Cryptographic Protocols for Securing Internet of Things (IoT)
Authentication; Communications; Cryptographic; EverDeveloping; Gadgets; Protocols
Cryptographic protocols are used to relax the ever-developing quantity of linked gadgets that make up the net of things (IoT). Those cryptographic protocols have been designed to make certain that IoT tool traffic stays cozy and personal, even while nevertheless allowing tool-to-device and cloud-to-tool communications. Examples of these protocols consist of TLS/SSL, PGP/GPG, IPsec, SSL VPN, and AES encryption. Every one of these protocols enables authentication, message integrity, and confidentiality via encryption and key trade. Moreover, a lot of these protocols are carried out in the form of diverse hardware and software answers, such as smart playing cards and gateways, to make certain that IoT traffic is secured. With the appropriate implementation of those cryptographic protocols, establishments can ensure that their IoT facts are blanketed and securely transmitted. 2024 IEEE.
Manimekala B.; Aravind T.; Sounderrajan T.; Rajakumareswaran V.; Kondreddi K.; Mohanambal K.
2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
Institute of Electrical and Electronics Engineers Inc.
Manimekala B., Christ University, Department of Computer Science, Yeshwanthpur, Bengaluru, India; Aravind T., Vel TecRangarajanDr.Sagunthala R&d Institute of Science and Technology, Department of Cse, School of Computing, India; Sounderrajan T., Erode Sengunthar Engineering College, Department of Ai and Data Science, Tamil Nadu, Thuduppathi, India; Rajakumareswaran V., Erode Sengunthar Engineering College, Department of Computer Science and Design, Tamil Nadu, Thuduppathi, India; Kondreddi K., Chaitanya Bharathi Institute of Technology, Electrical and Electronics Engineering, Ranga Reddy, Telangana, Gandipet, 500075, India; Mohanambal K., Bannariamman Institute of Technology, Department of Computer Science and Engineering, Sathyamangalam, India
Congestion Avoidance in Vehicular Ad Hoc Network MAC Layer Using Harmony SearchModified Laying Chicken Algorithm (HS-MLCA)
Congestion avoidance; Harmony search; HS-MLCA; MAC; VANET
To address congestion in the MAC layer and enhance overall performance, the HS-MLCA is proposed. This algorithm incorporates the principles of both Harmony Search and Laying Chicken Algorithm to optimize resource allocation and congestion control. At the MAC layer, HS-MLCA offers several advantages over traditional congestion control schemes. Firstly, it leverages the Harmony Search algorithm, which is known for its ability to exploit the best outcomes in search processes. By exploring the solution space and exploiting promising regions, HS-MLCA optimizes resource allocation in the MAC layer. The integration of the Laying Chicken Algorithm (LCA) further enhances performance by improving convergence speed and solution accuracy. This hybrid approach leverages the strengths of both Harmony Search (HS) and LCA, resulting in more efficient and effective resource management. The Laying Chicken Algorithm simulates the behavior of laying hens in terms of resource allocation and competition. This approach contributes to provide the solution in quality and convergence speed, as the algorithm adapts to the dynamic nature of the MAC layer and the varying traffic conditions in VANETs. By combining the strengths of Harmony Search and Laying Chicken Algorithm, HS-MLCA offers improved performance in terms of congestion control in the MAC layer. It optimizes resource allocation, minimizes collisions and packet loss, reduces delay, and enhances overall network efficiency. These improvements ultimately lead to better quality of service, increased network capacity, and enhanced user experience in VANETs. It is worth noting that the specific performance improvements and benefits of HS-MLCA may vary depending on the implementation details, network conditions, and the specific VANET scenario. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-1030 LNNS, pp. 161-174.
Springer Science and Business Media Deutschland GmbH
Manimekala B., Department of Computer Science, Christ University, Bengaluru, India; Prabha S.P., Department of Computer Applications, Dayananda Sagar College of Arts, Science and Commerce, Bengaluru, India; Suresh S., Department of Computer Applications, Dayananda Sagar College of Arts, Science and Commerce, Bengaluru, India
Dysgraphia Disorder Detection and Classification Using Deep Learning Technique
Deep learning; Dysgraphia detection; Handwriting diagnosis; Rotating region CNN; Text-RPN
Dysgraphia, a neurological condition, impedes childrens acquisition of standard writing abilities, leading to subpar written expression. Inadequate or underdeveloped writing proficiency can adversely affect a childs educational progress and self-esteem. To address this issue, our study involved compiling a novel dataset of handwritten operations and extracting an array of features to encapsulate the various dimensions of handwriting characteristics. This research presents the Rotational Region Convolutional Neural Network (R2CNN) as a novel approach to tackle this issue. The R2CNN framework integrates a multitask refinement network for accurate tilted box detection and a text region proposal network (Text RPN) to identify potential text areas. To address the imbalance in the training categories and mitigate the overpopulation problem through feature elimination, a balance parameter is incorporated into the loss function. This research focused on identifying dysgraphia by analyzing these extracted features, which included both handwriting and geometric elements. The feature-learning stage of deep transfer learning effectively extracts and applies characteristics to identify dysgraphia. Research findings indicate that this study can use handwritten images to detect dysgraphia in children. The results of the data-gathering process show that this investigation can leverage samples of handwritten text to recognize dysgraphia among young individuals. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
Manimekala, B.; Umamaheswari, D.; Rozario, Juliet; Kannan, M.; Savitha, P. Margaret
SN Computer Science;Volume;6;Issue;3;Article No.;269;
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/s42979-025-03825-5 https://www.scopus.com/pages/publications/105000063828?origin=resultslist
Manimekala B., Department of Computer Science, School of Sciences, Christ University, Karnataka, Bangalore, India; Umamaheswari D., Department of Computer Science, School of Sciences, Christ University, Karnataka, Bangalore, India; Rozario J., Department of Computer Science, School of Sciences, Christ University, Karnataka, Bangalore, India; Kannan M., Department of Computer Science, School of Sciences, Christ University, Karnataka, Bangalore, India; Savitha P.M., Department of Computer Science, School of Sciences, Christ University, Karnataka, Bangalore, India
Prediction of football players performance using machine learning and deep learning algorithms
Deep Learning Algorithm; Machine Learning algorithm Linear Regression; Neural networks; Performance Prediction
In modern days the margin of error for football game is low, therefore the ultimate aim of the game is to win the match. The performance of the players in the match affects the results of the game. Due to this it is very important to evaluate the player and know his weakness. Manual evaluation tends to generate many errors and take more time. In the current research the statistical model is proposed to predict the stats of the football player based on previous session data by considering various aspects of the game. Through literature reviews it is observed that machine learning and deep learning algorithms can be used predict the performance of football player. But which model would be more efficient considering the positions of the player is not considered in any article. The proposed model has designed separate model as per the position of the player during the game. This can help to predict the player's performance as per their playing position. The current study has successfully implemented various machine learning and deep learning models and provide comparative analysis of the same. Each position has considered different variables associated with that position. The performance of these models is compared for further clarification 2021 IEEE.
2021 2nd International Conference for Emerging Technology, INCET 2021
Manish. S., CHRIST(Deemed to Be University), Department of Data Science, Lavasa, Pune, India; Bhagat V., CHRIST(Deemed to Be University), Department of Data Science, Lavasa, Pune, India; Pramila R.M., CHRIST(Deemed to Be University), Department of Data Science, Lavasa, Pune, India
Mapping Cyclone and Flood Hazard Vulnerability in Puri District, Odisha, India, Using Geoinformatics
Cyclone; Disaster; Floods; Remote sensing; Risk management; Vulnerability
India is vulnerable to many natural and human-made disasters due to its unique geo-climatic and socio-economic conditions. This paper focuses on natural disasters; such as cyclones and floods in the Puri district in the Indian state of Odisha. In this study, a number of floods and cyclones that occurred in the district were identified. The thematic maps of the influencing factors such as soil type, flood and cyclone vulnerability, elevation, and 2020 land cover were created using ArcGIS 10.3. Thereafter, the weighted overlay method was adopted based on analytical hierarchy process (AHP) to map the overall vulnerability of the district. The results derived from this study exhibited that the district is highly vulnerable to floods and cyclones. Finally, strategies were recommended for hazard risk reduction covering enhancing awareness towards hazards, improving early warning systems, establishing better communication between various stakeholders, and strengthening environmental protection and disaster risk reduction. Furthermore, measures for mitigation such as creating shelters, post-disaster rehabilitation, better and improved health facilities, incorporating green infrastructure at critical locations, relying on nature-based measures, execution of mangrove plantation along the coastal belt of the district, creating barriers or dykes to prevent water tides, and plummeting leachate due to improper waste disposal near the coast are suggested. The analysis and mapping of hazard vulnerability can act as a reference for urban planners and policymakers to promote Sustainable Development Goal (SDG) number 11 which is sustainable cities and communities. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Advances in 21st Century Human Settlements, Vol-Part F3155, pp. 595-603.
Manisha K., NIT Hamirpur, Himachal Pradesh, Hamirpur, 177005, India, School of Architecture, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Chettry V., Manipal School of Architecture & Planning, Manipal Academy of Higher Education, Manipal, India
Developmental Regulations to Conserve Catchment Area of an Urban Water Body: A Case of Upper Lake in the City of Bhopal, Madhya Pradesh, India
Catchment; Development regulations; Ecosystem services; Urban water body; Wetland
A lake reflects its catchment and provides a wide range of ecological services useful for the sustenance and betterment of human lives and other living organisms. The fast-paced developmental activities in the catchment area of lakes and over-consumption of available resources are resulting in the degradation of ecological services of the lake. The Upper Lake in Bhopal, a million-plus population city situated in the central part of India, has been selected for the study. The current regulatory regime for environmental conservation of the catchment area comprises a legislative framework and a set of regulatory institutions. Inadequacies in each accelerates environmental degradation of the Upper Lake, delays and escalates cost of developmental projects. This paper is an attempt to find out gaps in the existing institutional framework to conserve the catchment area. The present study focused on identifying and assessing major issues pertaining to the degradation of the Upper Lake, thereafter establishing a cause-and-effect relationship between anthropogenic activities and their environmental impacts. Subsequently, formulating developmental regulations forconserving theUpper Lake and its catchment area. Primarily, GIS application has been employed to delineate the catchment area of the Upper Lake by using catchment area assessment technique. Furthermore, a primary survey targeting the village stakeholders in the catchment area was conducted to analyze socio-economic profile and assess the agricultural practices adopted. Finally, formulated an institutional framework to conserve, govern and monitor the catchment area and the Upper Lake from the anthropogenic issues. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Advances in 21st Century Human Settlements, Vol-Part F3155, pp. 17-27.
Manisha K., Department of Architecture, National Institute of Technology, Hamirpur, Himachal Pradesh, Hamirpur, India, School of Architecture, Christ (Deemed to be University), Kengeri, Mysore Road, Karnataka, Bangalore, India; Pandey R.U., School of Planning and Architecture, Madhya Pradesh, Bhopal, India
Investigating and analyzing the causality amid tourism, environment, economy, energy consumption, and carbon emissions using TodaYamamoto approach for Himachal Pradesh, India
Applied autoregressive distributed lag (ARDL); Energy consumption; Environmental degradation; Environmental Kuznets curve (EKC); Tourism development
Himachal Pradesh is a preferred tourist destination with a Compound Annual Growth Rate (CAGR) of 10.76% between 201112 and 202021. The increasing trend of CAGR has boosted the tourism economy in the state while impacting the local environment. The negative impacts have recently increased due to changes in climatic patterns and increased tourism influx during the post-pandemic period. In this context, the present study analyzed the impact of tourism on the environment, economy, and energy consumption using the Environmental Kuznets Curve (EKC) hypothesis. The novelty of this study is to the existing literature on sustainable tourism development through investigating the interrelationship between tourism, environment, economy, energy consumption, and carbon emissions by employing the TodaYamamoto (TY) technique. This study will be a pioneering scientific investigation with quantitative results in the western Himalayan states of India, encompassing Jammu & Kashmir, Uttarakhand, and Himachal Pradesh. The annual data for each variable, such as per capita carbon emission (CEP), per capita Gross State Domestic Product (GSDP), per capita GSDP square, per capita energy consumption (ECP), and per capita tourism receipts (TRP), was collected from 2010 to 2021. This study exhibited an inverted-U EKC in the state, signifying the initial stage of economic development and extensive exploitation of natural resources for tourism. The TY results indicated an inter-causal relationship and feedback association among the variables in the study area. Thus, increased TRP would lead to an upsurge in energy consumption affecting the environmental quality due to increased carbon emissions. Such environmental degradation in the state would negatively impact the tourism sector in the long run. The research findings would guide planners and policymakers in promoting sustainable tourism. 2023, The Author(s), under exclusive licence to Springer Nature B.V.
Environment, Development and Sustainability
Springer Science and Business Media B.V.
Manisha K., Department of Architecture, National Institute of Technology, Himachal Pradesh, Hamirpur, 177005, India, School of Architecture, Christ (Deemed to be University), Kengeri, Mysore Road, Karnataka, Bangalore, 560074, India; Singh I., Department of Architecture, National Institute of Technology, Himachal Pradesh, Hamirpur, 177005, India; Chettry V., Department of Architecture, Planning & Design, Indian Institute of Technology (BHU) Varanasi, Uttar Pradesh, Varanasi, 221005, India
Himachal Pradesh is a preferred tourist destination with a Compound Annual Growth Rate (CAGR) of 10.76% between 201112 and 202021. The increasing trend of CAGR has boosted the tourism economy in the state while impacting the local environment. The negative impacts have recently increased due to changes in climatic patterns and increased tourism influx during the post-pandemic period. In this context, the present study analyzed the impact of tourism on the environment, economy, and energy consumption using the Environmental Kuznets Curve (EKC) hypothesis. The novelty of this study is to the existing literature on sustainable tourism development through investigating the interrelationship between tourism, environment, economy, energy consumption, and carbon emissions by employing the TodaYamamoto (TY) technique. This study will be a pioneering scientific investigation with quantitative results in the western Himalayan states of India, encompassing Jammu & Kashmir, Uttarakhand, and Himachal Pradesh. The annual data for each variable, such as per capita carbon emission (CEP), per capita Gross State Domestic Product (GSDP), per capita GSDP square, per capita energy consumption (ECP), and per capita tourism receipts (TRP), was collected from 2010 to 2021. This study exhibited an inverted-U EKC in the state, signifying the initial stage of economic development and extensive exploitation of natural resources for tourism. The TY results indicated an inter-causal relationship and feedback association among the variables in the study area. Thus, increased TRP would lead to an upsurge in energy consumption affecting the environmental quality due to increased carbon emissions. Such environmental degradation in the state would negatively impact the tourism sector in the long run. The research findings would guide planners and policymakers in promoting sustainable tourism. The Author(s), under exclusive licence to Springer Nature B.V. 2023.
Manisha, Keerti; Singh, Inderpal; Chettry, Vishal
Environment, Development and Sustainability;Volume;27;Issue;4;pp.8731-8766
https://doi.org/10.1007/s10668-023-04252-3 https://www.scopus.com/pages/publications/105001071512?origin=resultslist
Mental health professionals insights on developing and implementing a Mental Health Awareness and Destigmatisation program (MHAD) for adolescents
Adolescents; destigmatisation; mental health awareness; professionals
This study examined mental health professionals insights on developing and implementing a Mental Health Awareness and Destigmatisation (MHAD) program for adolescents aged 1418 years in Bangalore. Qualitative Interviews with 17 professionals revealed three main themes: 1. Awareness and Destigmatisation Programs: A Boon 2. Key Ingredients: Program Content and Delivery Style 3. Shaping Program Success: Key Drivers and Challenges. Professionals recommend interactive programs that promote open discussions and educate them on symptom recognition, online behavior, and healthy relationships. They identified key enablers and challenges, emphasizing the programs ability to empower adolescents, parents, and educators to create a supportive, stigma-free environment. 2024 Taylor & Francis Group, LLC.
Social Work in Mental Health, Vol-23, No. 2, pp. 165-192.
Manjari A.S., MPhil, Department of Psychology, School of Psychological Sciences, Christ (Deemed to be University), Bangalore, India; N.T S., Department of Psychology, School of Psychological Sciences, Christ (Deemed to be University), Bangalore, India
Knowledge, Attitude, and Stigma Among Adolescents: Effect of Mental Health Awareness and Destigmatisation (MHAD) Program
adolescence; destigmatisation; effect; mental health awareness
Background: Stigma against mental health problems is a common issue for adolescents aged 1418 years. However, comprehensive programs that simultaneously address awareness and stigma reduction tailored to the specific needs of this age group are lacking. Method: This study investigated the effectiveness of the Mental Health Awareness and Destigmatisation Program (MHAD) in reducing stigma and improving knowledge and attitudes towards peers with mental health problems. A quasi-experimental pre-post design was employed among adolescents aged 1418 years from an educational institution in Bangalore. After excluding those with high baseline mental health symptoms (PSC-17 > 20), a preassessment was conducted on adolescents' knowledge, attitude, and stigma (n = 52) using the Mental Health Knowledge Schedule, Self-structured Case Vignettes, and Peer Mental Health Stigmatization Scale. After completing the 6-week program, three participants were excluded from the post-assessment, as their attendance was less than 50%. A total of 49 (mean age = 16 years) adolescents were included in the post-assessment. Results: The paired sample t-test revealed significant improvements in all stigma scores. The Wilcoxon signed-rank test indicated a significant improvement in Recognition of Mental Illness scores. Conclusion: Findings showed that MHAD, an education-based program, was effective in reducing adolescents' stigma towards peers with mental health problems and improving their overall recognition of mental health symptoms. Research across larger adolescent populations is essential to enhance these interventions' long-term impact and sustainability. By closely monitoring and expanding research efforts, we can gain deeper insights into how these programs foster self-awareness, a crucial factor in recognizing mental health needs, challenging stigma, and promoting help-seeking behaviors among adolescents. 2024 Wiley Periodicals LLC.
Journal of Child and Adolescent Psychiatric Nursing, Vol-37, No. 4
https://doi.org/10.1111/jcap.70003 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210591089&doi=10.1111%2fjcap.70003&partnerID=40&md5=a05f7cc8343c6dcc061bde3f6d5de03b
Manjari A.S., Department of Psychology, School of Psychological Sciences, CHRIST (Deemed to be University), Bangalore, India; Sudhesh N.T., Department of Psychology, School of Psychological Sciences, CHRIST (Deemed to be University), Bangalore, India
Social Work in Mental Health;Volume;23;Issue;2;pp.165-192
https://doi.org/10.1080/15332985.2024.2406542 https://www.scopus.com/pages/publications/85210028217?origin=resultslist
Empowering Adolescents and Communities: Integrating Mental Health Awareness and Destigmatisation into Curriculum and Building a Foundation for Life
Adolescents; inclusive; mental health curriculum; romanticization of mental health
Adolescence, a critical period of growth, has seen an increase in mental health issues. Despite some successes in government initiatives, stigma continues to hinder help?seeking. To address this, programs should focus on anti?stigma efforts and emerging needs. A comprehensive mental health curriculum can help normalize discussions, reduce stigma, and encourage help?seeking. However, overcoming challenges such as time constraints, parental resistance, and a shortage of trained educators is essential. Organizing awareness events and sharing recovery stories can help combat stigma. Although social media has its drawbacks, it offers accessible and anonymous outreach. A concerning trend is the romanticization of mental health, which can trivialize real struggles and potentially lead adolescents to pretend mental illness for attention. A two?tiered solution is necessary to address this issue, involving certified educators, first?aid responders, and curriculum integration. This system allows professionals to focus on prevention and promotion while specialists handle diagnosis and treatment, ultimately fostering a long?term, inclusive, and mentally healthy society. 2025 Indian Journal of Social Psychiatry.
Indian Journal of Social Psychiatry;Volume;41;Issue;4;pp.439-444
https://doi.org/10.4103/ijsp.ijsp_175_24 https://www.scopus.com/pages/publications/105026265855?origin=resultslist
Manjari A.S., Department of Psychology, School of Humanities and Social Sciences, Kristu Jayanti (Deemed to be University), Karnataka, Bengaluru, India, Department of Psychology, School of Psychological Sciences, Christ (Deemed to be University), Karnataka, Bengaluru, India; Sudhesh N.T., Department of Psychology, School of Psychological Sciences, Christ (Deemed to be University), Karnataka, Bengaluru, India
Predicting Player Engagement in Online Gaming: A Machine Learning Approach
Behavior; Machine Learning models; Online Gaming; Player Engagement
The aim of this research is to make precise forecasts on player participation in online game using state-of-the-art machine learning algorithms. Player engagement plays a crucial element in determining the success of online games because it affects player retention, satisfaction and monetization. By understanding and predicting engagement levels, game developers and marketers can enhance the gaming experience and develop strategies to keep players invested. This research involves a comprehensive analysis of player behavior data from an online gaming platform. The dataset includes various demographic and behavioral features such as age, gender, location, game genre, playtime hours, in-game purchases, game difficulty, sessions per week, average session duration, player level, achievements unlocked, and engagement level. The data was preprocessed through handling missing values, normalizing numerical features, and encoding categorical variables. Exploratory Data Analysis (EDA) was conducted to understand the distribution and relationships between different features. Multiple machine learning models were evaluated to predict player engagement levels, including Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM). These models were then compared through the accuracy, precision, recall, and F1-score metrics. In the comparison, XGBoost emerged as the best model. Since it is the best-performing model, we can make the feature importance analysis to identify the best factors for predicting engagement in the next step. The XGBoost model achieved the highest accuracy of 91%, demonstrating superior precision, recall, and F1-scores across all engagement levels (High, Medium, Low). Ensemble methods like XGBoost, Gradient Boosting, and Random Forest outperformed the SVM model, highlighting their effectiveness in handling complex datasets. 2024 IEEE.
Manju Usha Sree T.; Sasikumar P.; Ayesha S.; Amzad Basha M.S.; Martha Sucharitha M.
Proceedings of NKCon 2024 - 3rd Edition of IEEE NKSS's Flagship International Conference: Digital Transformation: Unleashing the Power of Information
Manju Usha Sree T., Master of Business Administration Mallareddy Engineering College (Autonomous), Secunderabad, India; Sasikumar P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Ayesha S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Amzad Basha M.S., Gitam (Deemed to Be University), Gitam School of Business, Bengaluru, India; Martha Sucharitha M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India
Attention-Enhanced Vision Transformer Model for Precise Skin Cancer Detection
Attention Mechanism; Contrast Enhancement (CLAHE); Dermoscopic Image Analysis; Feature Fusion; Lesion Segmentation; Skin Cancer Detection; Vision Transformer (ViT)
Skin cancer is one of the most prevalent and potentially fatal diseases, requiring early and accurate detection for effective treatment. Recent advances in deep learning have significantly improved automated skin lesion classification, but traditional Convolutional Neural Networks (CNNs) struggle with capturing long-range dependencies in dermoscopic images. To address this limitation, we propose a Preprocessing-Optimized Vision Transformer (ViT) Model that enhances lesion detection using attention-based feature fusion. Our methodology includes contrast enhancement (CLAHE), hair removal (DullRazor), lesion segmentation (K-Means + Otsus Thresholding), and data augmentation, ensuring robust model training. The proposed Attention-Enhanced ViT Model effectively learns global contextual features from dermoscopic images through self-attention mechanisms. The proposed model is evaluated our model on the ISIC Skin Cancer Dataset, achieving an accuracy of 94.6%, precision of 92.8%, recall of 93.5%, and an AUC-ROC score of 0.97, outperforming traditional CNN-based models such as ResNet50 (92.1% accuracy) and EfficientNet-B0 (93.3% accuracy). Our results demonstrate that integrating preprocessing techniques with Vision Transformers significantly enhances classification performance, making this approach a viable solution for real-world computer-aided dermatology. 2025 IEEE.
Manju, V.N.; Dayana, D.S.; Patwari, Neha; Bindu Madavi, K.P.; Krishna Sowjanya, K.
Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025;
https://doi.org/10.1109/ETCC65847.2025.11108453 https://www.scopus.com/pages/publications/105015616471?origin=resultslist
Manju V.N., Dept. of Computer Science and Engineering, CMR Institute of Technology, Bengaluru, India; Dayana D.S., Dept. of Networking and Communications, SRM Institute of Science and Technology, Chengalpet District, Kattankulathur, India; Patwari N., Dept. of Information Technonology, Thakur College of Engineering & Tech., Mumbai, India; Bindu Madavi K.P., Dept. of Computer Science and Engineering, Christ University, Bengaluru, India; Krishna Sowjanya K., Dept. of Computer Science and Engineering, CMR Institute of Technology, Bengaluru, India
IoT-Based Smart Indoor Navigation System with Voice Assistance for Museums
Bluetooth low energy; Internet of Things; Navigation system; Smart indoor systems
In the current era of smart heterogenous devices, the surrounding environment too needs to be smarter to match the gravity of such devices. Such advanced environment can be built with the technology called Internet of Things (IoT). Due to the presence of such vivid thing devices in the Internet of Things (IoT) environment, the task of automatically predicting the end users desires can play an important role when it comes to match the pace of modern society with too much diverse aspects. Since last decade, people have deviated their attention towards Indian ancient culture and Museums are eye catching attraction where our ancient cultural heritage exist. To improvise the slow pace growth of the tourism sector, there is the crucial requirement of technological improvement especially due to the restrictions on installations of external hardware within the close proximity. One prominent way of improving tourists experience at museums is to renovate existing museums with IoT-based smart devices which is programmed such a way to automatically navigate the user indoor and briefs the associated information about artwork without any user intervention. In this paper, we propose an IoT-based smart indoor navigation system along with voice assistance which can enhance the tourists experience in a museum. In addition, the proposed design also delivers the very personalized cultural contents related to the visited artworks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-967 LNNS, pp. 449-457.
Manju, Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, India; Singh S., Department of Computer Science and Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, 560074, India
Environmental Applications of Green Engineered Copper Nanoparticles
Anticancer; Bioremediation; Crop management; Cu nanoparticles; Dye degradation; Green synthesis
Naturally engineered nanomaterials in recent times have myriad potential in different fields. Moreover, green derived nanoparticles (NPs) encourage broader implementation for wider applications. Amongst many metals, copper and its oxide-based nanoparticles (CuONPs) have increased utmost consideration owing to its specific characteristics, abundance, and cost-effectiveness. Major setback of chem-ical and physical methods of synthesising CuONPs involves high cost along with environmental hazards. Aforementioned challenge compelled researchers to explore green synthesised CuONPs that is much cheaper, efficient, economically beneficial, non-toxic, and eco-friendly. Existing plant-based CuONPs have potential efficiency to enhance the toxic effects against the plant pathogens and combating environmental pollution through bioremediation. Several extracts of plant derivatives have been used for the synthesis of CuONPs such as Azadirachta indica, Hibiscus rosa-sinensis, Murraya koenigii, Moringa oleifera, Tamarindus indica, Eclipta prostrate, Olea europaea, etc. Microbes as cell factories are more efficiently used as NPs compared to larger plants such as, green algae Botryococcus braunii, brown algae Macrocystis pyrifera, Bifurcaria bifurcate etc. Bio-based CuONPs have been applied in numerous fields such as pharmaceutical, molecular biology, bioremediation, cosmetics, textiles etc. Several of them also employed in dye degradation, water treatment, food preser-vation, Photovoltaic devices, solar energy conversions, and field emission emitters. However, as in clinical setup due to their efficacy these are exclusively used as anti-cancer, antimicrobial agents. Further, their high antioxidant potential renders them as an invaluable tool for biomedical devices. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.
Manjula N.G.; Sarma G.; Shilpa B.M.; Suresh Kumar K.
Manjula N.G., School of Basic and Applied Sciences, Dayananda Sagar University, Kumaraswamy Layout, Karnataka, Bengaluru, 560111, India; Sarma G., School of Basic and Applied Sciences, Dayananda Sagar University, Kumaraswamy Layout, Karnataka, Bengaluru, 560111, India; Shilpa B.M., School of Basic and Applied Sciences, Dayananda Sagar University, Kumaraswamy Layout, Karnataka, Bengaluru, 560111, India, Christ-Deemed to be University, Bangalore Kengeri Campus, Kanmanike, Mysore Road, Karnataka, Kumbalgodu, Bangalore, 560074, India; Suresh Kumar K., Department of Chemistry, BMS Institute of Technology and Management, Doddaballapur Main Road, Avalahalli, Yelahanka, Karnataka, Bengaluru, 560064, India
Fungal-based synthesis to generate nanoparticles for nanobioremediation
Biogenic fungi; Bioremediation; Eco-friendly; Nanoparticles; Pollutants
Nanotechnology has gained immense popularity with its innumerable biological agents, which are replacing toxic chemicals with an advanced technique for reducing and stabilizing nanoparticles (NPs). Fungal nanotechnology has represented exceptional technique in this area, owing to its nontoxicity, eco-friendly nature for fungal NPs, and nanostructure synthesis by reducing enzymes using either intracellular or extracellular techniques. Further, ease lies in the scale up-and downstream process owing to the increased surface area of the mycelial cells. Fungi and yeast are highly potential secretors of extracellular enzyme, grow fast, and are simple to maintain. Biogenic fungal NPs have been applied in the field of industry, agriculture, medicine, and other sectors too, and are used as bioremediators, drug delivery, biosensors, MRI, medical imaging, cancer therapy, etc. Mycoremediation can serve as a facilitator in bioremediating the toxins by immobilizing or inducing the synthesis of enzymes. Fungal NPs have shown an effective and efficient clean-up of the environment from the chemical pollutants and heavy metals, reducing total time consumption and total cost reduction. Fungal species of A. flavus and T. harzianum have shown promising crude oil degrading abilities with silver NPs at a very low concentration. Other fungal species used as resources for metal NPs that have been useful as bioremediators include Aspergillus, Fusarium, Penicillium, and Verticillium. The Author(s), 2023. All rights reserved.
Manjula N.G.; Tajunnisa; Mamani V.; Meghana C.A.; Mayegowda S.B.
Green Nanoremediation: Sustainable Management of Environmental Pollution, pp. 83-108.
Manjula N.G., Department of Microbiology, School of Basic and Applied Sciences (SBAS), Dayananda Sagar University, Bengaluru, Karnataka, India; Tajunnisa, Department of Microbiology, School of Basic and Applied Sciences (SBAS), Dayananda Sagar University, Bengaluru, Karnataka, India; Mamani V., Department of Microbiology, School of Basic and Applied Sciences (SBAS), Dayananda Sagar University, Bengaluru, Karnataka, India; Meghana C.A., Department of Microbiology, School of Basic and Applied Sciences (SBAS), Dayananda Sagar University, Bengaluru, Karnataka, India; Mayegowda S.B., Department of Psychology, CHRIST Deemed to be University, Kengeri Campus, Bengaluru, Karnataka, India
A Gated Recurrent Unit Based Continual Normalization Model for Arrythmia Classification Using ECG Signals
Cardiac arrhythmias; Continual normalization; Electrocardiogram; Gated Recurrent Unit; MIT-BIH
In this world, around 31% of the deaths are commonly caused because of cardiovascular diseases. Around 80% of sudden deaths occur due to cardiac arrhythmias and heart diseases. The mortality rate has increased for cardiac disease and therefore early heart disease detection is significant to preclude patients from dying. At the initial phase, the heart disease is detected by analyzing abnormal heartbeats. The existing models failed to select the features before performing the extraction of features. The developed model examined MIT-BIB database to surpass the overfitting issue. Therefore, in the present research work, the Gated Recurrent Unit (GRU) based Continual Normalization (CN) classifier is used to speed up the training to a higher learning rate to enable simpler learning for the standard deviation of the neurons' output. The extracted features were used to classify Electrocardiogram (ECG) signals into 5 important classes named as N, S, V, F & Q which denote the kinds of arrhythmia. The findings revealed that the proposed GRU based Continual Normalization technique obtained an accuracy of 99.41% which is better when compared with the existing researches. 2023 IEEE.
Manjunath C.; Hemalatha K.L.; Sandya V.; Sunil G.; Shakir A.M.
IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
Manjunath C., CHRIST(Deemed to Be University), Department of Cse, Bengaluru, India; Hemalatha K.L., Sri Krishna Institute of Technology, Department of Information Science and Engineering, Bengaluru, India; Sandya V., Cmr Technical Campus, Department of CSE(Data Science), Hyderabad, India; Sunil G., School of Computer Science & Artificial Intelligence, Sr University, Warangal, India; Shakir A.M., The Islamic University, Najaf, Iraq
Stock market prediction employing ensemble methods: the Nifty50 index
Bagging; Boosting; Fundamental analysis; Machine learning; National stock exchange fifty; Stacking; Stock market forecasting
Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions. 2024, Institute of Advanced Engineering and Science. All rights reserved.
IAES International Journal of Artificial Intelligence, Vol-13, No. 2, pp. 2047-2057.
Institute of Advanced Engineering and Science
All Open Access; Hybrid Gold Open Access
https://doi.org/10.11591/ijai.v13.i2.pp2049-2059 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192996983&doi=10.11591%2fijai.v13.i2.pp2049-2059&partnerID=40&md5=defcf1993f4e9872d40f39c2da5ffd84
Manjunath C., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India; Marimuthu B., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India; Ghosh B., Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Bengaluru, India
Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance
National stock exchange fifty; Principle component analysis; Stock market; Technical indicators; Time series forecast
Predicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF). In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifier (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968). In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1. 2023 Institute of Advanced Engineering and Science. All rights reserved.
International Journal of Electrical and Computer Engineering, Vol-13, No. 3, pp. 3549-3560.
https://doi.org/10.11591/ijece.v13i3.pp3549-3560 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149106688&doi=10.11591%2fijece.v13i3.pp3549-3560&partnerID=40&md5=cf8a5be4173b9508721b5ea5f7f6664f
Manjunath C., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India; Marimuthu B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India; Ghosh B., Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Bengaluru, India