32c86b2e61186e4
Ramachandra M., Department of Chemistry CHRIST, Deemed to be University), Hosur road, Bengaluru, 560029, Karnataka, India; Devi Kalathiparambil Rajendra Pai S., Department of Chemistry CHRIST, Deemed to be University), Hosur road, Bengaluru, 560029, Karnataka, India; Resnik Jaleel UC J., Department of Chemistry CHRIST, Deemed to be University), Hosur road, Bengaluru, 560029, Karnataka, India; Pinheiro D., Department of Chemistry CHRIST, Deemed to be University), Hosur road, Bengaluru, 560029, Karnataka, India
Improved Random Forest Algorithm for Cognitive Radio Networks' Distributed Channel and Resource Allocation Performance
Channel Allocation; Cognitive Radio Networks; Random Forest; Routing; Throughput
Modified Random Forest (MRF) machine learning algorithm aimed at improving the distributed channel allocation and resource allocation performance in cognitive radio networks (CRNs). The purpose of this research is to enhance the efficiency and effectiveness of CRNs by optimizing the allocation of channels and resources. The proposed MRF algorithm is developed by adapting and modifying the random forest technique to address the specific challenges of CRN allocation. Experimental evaluations demonstrate that the MRF algorithm achieves higher accuracy and efficiency compared to existing routing techniques and channel allocation algorithms like ACO and PSO. It exhibits a high packet delivery ratio, increased throughput, and reduced delay in channel selection, thus improving the overall performance of CRNs.The implications of this research are twofold. On a theoretical level, this study contributes to the field by extending the capabilities of the random forest algorithm and adapting it to the domain of CRNs. The modified algorithm demonstrates the potential of machine learning techniques in addressing allocation challenges in wireless communication systems. The findings emphasize the importance of advanced algorithms in improving the efficiency and effectiveness of channel and resource allocation processes. 2023, Success Culture Press. All rights reserved.
Rajesh Kanna R.; Mohana Priya T.; Rohini V.; Ashok Immanuel V.; Senthilnathan T.; Kirubanand V.B.; Ibrahim A.
Journal of Logistics, Informatics and Service Science, Vol-10, No. 3, pp. 98-106.
https://doi.org/10.33168/JLISS.2023.0308 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167429928&doi=10.33168%2fJLISS.2023.0308&partnerID=40&md5=cd5f32598ce55cc095f1da1e1fd3d492
Rajesh Kanna R., Christ (Deemed to be University), India; Mohana Priya T., Christ (Deemed to be University), India; Rohini V., Christ (Deemed to be University), India; Ashok Immanuel V., Christ (Deemed to be University), India; Senthilnathan T., Christ (Deemed to be University), India; Kirubanand V.B., Christ (Deemed to be University), India; Ibrahim A., Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia
Improved reptile search algorithm with sequential assignment routing based false packet forwarding scheme for source location privacy protection on wireless sensor networks
false data forwarding; fitness function; metaheuristics; routing; source location privacy preserving; Wireless sensor networks
Source Location Privacy (SLP) in Wireless Sensor Networks (WSNs) refers to a set of techniques and strategies used to safeguard the anonymity and confidentiality of the locations of sensor nodes (SNs) that are the source of transmitted data within the network. This protection is important in different WSN application areas like environmental monitoring, surveillance, and healthcare systems, where the revelation of the accurate location of SNs can pose security and privacy risks. Therefore, this study presents metaheuristics with sequential assignment routing based false packet forwarding scheme (MSAR-FPFS) for source location privacy protection (SLPP) on WSN. The contributions of the MSAR-FPFS method revolve around enhancing SLP protection in WSNs through the introduction of dual-routing, SAR technique with phantom nodes (PNs), and an optimization algorithm. In the presented MSAR-FPFS method, PNs are used for the rotation of dummy packets using the SAR technique, which helps to prevent the adversary from original data transmission. Next, the MSAR-FPFS technique uses an improved reptile search algorithm (IRSA) for the optimal selection of routes for real packet transmission. Moreover, the IRSA technique computes a fitness function (FF) comprising three parameters namely residual energy (RE), distance to BS (DBS), and node degree (ND). The experimental evaluation of the MSAR-FPFS system was investigated under different factors and the outputs show the promising achievement of the MSAR-FPFS system compared to other existing models. 2024-IOS Press. All rights reserved.
Sankaralingam R.P.; Arunachalaperumal C.; Mary Anita E.A.
Journal of Intelligent and Fuzzy Systems, Vol-46, No. 1, pp. 1801-1812.
https://doi.org/10.3233/JIFS-233541 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184150251&doi=10.3233%2fJIFS-233541&partnerID=40&md5=ed7d76cb3c0d64b4e007fc3aca821c9b
Sankaralingam R.P., Department of MCA, Madha Engineering College, Tamil Nadu, Chennai, India; Arunachalaperumal C., Department of ECE, Ramco Institute of Technology, Tamil Nadu, Rajapalayam, India; Mary Anita E.A., Department of CSE, CHRIST University, Bengaluru, India
Improved Security of the Data Communication in VANET Environment Using ASCII-ECC Algorithm
American standard code for information interchange based elliptic curve cryptography (ASCII-ECC); Authentication; Cluster head (CH); Median based K-means (MKM); Modified cockroach swarm optimization (MCSO); On-board unit (OBU); Road side units (RSUs); Trusted authority (TA); Vehicular ad-hoc network (VANET)
Now-a-days, with the augmenting accident statistics, the Vehicular Ad-hoc Networks (VANET) are turning out to be more popular, helping in prevention of accidents in addition to damage to the vehicles together with populace. In VANET, message can well be transmitted within a pre-stated region to attain systems safety and also improveits efficacy. Ensuring authenticity of messages is a challenge in such dynamic environment. Though few researchers worked on this, security level is very less. Hence enhanced communicationsecurity on the VANET environment utilizing the American Standard Code for Information Interchange centred Elliptic Curve Cryptography (ASCII-ECC) is proposedin this paper. The network design is definedinitially. Subsequently, the entire vehicles get registered to the Trusted Authority (TA); similarly, all vehicle users areregistered with their On-Board Unit (OBU). This is followed byMedian-centred K-Means (MKM) performs the cluster formation together with Cluster Head Selection (CHS). Next, TA takes care of the verification procedure. Modified Cockroach Swarm Optimization (MCSO) calculates the shortest path and the ASCII-ECC carries out the secure data communication if the vehicle is an authorized one. If not, TA sends the alert message for discarding the request. The system renders better performance when it was weighed against the top-notch methods. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Wireless Personal Communications, Vol-128, No. 2, pp. 759-776.
Sajini S., Anna University, Chennai, India, SRM Institute of Science and Technology, Ramapuram, India; Anita E.A.M., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India; Janet J., Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India
Improved tweets in English text classification by LSTM neural network
English tweets; LSTM; Mendeley Data; Neural network; Word2Vec
This paper analyzes the performance of an LSTM-type neural network in the sentiment analysis task in tweets in English about the COVID-19 pandemic. Primarily, the organization and cleaning a database of tweets about the COVID-19 pandemic is performed. From the original database, two other databases through different discretizations of the polarities of the tweets using Heaviside-type functions are created. Vectorization of tweets using the Word2Vec word embedding technique is carried out. Computational implementations of LSTM neural networks to the context of our research problem are adapted. Analyzes and discussions on the feasibility of the proposed solution taking into account different types of hyperparametric adjustments in the neural network models is carried out. Publicly available databases organized through the Mendeley Data public data repository are used. 2023 IEEE.
Yashasvi B.N.; Samson Lsaac J.; Raghavendra S.; Tiwari M.; Ananthi K.
2023 1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023
https://doi.org/10.1109/ICAEECI58247.2023.10370893 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183557680&doi=10.1109%2fICAEECI58247.2023.10370893&partnerID=40&md5=dbec87fe8b9fff9c18cc6389e2f6ad72
Yashasvi B.N., R N S Institute of Technology, Bangalore, India; Samson Lsaac J., Karunya Lnstitute of Technology and Sciences, Department of Biomedical Engineering, Tamilnadu, Coimbatore, India; Raghavendra S., Christ Deemed to Be University, Department of Computer Science Engineering, Benguluru, India; Tiwari M., Bharati Vidyapeeth's College of Engineering, Department of Computer Science and Engineering, Delhi, India; Ananthi K., Sri Eshwar College of Engineering, Department of Computer Science and Business Systems, Tamilnadu, Kinathukadavu, India
Improvement in food preservation with nanozymes
Antimicrobial activity; Food preservation; Food quality; Nanozymes; Smart packaging
To ensure safety, quality, and extended shelf life of perishable food products, food preservation is a critical aspect of food industries. Concerns regarding the potential health risks and loss of nutritional value of food because of traditional methods of preservation such as using chemical additives and high temperatures have set the need for finding alternative methods of preservation, for the betterment of health and the environment. Enzymes have the potential to kill microorganisms. Enzymes such as oxidases, peroxidases, hydrolases, catalases, and others have been extensively studied for their microbicidal activities. However, natural enzymes have shortfalls as they can be easily denatured and cannot be recycled. Nanozymes have gained the limelight in recent years as they can be applied in food industries to overcome the shortfalls of natural enzymes. They embody the highly beneficial properties of both enzymes and nanoparticles at the same time. Due to their enzyme-mimicking properties and versatile applications, nanozymes have become more popular in the last few years. Nanozymes have evolved as a promising alternative for food preservation and the detection of various contaminants in food. However, before the integration of nanozymes into the food industry, several factors such as their stability, biocompatibility, longevity, toxicity, cost-effectiveness, scalability, and regulatory approval need to be addressed. This chapter discusses the concept of nanozymes, its classification, and various applications in food industries specially designed for preservation of food products. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.
Sridhar A.; Sunil C.K.; Al-Khayri J.M.; Nagella P.
Nanozymes: Approachable Bio-applications, pp. 361-383.
Sridhar A., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Sunil C.K., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Al-Khayri J.M., Department of Agricultural Biotechnology, College of Agriculture and Food Sciences, King Faisal University, Al-Ahsa, Saudi Arabia; Nagella P., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India
Improvement of Automatic Glioma Brain Tumor Detection Using Deep Convolutional Neural Networks
convolutional neurol networks; deep learning; higher grade gliomas; image segmentation; lower grade gliomas; voxel
This article introduces automatic brain tumor detection from a magnetic resonance image (MRI). It provides novel algorithms for extracting patches and segmentation trained with Convolutional Neural Network (CNN)'s to identify brain tumors. Further, this study provides deep learning and image segmentation with CNN algorithms. This contribution proposed two similar segmentation algorithms: one for the Higher Grade Gliomas (HGG) and the other for the Lower Grade Gliomas (LGG) for the brain tumor patients. The proposed algorithms (Intensity normalization, Patch extraction, Selecting the best patch, segmentation of HGG, and Segmentation of LGG) identify the gliomas and detect the stage of the tumor as per taking the MRI as input and segmented tumor from the MRIs and elaborated the four algorithms to detect HGG, and segmentation to detect the LGG works with CNN. The segmentation algorithm is compared with different existing algorithms and performs the automatic identification reasonably with high accuracy as per epochs generated with accuracy and loss curves. This article also described how transfer learning has helped extract the image and resolution of the image and increase the segmentation accuracy in the case of LGG patients. Copyright 2022, Mary Ann Liebert, Inc., publishers 2022.
Altameem A.; Mallikarjuna B.; Saudagar A.K.J.; Sharma M.; Poonia R.C.
Journal of Computational Biology, Vol-29, No. 6, pp. 530-544.
ISSN: 10665277; PubMed ID: 35235381; CODEN: JCOBE
https://doi.org/10.1089/cmb.2021.0280 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131665529&doi=10.1089%2fcmb.2021.0280&partnerID=40&md5=86242a97b59a579c63394dfb7f07852c
Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, Saudi Arabia; Mallikarjuna B., School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia; Sharma M., School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India
Improvement of Speech Emotion Recognition by Deep Convolutional Neural Network and Speech Features
Data augmentation; Deep convolutional neural network; Emotion recognition; Speech dataset; Speech features
Speech emotion recognition (SER) is a dynamic area of research which includes features extraction, classification and adaptation of speech emotion dataset. There are many applications where human emotions play a vital role for giving smart solutions. Some of these applications are vehicle communications, classification of satisfied and unsatisfied customers in call centers, in-car board system based on information on drivers mental state, human-computer interaction system and others. In this contribution, an improved emotion recognition technique has been proposed with Deep Convolutional Neural Network (DCNN) by using both speech spectral and prosodic features to classify seven human emotionsanger, disgust, fear, happiness, neutral, sadness and surprise. The proposed idea is implemented on different datasets such as RAVDESS, SAVEE, TESS and CREMA-D with accuracy of 96.54%, 92.38%, 99.42% and 87.90%, respectively, and compared with other pre-defined machine learning and deep learning methods. To test the real-time accuracy of the model, it has been implemented on the combined datasets with accuracy of 90.27%. This research can be useful for development of smart applications in mobile devices, household robots and online learning management system. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-608, pp. 117-129.
Springer Science and Business Media Deutschland GmbH
Mohanty A., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Cherukuri R.C., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Prusty A.R., DGT, RDSDE, NSTI(W), West Bengal, Kolkata, India
Improvement to Recommendation system using Hybrid techniques
Collaborative Filtering method; Content-based filtering method; Hybrid Recommendation; Limitation; Movie Recommendation; User ratings
Currently, recommendation systems are a common tool for providing individualized recommendations and item information to users. For personalization in the recommendation system, there are a variety of strategies that can be used. To improve system performance and offset the shortcomings of individual recommendation strategies, a hybrid recommender system integrates two or even more recommendation techniques. The demand to summarize all of the knowledge on actual methods and algorithms utilized in hybrid recommended systems necessitates the need for a systematic review in the domain. These materials will be employed to aid in the development of an auto-switching hybrid recommender system. In the content-based filtering technique, the algorithm is based on the contents of items and the collaborative filtering technique algorithm combines the relationship between user and item. Both of the approaches of recommendation system are suffers from some limitations, this is a big issue to predict better recommendations to the user. Hybrid systems are introduced to overcome the main limitations of both techniques. These systems are made with a combination of content-based and collaborative filtering techniques and have advantages of both techniques. With the use of hybrid systems, the quality of recommendations is improved. Hybrid recommendation systems use previous data of a user to find his/her interest and then they target the set of an adjacent user which is similar with that user and according to adjacent user recommend things to the user. Hybrid systems offer the items that share the common things that a user rated highly (Content-based filtering) and make suggestions by comparing the interest of a similar user (Collaborative filtering). 2022 IEEE.
2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 778-782.
https://doi.org/10.1109/ICACITE53722.2022.9823879 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135492454&doi=10.1109%2fICACITE53722.2022.9823879&partnerID=40&md5=c6569f4ff71fcb2f09fb9d7977dfd85d
Ramesh R., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Pune, India; Vijayalakshmi S., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Pune, India
Improving Consumer Engagement with AI Chatbots: Exploring Perceived Humanness, Social Presence, and Interactivity Factors
AI chatbots; Artificial intelligence; Consumer engagement; Customer interaction; Human-computer interaction; Interactivity; Perceived humanness; Social presence; User experience
In many consumer industries, AI robots are becoming more and more popular because they let businesses communicate with their customers in a cheap and quick way. However, how well these measures work rests on how real and present people think they are in social situations. The main things that affect how customers deal with AI chatbots are looked into in this research. These are interaction, social presence, and perceived humanity.A wide range of users will be asked to fill out quantitative polls that will be used to judge how humanlike AI chatbots are, how well they can interact with others, and how much they interact with people. Additionally, performing qualitative interviews will give you a fuller picture of what customers want and how they interact with AI chatbots. Companies can make their chatbot exchanges with customers better by figuring out what makes the bots act like humans: friendly, interested, and sociable. This will allow them to make chatbots that are very specific to their customers' needs and tastes. The goal of this researchprogramme is to make customers happier, more loyal to brands, and have better experiences by creating AI chatbots that can have conversations with people like real people. 2024 IEEE.
Tripathi A.P.; Dhaundiyal P.; Sharma K.; Sukheswala J.; Dhiman A.; Sharma A.K.
Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024
https://doi.org/10.1109/ACCAI61061.2024.10601926 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200559335&doi=10.1109%2fACCAI61061.2024.10601926&partnerID=40&md5=ecec965d4af409edc0320da4b08432c7
Tripathi A.P., Christ Deemed to Be University, Uttar Pradesh, India; Dhaundiyal P., Christ Deemed to Be University, Uttar Pradesh, India; Sharma K., K P B Hinduja College of Commerce, Maharashtra, India; Sukheswala J., K P B Hinduja College of Commerce, Maharashtra, India; Dhiman A., Manipal University Jaipur, Rajasthan, India; Sharma A.K., University of Technology and Applied Sciences, College of Economics and Business Administration, Salalah, Oman
Improving crop production using an agro-deep learning framework in precision agriculture
Agricultural technology; Agro deep learning framework; Artificial intelligence in agriculture; Crop monitoring; Crop yield prediction; Data analysis; Deep learning; Neural networks; Precision agriculture; Smart farming
Background: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets. These datasets include variables such as soil moisture, temperature, and humidity, all of which are essential to understanding and predicting crop behavior. By leveraging deep learning models, the framework seeks to improve decision-making processes, detect potential crop problems early, and boost agricultural productivity. Results: The study found that the Agro Deep Learning Framework (ADLF) achieved an accuracy of 85.41%, precision of 84.87%, recall of 84.24%, and an F1-Score of 88.91%, indicating strong predictive capabilities for improving crop management. The false negative rate was 91.17% and the false positive rate was 89.82%, highlighting the framework's ability to correctly detect issues while minimizing errors. These results suggest that ADLF can significantly enhance decision-making in precision agriculture, leading to improved crop yield and reduced agricultural losses. Conclusions: The ADLF can significantly improve precision agriculture by leveraging deep learning to process complex datasets and provide valuable insights into crop management. The framework allows farmers to detect issues early, optimize resource use, and improve yields. The study demonstrates that AI-driven agriculture has the potential to revolutionize farming, making it more efficient and sustainable. Future research could focus on further refining the model and exploring its applicability across different types of crops and farming environments. The Author(s) 2024.
Logeshwaran J.; Srivastava D.; Kumar K.S.; Rex M.J.; Al-Rasheed A.; Getahun M.; Soufiene B.O.
ISSN: 14712105; PubMed ID: 39487390; CODEN: BBMIC
Logeshwaran J., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India; Srivastava D., Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, India; Kumar K.S., Department of Computing Technologies, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Rex M.J., Department of Computing Technologies, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Al-Rasheed A., Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Getahun M., Department of Computer Science and Information Technology, College of Engineering and Technology, Kebri Dehar University, Kebri Dehar, Ethiopia; Soufiene B.O., PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse, Tunisia