Early-Stage Cervical Cancer Detection via Ensemble Learning and Image Feature Integration
cervical cancer; Deep learning; DenseNet121; feature integration; K means; ResNet50
Cervical cancer ranks as the fourth most common malignancy worldwide and poses a significant threat, particularly in resource-constrained regions. Automated diagnostic approaches, leveraging colposcope image analysis, hold great promise in curbing the impact of this disease. In this study, we introduce an ensemble of machine learning and deep learning models, including DenseNet 121, ResNet 50, and XGBoost to classify the cervical intraepithelial neoplasia. A novel feature integration is proposed which ensembles the results of the individual models in five fold validation process. Our methodology is deployed on a dataset sourced from the International Agency for Cancer Research. The results from the proposed framework have shown to be accurate, robust and dependable. This method can be utilized for achieving automatic identification of cervical cancer in early stages so it can be treated appropriately. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 112-122.
Springer Science and Business Media Deutschland GmbH
Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India
An Integration of AI Technique in the Field of Healthcare Industry
AI; Biological Computation; Biomedicine; Disease Diagnoses; Healthcare; Living Support; Neural Networks; Predictive Studies; Research; Scientific Successes; Technological Environment
Over the last few years, the field of intelligent machines (AI) has experienced fast improvements in software algorithms to hardware deployment, and varied uses, especially in the area of healthcare. This thorough study aims to capture recent developments in AI uses within biomedicine, spanning disease diagnoses, living support, biological computation, and research. The primary goal is to record recent scientific successes, discern what is happening in the technological environment, perceive the enormous future scope of AI on biomedicine along and serve as a source of stimulus for researchers through related fields. It is obvious that, similar to the development of AI itself, the use of it in biology continues to remain in its infant state. This review expects ongoing breakthroughs and improvements that will push the limits and broaden the range of AI uses in the near future. In order to communicate the changing possibility of AI in biology, the study dives into individual case studies. These include anticipating of epileptic seizure events and the uses of AI in treating a faulty urine bladder. By studying these cases, the overview seeks to explain the visible impact of AI off healthcare and reinforce the chance of immediate developments in this evolving and promising field. 2024 IEEE.
Priyadharsini P.; Khare N.; Praveenraj D.D.W.; Senthurya S.; Arunkumar B.; Reddy G.V.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1981-1986.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICACITE60783.2024.10617363 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201800827&doi=10.1109%2fICACITE60783.2024.10617363&partnerID=40&md5=cd0239100bf98c7c6aa2f549eb7cd28a
Priyadharsini P., Nehru Institute of Engineering and Technology, T.M.Palayam, Coimbatore, India; Khare N., Ies University, Ies Insititute of Technology and Management, M.P., Bhopal, 462044, India; Praveenraj D.D.W., Christ University, Bangalore, India; Senthurya S., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Arunkumar B., Karpagam Academy of Higher Education, Coimbatore, India; Reddy G.V., Cmr Technical Campus, Hyderabad, Medchal, India
Towards a Framework for Supply Chain Financing for Order-Level Risk Prediction: An Innovative Stacked A-GRU Based Technique
Gated Recurrent Unit (GRU); Stacked Autoencoder Feature Extraction (SAFE); Supply Chain Finance
Order financing is changing the game in the banking and financial supply chain industry. It's great for SMEs and opens up new revenue streams for logistics and finance companies. But in order to find the weak spots offered by banks and other financial institutions, companies need to undertake thorough risk assessments right now. Careful timing is crucial for training the model, extracting features, and preprocessing. Outlier identification and missing value handling are the first steps in preprocessing, which also includes normalization and standardization to improve data integrity and reduce unit discrepancies. Principal component analysis makes use of multivariate statistics to aid in feature extraction, guaranteeing effective data representation. Careful consideration of every detail is required during the training of a Stacked-A-GRU model, which follows attribute selection. Impressively outperforming state-of-the-art algorithms SAFE and GRU, the suggested solution achieves a remarkable correctness rating of 97.34%, indicating notable progress in predicting accuracy. 2024 IEEE.
Amarnath Reddy B.; Vinesh Kumar G.; Dhiliphan Kumar M.; Veena S.; Chauhan A.; Anwer Basha H.
1st International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024
Amarnath Reddy B., Vignana Jyothi Institute of Management, Department of Finance, Telangana, Hyderabad, India; Vinesh Kumar G., Vardhaman College of Engineering, Department of Management Studies, Telangana, Hyderabad, India; Dhiliphan Kumar M., Deemed University, Business Administration, Kalasalingam Academy of Research and Education, Virudhunagar, India; Veena S., Deemed University, Business Administration, Kalasalingam Academy of Research and Education, Virudhunagar, India; Chauhan A., School of sciences, CHRIST (Deemed to be university), Department of life sciences, Karnataka, Bengaluru, India; Anwer Basha H., Saveetha College of Liberal Arts and Sciences, Saveetha Institute of Medical and Technical Sciences, Department of Computer Science, Chennai, India
The Role of IOT in Creating SC'S through Ultra Fast Updation of the Status for Accurate Action Plan
AI-powered systems; blockchain; data management; healthcare; IoT; machine learning; smart cities; smart technology; urban efficiency; wireless sensor networks
The idea of a smart city includes the merging of technologies and advances aimed at improving urban efficiency, scientific progress, the preservation of the environment, and social inclusion. Coined in the year 2000, the term became widely used in politics, business, management, and urban planning groups to drive tech-based changes in urban areas. It reacts to the difficulties posed by postindustrial communities handling problems such as pollution to the environment, demographic changes, population growth, health care monetary crises, and resource shortages. Beyond technical answers, the smart city idea includes non-technical innovations for healthy urban life. Particularly encouraging is the application that uses Internet of The circumstances (IoT)based sensors in healthcare, applying machine learning for effective data management. This paper discusses the application of AI-powered Ai and Wireless Sensor Networks, more commonly known as the field of health care, acting as a basic study to understand the impact of IoT in smart cities, especially in healthcare, for the sake of future research. 2024 IEEE.
Hariharasudhan S.; Subasree S.; Bhuvaneswari M.; Victor M.; Kumar R.; Kumar K.S.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 188-193.
https://doi.org/10.1109/ICACITE60783.2024.10616499 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201812358&doi=10.1109%2fICACITE60783.2024.10616499&partnerID=40&md5=b9d2d82983a7c77ec9ba3b6dd903a342
Hariharasudhan S., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Information Technology, India; Subasree S., Nehru Institute of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nadu, T.M. Palayam, India; Bhuvaneswari M., Karpagam Academy of Higher Education, Department of Biomedical Engineering, TN, Coimbatore, India; Victor M., Christ University, School of Business and Management, Bangalore, India; Kumar R., Ies University, Department of Mech Engg, Ies Institute of Technology and Management, M.P., Bhopal, 462044, India; Kumar K.S., Vel Tech Rangarajan Dr. Sagunthala R D Inst. of Science and Technology (Deemed to Be University), Chennai, Tamil Nadu, Avadi, India
Advances in Crime Identification: A Machine Learning Perspective
Crime Patterns; Crime prevention; Detection; Machine Learning; Prediction
Crime profoundly impacts individuals, communities, and families. Technological advancements have provided perpetrators with new opportunities for criminal activities. The primary objective of the police department is to resolve crimes, ensuring justice for the victims. Additionally, preventing such incidents is crucial for creating a safer world. The landscape of criminal justice has undergone a significant shift with the integration of machine learning techniques, unlocking unparalleled potential for accuracy and efficiency. This study thoroughly examines the concept of various applications of machine learning in crime detection, prediction, and prevention. We examine the evolution of these technologies, from early developments to state- of-the-art methodologies, conducting a thorough analysis of their strengths, limitations, and ethical considerations. Moreover, the paper sheds light on crimes discussed in academic circles, serving as a repository for scholars and researchers. This facilitates informed discussions and guides future research endeavours. 2024 IEEE.
Rachana N.; Norton Stanley S.A.; Sandeep J.; Sreeja C.S.
Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024, pp. 68-74.
Rachana N., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India; Norton Stanley S.A., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India; Sandeep J., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India; Sreeja C.S., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India
A Systematic Review of Various Advancements Implementation in the Field of Crop (Plant) Production
agricultural robots; challenges; crop protection; Pest management
An essential component of agricultural output is pest management, especially in fertigation-based farming. Although fertigation systems in Malaysia are beneficial for irrigation and fertilization, they frequently don't have effective pest control techniques. Because pests usually live beneath crop leaves, hand spraying is difficult and labor-intensive. Insect pests have the power to seriously harm, weaken, or even kill agricultural plants, which can lead to lower yields, worse-quality goods, and unsalable outcomes. Furthermore, insects may still cause harm to processed or stored items after harvest. Therefore, creating an autonomous pesticide sprayer specifically designed for chilli fertigation systems is the main goal of this research. The main goal is to create a sprayer arm that is flexible enough to reach under crop leaves. The goal of this project is to build an autonomous, unmanned pesticide sprayer. The goal of autonomous operation is to reduce the amount of dangerous pesticides that people are exposed to, especially in enclosed spaces like greenhouses. In addition, the sprayer arm's adaptability to different agricultural circumstances makes it a valuable tool in both greenhouse and outdoor settings. It is expected that the successful adoption of the autonomous pesticide sprayer would completely transform fertigation-based farming's approach to pest management. 2024 IEEE.
Babu P.B.; Bisht D.; Shilpa K.; Alzubaidi L.H.; Sharma A.; Rajesh Kanna R.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1306-1310.
Babu P.B., Aditya Engineering College, Department of Electronics and Communication Engineering, Surampalem, India; Bisht D., Uttaranchal Institute of Management, Uttaranchal University, Department of Management, Dehradun, 248007, India; Shilpa K., Cmr Technical Campus, Department of Cse, Telangana, Hyderabad, India; Alzubaidi L.H., The Islamic University, Najaf, Iraq; Sharma A., Chitkara University, Chitkara Centre for Research and Development, Himachal Pradesh, 174103, India; Rajesh Kanna R., Christ(Deemed to Be University), Department of Cse, Bangalore, India
Enhanced Security in Payment Gateways Through Face Detection: An Advanced Approach Using DenseNet 121- BiLSTM Models
Facial Recognition (FR); Field Programmable Gate Arrays (FPGA); Payment System
Because it is one of the most promising applications of image analysis, face recognition has been the subject of intense research and development for many decades. Many modern identification and verification requirements have found a potential new home with the introduction of face recognition (FR) technology. Facial recognition is just one of numerous uses for biometric pattern recognition algorithms. Sequencing is essential for many tasks, including as feature extraction, model training, and preprocessing. Eliminating background noise and obtaining dense vertical edges are part of the preprocessing procedures. Facial feature extraction will be employed to extract features after feature extraction. Use attributes cautiously when training a Desnet121-BiLSTM model. In every respect, the suggested method outperforms two state-of-the-art algorithms, Desnet121 and BiLSTM. An accuracy rating of 97.19% was indicative of a considerable improvement in the figures. 2024 IEEE.
Gowsic K.; Sowmiya M.; Nirmala Devi M.; Chauhan A.; Arul Mary Rexy V.; Singh D.
Gowsic K., Mahendra Engineering College (Autonomous), Department of Computer Science and Engineering, Namakkal, India; Sowmiya M., Mahendra Engineering College (Autonomous), Department of Computer Science and Engineering, Namakkal, India; Nirmala Devi M., School of Sciences, CHRIST (Deemed to be University), Department of Life Sciences, Karnataka, Bengaluru, India; Chauhan A., St. Martin's Engineering College, Department of English, Secunderabad, India; Arul Mary Rexy V., Saveetha College of Liberal Arts and Sciences, SIMATS, Department of Commerce, Kuthambakkam, India; Singh D., School of Management Sciences, Department of Applied Science, Lucknow, India
A Study Examining the Relationship Between College Students Demographic Characteristics and Financial Literacy- With Special Reference to a Union Territory in India
Borrowings; Demographic Behaviour; Financial Literacy; Financial well-being
Over the past ten years, the importance of financial literacy has been growing across the world. Prior research has found that a lack of financial knowledge can have several negative consequences, the inability to make correct financial decisions, high levels of debt, high-cost borrowing and misuse of credit. Limited knowledge of financial concepts also has an impact on the economy as a whole. This study attempts to measure the level of financial literacy of college students in Goa, India. A total of 378 respondents were surveyed and their level of financial literacy was measured through a percentage analysis. The respondents level of financial literacy was also studied concerning various demographic characteristics. The results show an association between financial literacy and sex, level of education, field of education, percentage of respondents and income level. The findings of the study suggest a need for the strengthening of initiatives by policymakers to introduce the concept of financial literacy for students all over the state as well as the country, in every field. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Dave K.A.; Ashfaque M.Z.; Guruprasad D.; Narasimha M.H.; Kazim S.; Mukthar K.P.J.
Lecture Notes in Networks and Systems, Vol-1080 LNNS, pp. 412-425.
Dave K.A., St. Josephs College of Commerce (Autonomous), Karnataka, Bengaluru, India; Ashfaque M.Z., St. Josephs College of Commerce (Autonomous), Karnataka, Bengaluru, India; Guruprasad D., Department of Commerce, Manipal Academy of Higher Education, Bengaluru, India; Narasimha M.H., CHRIST (Deemed to be University), Bengaluru, India; Kazim S., Jain (Deemed to be University) CMS, Bengaluru, India; Mukthar K.P.J., Kristu Jayanti College (Autonomous), Karnataka, Bengaluru, India
The Roadmap Implementation for Smart Cities via High Level Communication Technology
6g technologies; fog computing; Internet of Things; Security Model; Smart Cities
This paper explores the integration of smart city technologies to enhance urban living standards and optimize city services. Leveraging modern 6 g technologies such as the Internet of Things (IoT), fog computing, robotics, and predictive analytics, smart cities aim to improve efficiency across various sectors including healthcare, transportation energy, and education. Through real-time monitoring enabled by Wireless Sensor Networks (WSNs), IoT devices, and unmanned aerial vehicles (UAVs), smart cities can efficiently manage resources and infrastructure. In this paper proposes an architectural design to improve urban security using 6G technology and present an extensively light weighted secured mechanism for easing intricacy in medium channel. This study validate and test arithmetical framework with respect to extensively light weighted secured mechanism. The instant study explores the background of defined urban security framework focusing on Internet of Things technology and its application in urban development. This study also introduces a lightweight edge fogging algorithm to optimize general computer network topologies. The proposed framework is thoroughly analyzed and evaluated through computational analysis, simulation, and comparison with existing models. The results show that the proposed framework with 6 G technology and lightweight security model shows better performance, less service downtime, and higher connectivity with current models. 2024 IEEE.
Gupta S.; Al-Fatlawy R.R.; Aggarwal S.; Divya Shree V.; Devadas G.; Lazer N.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 868-872.
https://doi.org/10.1109/ICACITE60783.2024.10616448 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201820512&doi=10.1109%2fICACITE60783.2024.10616448&partnerID=40&md5=8bcd90d428e1e2ae3d47d18810fd7376
Gupta S., Chitkara University, Chitkara Centre for Research and Development, Himachal Pradesh, India; Al-Fatlawy R.R., The Islamic University, Najaf, Iraq; Aggarwal S., Uttaranchal University, Uttaranchal Institute of Technology, Department of Mechanical Engineering, Dehradun, India; Divya Shree V., Christ (Deemed to Be University), School of Business and Management, Bangalore Yeshwanthpur campus, India; Devadas G., Cmr College of Engineering & Technology, Department of Eee, India; Lazer N., Saveetha Institute of Medical and Technical Sciences, Saveetha University, Electronics and Communication Engineering, Saveetha School of Engineering, Chennai, India
Knee-Osteoarthritis Detection Using Deep Learning
Artificial neural networks; CNN; Deep learning; Knee osteoarthritis
Arthritis is a condition that causes pain, stiffness, inflammation, and other symptoms in one or more joints. It is more common in older adults and tends to worsen with age. There are different types of arthritis, but osteoarthritis is the most prevalent. A study discusses the use of Convolutional Neural Networks (CNN) for detecting knee osteoarthritis. CNN is a deep learning algorithm that can analyze data and classify images accurately, like the human brain. The purpose of this study is to classify different knee X-ray images to predict the severity of the disorder, allowing for early detection and lifestyle changes to prevent the disease from worsening. An online tool has been developed to diagnose knee osteoarthritis and provide remedies based on various K-grade predictions. This tool can help patients understand their knee's condition and take necessary measures to manage the disease. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Garg A.; Suryavanshi S.; James J.; Srivastava S.
Lecture Notes in Networks and Systems, Vol-994 LNNS, pp. 75-87.
Garg A., CHRIST (Deemed to be University), NCR, Delhi, India; Suryavanshi S., CHRIST (Deemed to be University), NCR, Delhi, India; James J., CHRIST (Deemed to be University), NCR, Delhi, India; Srivastava S., CHRIST (Deemed to be University), NCR, Delhi, India
A Implementation of Integration of AI and IOT Along with Metaverse Technology in the Field of Healthcare Industry
In the evolving panorama of healthcare, the appearance of Metaverse technology emerges as a transformative pressure, redefining traditional paradigms of healthcare shipping and education. This systematic assessment delves into the multifaceted impact of Metaverse technology, encapsulating their role in revolutionizing healthcare through modern-day academic frameworks, patient care interventions, and groundbreaking enhancements in medical imaging. Through an in-depth assessment of present-day literature, this observe illuminates the Metaverse's potential to facilitate immersive mastering tales, allow far flung interventions, and enhance the pleasant of scientific diagnostics and treatment making plans with its 3 -dimensional virtual environments. The findings underscore a burgeoning growth in Metaverse packages inner healthcare, highlighting its capability to noticeably beautify healthcare outcomes, affected person engagement, and expert abilities. Consequently, this evaluate advocates for the prolonged integration of Metaverse generation in healthcare, urging stakeholders to embody the ones enhancements and adapt to the following digital transformation in healthcare services and education. 2024 IEEE.
Joshi A.; Sharon Sophia J.; Bhatia H.S.; Kirubakaran S.; Adnan M.M.; Krishnammal P.M.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 907-911.
https://doi.org/10.1109/ICACITE60783.2024.10617296 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201791290&doi=10.1109%2fICACITE60783.2024.10617296&partnerID=40&md5=a7f2c5f902d2f8eb9c977fd4b3092dbd
Joshi A., Uttaranchal University, School of Liberal Arts, Department of Liberal Arts, Dehradun, 248007, India; Sharon Sophia J., Christ (Deemed to Be) University, School of Business and Management, Bangalore Yeshwanthpur Campus, India; Bhatia H.S., Chitkara University, Centre of Research Impact and Outreach, Punjab, Rajpura, 140401, India; Kirubakaran S., Cmr College of Engineering & Technology, Department of Cse, India; Adnan M.M., The Islamic University, Najaf, Iraq; Krishnammal P.M., VIT-AP University, School of Electronics Engineering, Andhra Pradesh, Amaravati, India
Comprehensive Comparative Analysis of Breast Cancer Forecasting Using Machine Learning Algorithms and Feature Selection Methods
artificial neural network; Breast cancer; CT scan; deep neural network; machine learning; MRI scan; support vector machine
This research leveraged machine learning models, including Deep Neural Network (DNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM), to predict breast cancer from CT and MRI scans. A dataset comprising 2345 instances of malignant and benign cases was meticulously curated, with 80% allocated for training and 20% for testing. The experimental results revealed the DNN as the top-performing model, exhibiting remarkable accuracy (95.2%), precision (94.8%), recall (95.6%), and F1 score (95.2%). The ANN also demonstrated strong performance, achieving an accuracy of 93.6% with balanced precision and recall scores. In contrast, the SVM, while respectable, fell slightly behind the machine learning models in terms of overall accuracy and performance. Detailed confusion matrices further elucidated the models capabilities and limitations, providing valuable insights into their diagnostic prowess. These findings hold great promise for breast cancer diagnosis, offering a non-invasive and highly accurate means of early detection. Such a tool has the potential to enhance patient care, reduce the strain on healthcare systems, and alleviate patient anxiety. The success of this research highlights the transformative impact of advanced machine learning in medical imaging and diagnosis, signaling a path toward more efficient and effective healthcare solutions. Further research and clinical validation are essential to translate these promising results into practical applications that can positively impact patients and healthcare providers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Singh J.; Dharani M.; Shelke N.A.; Sajid M.; Alsahlanee A.T.R.; Upreti K.
Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 123-131.
Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Dharani M., Department of Electronics and Communication Engineering, K. S. R. Institute for Engineering and Technology, Tiruchengode, India; Shelke N.A., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Sajid M., Department of Computer Science, Aligarh Muslim University, Aligarh, India; Alsahlanee A.T.R., Development and Continuous Education Center, University of Thi-Qar, Thi-Qar, Iraq; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Ghaziabad, India
Unmanned Artificial Intelligence-Based Financial Volatility Prediction in International Stock Market
Financial Volatility Prediction; International Stock Market; Machine Learning; Predictive Analytics; Unmanned Artificial Intelligence
This study investigates the capacity of autonomous artificial intelligence to predict the volatility of the worldwide stock market and proposes an innovative approach utilizing cutting-edge AI algorithms. A comprehensive literature review examines the evolution of financial prediction systems and the transformative effects of artificial intelligence in improving predictive capabilities. The AI system under consideration employs machine learning techniques more effectively than traditional methods for collecting and predicting financial volatility. The strategy heavily relies on automated data capture, preprocessing, and model training. A recall of 76%, an accuracy rate of 94%, a precision of 81%, an area under the curve of 0.87, and a sharp ratio of 1.25 comprise the model's impressive specifications. This research illuminates the prospective financial applications of artificial intelligence and provides a way to navigate the intricacies of international stock markets. 2024 IEEE.
Rani T.; Vijayakumar L.; Chandirasekar B.; Geethanjali N.; Roja M.P.; Salunkhe H.A.
2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024
Rani T., SRM-IST, Fsh, Institute of Science and Technology, Department of Commerce, Chennai, Ramapuram, India; Vijayakumar L., Saveetha Institute of Medical and Technical Sciences, Saveetha College of Liberal Arts & Sciences, Department of Commerce, Tamil Nadu, Kanchipuram, India; Chandirasekar B., Saveetha Institute of Medical and Technical Sciences, Saveetha College of Liberal Arts & Sciences, Department of Commerce, Tamil Nadu, Kanchipuram, India; Geethanjali N., Psna College of Engineering and Technology, Department of Management Studies, Tamil Nadu, Dindigul, India; Roja M.P., Psna College of Engineering and Technology, Department of Management Studies, Tamil Nadu, Dindigul, India; Salunkhe H.A., Christ University (Deemed to Be University), Department School of Business and Management, Bangalore, India
The Optimization of Output of Wind Turbine with the Ongoing Grid System through BP Method Using ANN
ANN; automation; BP; design optimization; wind turbines
Wind turbines are intricate devices that need careful planning, evaluation, and installation to guarantee peak performance under a range of environmental circumstances. Comprehensive load calculations, performance evaluations, and iterative optimisation processes are all part of the design process. However, complex simulation techniques are required to adequately depict the non-linear behaviour of wind turbine systems because of their complicated structure. Automation of optimisation processes and simulation executions is crucial to optimise the design process and manage the large number of simulations that are needed. This work provides a thorough framework using back propagation (BP) and artificial neural networks (ANN) for simulation and optimization that will make it easier to manage and automate the execution of iterative simulations during the design and development of wind turbines. The framework's main goals are to make design load case simulations easier and optimise activities more automatically. The framework makes it possible to optimise wind turbine systems and explore design options more effectively by automating these procedures. Three example optimisation jobs illustrate the framework's versatility and functionality. 2024 IEEE.
Prabha B.; Donald C.; Dhandapani V.K.; Sura S.R.; Sayed R.M.; Jweeg M.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 400-403.
Prabha B., Vellore Institute of Technology, School of Computer Science and Engineering (SCOPE), Chennai Campus, Chennai, India; Donald C., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, India; Dhandapani V.K., TX, United States; Sura S.R., Gitam School of Technology, Gitam (Deemed to Be University), Department of Electrical Electronics and Communication Engineering, Andhra Pradesh, Visakhapatnam, India; Sayed R.M., National University of Science and Technology, Collage of Nursing, Dhi Qar, 64001, Iraq; Jweeg M., Al-Farahidi University, Baghdad, Iraq
Analyzing the Performance of Conformable and Non-Conformable Patch Antennas
Graphene; MCNT; Patch Antenna; Return Loss; SCNT; VSWR
This paper presents a performance analysis between a conventional triangular shaped patch antenna and a future reconfigurable patch antenna. There are different materials with different electronic properties for the simulation of triangular shaped patch antenna. All the materials for the triangular patch antenna are simulated using FEKO tool. Materials selected for triangular patch antenna are Copper, Single-wall Carbon Nano-tube (SCNT), Multiple-wall Carbon Nano-tube (MCNT) and Graphene. For the futuristic antennas, cotton fabric based reconfigurable patch antenna is also analyzed and compared with triangular shaped patch antenna. Graphene based triangular patch antenna has been analyzed best out of other materials. Reconfigurable cotton fabric-based patch antenna provides better bandwidth and results are validated through simulation and experimental setup. 2024 IEEE.
Dhariwal S.; Lamba V.K.; Pathak J.; Choudhary A.; Kumar G.
2024 5th International Conference for Emerging Technology, INCET 2024
Dhariwal S., Alliance University, Bengaluru, India; Lamba V.K., Symbiosis Institute of Technology, Pune, India; Pathak J., School of Engg. and Techn., Christ University, Bangalore, India; Choudhary A., Alliance University, Bengaluru, India; Kumar G., Alliance University, Bengaluru, India
A Quality of Service Study for Downlink Scheduling Algorithms in Mobile Networks
Applications; Channel Aware; Mobile Networks; Quality of Service; Scheduling
Internet usage and the number of applications/users growth is going in an unprecedented manner. In these days, lot of users are changed themselves to use internet-based applications rather than traditional voice service. The fundamental of voice-based communication is shifted to packet data access for satisfying the human needs through internet based mobile applications. 4G network is an IP supported rising technology for the past decade and at present also because of un availability service of 5G in all the places. Still, 4G is ruling the globe and the number of subscribers kept growing only. In these days, this remains on the list of latest research topics. Under 4G technology lot of research problems are exist like QoS, Uplink and Downlink Scheduling, Security, Mobility etc., Inspite of discussing that several issues, this paper mainly focusing the QoS in Downlink scheduling algorithms. Also, it presents the issues of various existing QoS downlink scheduling algorithms, names, QoS aware/unaware, parameters used/simulated, drawbacks of those algorithms and result verifications etc. Packet scheduling plays a crucial role for providing Quality of Service (QoS) to the mobile users. Ultimately, it gives some suggestions to explore more further about QoS based research work in Mobile Networks. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Krishnamoorthy R.; Nagaraj S.; Robert N.R.; Donald A.C.; Suresh K.; Cynthia T.
Lecture Notes in Networks and Systems, Vol-1050 LNNS, pp. 436-443.
Krishnamoorthy R., School of CS and IT, JAIN (Deemed to Be University), Bangalore, India; Nagaraj S., Departmentof CSE, Alliance University, Bengaluru, India; Robert N.R., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Donald A.C., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Suresh K., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Cynthia T., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India
On Two-Dimensional Approximate Pattern Matching Using Fuzzy Automata
Array matching; Fuzzy automata; Pattern matching; Position specific approximate pattern matching
Pattern matching has been extensively studied in the last few decades, owing to its great contribution in various fields such as search engines, computational biology, etc. Several real-life situations require patterns that allow ambiguity in specified positions. In this paper, one-dimensional and two-dimensional approximate pattern matching models have been constructed using fuzzy automata. The similarity function used in fuzzy automata enables the occurrence of all exact and similar one-dimensional and two-dimensional patterns. This kind of searching approximate patterns is not possible with regular search models. The time complexity of the proposed algorithm has also been analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-994 LNNS, pp. 595-612.
John Kaspar A., Department of mathematics, Christ University, Karnataka, Bengaluru, 560029, India; Sheena Christy D.K., Department of mathematics, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India
Attention Based Meta-Module to Integrate Cervigrams with Clinical Data for Cervical Cancer Identification
attention; cervical cancer; Deep learning; EfficientNet; feature integration
Cervical cancer remains a significant burden on public health, particularly in developing countries, where its malignancy and mortality rates are alarmingly high. Early diagnosis stands as a pivotal factor in effectively treating and potentially curing the cervical cancer. This study introduces a novel approach of meta module based on recurrent gate architecture designed to enhance the classification of cervix images efficiently. This innovative framework incorporates a meta module capable of dynamically selecting image modalities most pertinent attributes. Furthermore, it integrates clinical data with extracted image features and employs a range of EfficientNet architectures (B0-B5) for image classification. Our results indicate that the EfficientNet B5 architecture outperforms its counterparts, achieving an AUC (Area Under the Curve) score of 55.1 and an F1-Score of 75.1. Overall, this work represents a crucial step towards improving the early detection of cervical cancer, which in turn can lead to more effective treatment strategies and, ultimately, better outcomes for patients worldwide. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 286-295.
The Development of Structured Tele Based Medicine Concept Using Programmable System
and telemedicine; Binomial law; distributed adaptation; videoconferencing
In the medical field, clinics and hospitals frequently use dispersed applications like telediagnosis. These apps must nevertheless provide information security in order to properly transit security measures like firewalls and proxies. The User Datagram Protocol (UDP) is often recommended for videoconferencing applications because of its low latency; nevertheless, security problems occur when UDP tries to pass through firewalls and proxies without a specified set of fixed ports. In order to overcome these obstacles, this study presents a revolutionary platform that uses Transmission Control Protocol (TCP) rather of UDP: VAGABOND, which stands for 'Video Adaptation framework, across security gateways, based on transcription,' Adaptation Proxies (APs) that are designed to accommodate user preferences, device variations, and dynamic changes in network capacity comprise VAGABOND. This platform's versatility at the user and network levels guarantees seamless operation in a range of scenarios. VAGABOND uses a binomial probability distribution to start making adaptation decisions. This distribution is formed from the retention of video packets inside a certain time period. VAGABOND gets beyond firewall and proxy constraints by using ordinary TCP ports (like 80 or 443) to provide videoconferencing data via TCP. But even though TCP is a dependable transport protocol, it can occasionally have latency and socket timeout problems. VAGABOND has clever adaptation techniques to deal with these problems and ensure smooth data transfer. 2024 IEEE.
Adnan M.M.; Pant R.; Nirsanametla Y.; Fatima T.; Kumar M.; Kumar N.S.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1285-1289.
Adnan M.M., The Islamic University, Najaf, Iraq; Pant R., Uttaranchal University, Uttaranchal Institute of Management, Uttarakhand, India; Nirsanametla Y., Chitkara University, Centre of Research Impact and Outreach, Punjab, Rajpura, 140401, India; Fatima T., Cmr Technical Campus, Department of Cse, Telangana, Hyderabad, India; Kumar M., Christ University, School of Business and Management, Bangalore, India; Kumar N.S., Srm Institute of Science & Technology, Department of Computer Science & Engineering, Chennai, Vadapalani, 26, India
ThermAI: Exploring Temperature Analysis Through Diverse Machine Learning Models
Machine learning methods; Meteorological forecasting; Random forests; Temperature predictions
Meteorological forecasting is crucial in multiple industries, including agriculture, aviation, and daily routines. The objective of this inquiry is to improve temperature predictions by examining and comparing several machine learning methods, such as linear regression, decision trees, and random forests. This work aims to fill the gap in assessing machine learning models for temperature forecasting on a broader scale by utilising the comprehensive Indian meteorological dataset, which covers a wide range of geographical regions. The research utilises a thorough technique that includes gathering data, selecting relevant features, choosing appropriate models, and evaluating the results using R-squared and Mean Square Error metrics. The findings demonstrate that the Random Forest model surpasses both multiple linear regression and decision trees in terms of performance, displaying superior accuracy and reduced prediction errors. This study enhances proactive weather management and decision-making processes by offering valuable insights and tools to stakeholders in various industries. The work is organised into distinct sections that encompass a literature review, methodology, results, and conclusions, providing a comprehensive viewpoint on developments in temperature forecasting. 2024 IEEE.
https://doi.org/10.1109/ICECCC61767.2024.10593875 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200676343&doi=10.1109%2fICECCC61767.2024.10593875&partnerID=40&md5=f800e9b2b92ef9f1d9ee6fc790fc086a
Thomas D.R.B., CHRIST University, Department of computer science, Bangaluru, India; Joy H.K., CHRIST University, Department of computer science, Bangaluru, India