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9. List of contributors

Author

Ahmed, Shakeel, primary, Ali, Akbar, additional, Ali, S. Wazed, additional, Bairagi, Satyaranjan, additional, Bharti, Kanchan, additional, Bhattacharyya, Debarati, additional, Bhosale, Rohit, additional, Can, Özlem Pelin, additional, Chopra, Hitesh, additional, Dar, Umar Ali, additional, Dedebas, Tugba, additional, Dubey, Satish, additional, Gadkari, Pooja, additional, Göksel Saraç, Meryem, additional, Gowda, Devegowda, additional, Guzel Kaya, Gulcihan, additional, Halagali, Praveen, additional, Jha, Abhishek, additional, Kapoor, Ramit, additional, Kazi, Heena, additional, Kiran Raj, G., additional, Kumar, Charchit, additional, Kumar, Manish, additional, Kumar, Shashikant, additional, Kumar, Vaneet, additional, Kumar, Vijay, additional, Maiti, Sabyasachi, additional, Mehta, Arundhati, additional, Mishra, Brahmeshwar, additional, Mittal, Pooja, additional, Mulvihill, Daniel M., additional, Nath, Jyotendra, additional, Osmani, Riyaz Ali, additional, Pokale, Rahul, additional, Rana, Vikrant Singh, additional, Rong, Liyuan, additional, Saleem, Shabnum, additional, Sehgal, Rakesh, additional, Sehgal, Rishabh, additional, Sharma, Kashma, additional, Sharma, Nisha, additional, Sharma, Vishal, additional, Shen, Mingyue, additional, Soman, Reshma, additional, Tantary, Aafaq Ahmad, additional, Tekchandani, Pawan, additional, Wani, Shahid Ud Din, additional, Xie, Jianhua, additional, and Yadav, Harsh, additional

Published
2024
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10. Machine Learning-Based Prediction of Parking Space Availability in IoT-Enabled Smart Parking Management Systems

Author

Dahiya, Anchal, Mittal, Pooja, Sharma, Yogesh Kumar, Lilhore, Umesh Kumar, Simaiya, Sarita, Ghith, Ehab, and Tlija, Mehdi

Subjects
Company business management, Traffic congestion, Machine learning
Abstract

Parking space management has become a critical challenge in urban areas due to increasing vehicle numbers and limited parking infrastructure. This paper presents a comprehensive study of machine learning (ML) models in IoT-enabled environments focusing on proposing an ML-based model for predicting available parking space. The study evaluates the performance of various models including K-nearest neighbors (KNNs), support vector machines (SVMs), random forest (RF), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) based on “precision, recall, accuracy, and F1-score performance metrics”. The results obtained by implementing ML models on the data with 65% and 85% threshold values are compared to draw meaningful conclusions regarding their performance in predicting parking space availability. Among the evaluated models, random forest (RF) demonstrates superior performance with high precision, recall, accuracy, and F1-score values. It showcases its effectiveness in accurately predicting parking space availability in the IoT-enabled environment. On the other hand, models such as K-nearest neighbors (KNNs), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) show relatively lower performance in complex parking scenarios. The paper concludes that the use of advanced predictive models, particularly random forest, significantly enhances the accuracy and reliability of IoT-enabled parking management systems and also reduces the waiting time of the vehicles, leading to more efficient resource utilization, reduced traffic congestion in real-time scenarios, and better user satisfaction in the IoT-enabled environment., Author(s): Anchal Dahiya [1]; Pooja Mittal [1]; Yogesh Kumar Sharma [2]; Umesh Kumar Lilhore (corresponding author) [3,4]; Sarita Simaiya [4]; Ehab Ghith [5]; Mehdi Tlija [6] 1. Introduction As more [...]

Published
2024
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27. A glimpse

Author

Mittal, Pooja

Published
1998

32. Contributors

Author

Alexiou, Athanasios, primary, Bhardwaj, Sakshi, additional, Bibi, Shabana, additional, Borkar, Maheshkumar, additional, Chopra, Bhawna, additional, Chopra, Hitesh, additional, Corriero, Anna Chiara, additional, Dhingra, Ashwani K., additional, Gautam, Rupesh Kumar, additional, Goyal, Rajat, additional, Grewal, Ajmer Singh, additional, Guarve, Kumar, additional, Gupta, Gaurav, additional, Hasan, Mohammad Mehedi, additional, Hossain, Md. Shahadat, additional, Htar, Thet Thet, additional, Islam, Fahadul, additional, Joon, Shikha, additional, Kamal, Mohammad Amjad, additional, Kanugo, Abhishek, additional, Kapoor, Ramit, additional, Kaur, Gurjeet, additional, Kaur, Komal Preet, additional, Khan, Ashraf Ullah, additional, Khan, Muhammad Saad, additional, Khan, Shafi Ullah, additional, Kumaravel, J., additional, Medhi, Bikash, additional, Mishra, Anurag, additional, Mittal, Pooja, additional, Moysidis, Dimitrios V., additional, Nandave, Mukesh, additional, Papazoglou, Andreas S., additional, Parmar, Ketan C., additional, Pathak, Sachchidanand, additional, Prabhu, Arati, additional, Prakash, Ajay, additional, Raizaday, Abhay, additional, Rawat, Sarita, additional, Rayan, Rehab A., additional, Sharma, Kalicharan, additional, Sharma, Priyanka, additional, Sharma, Saurabh, additional, Shen, Bairong, additional, Singh, Santosh Kumar, additional, Singla, Rajeev K., additional, Sonawane, Ganesh, additional, Sonawane, Kajal, additional, Tjoa, Enty, additional, Tsagkaris, Christos, additional, and Yousafi, Qudsia, additional

Published
2023
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35. Clinical and molecular characterization of FAP and SPP1 in colorectal cancer (CRC), CALGB (Alliance)/SWOG 80405 and real-world data.

Author

Ashouri, Karam, Millstein, Joshua, Yang, Yan, Xiu, Joanne, Soni, Shivani, Jayachandran, Priya, Mittal, Pooja, Algaze, Sandra, Torres-Gonzalez, Lesly, Shah, Unnati Hermant, Shields, Anthony F., Goldberg, Richard M, Lou, Emil, Weinberg, Benjamin Adam, Marshall, John, Mumenthaler, Shannon M., Venook, Alan P., Finley, Stacey D, Battaglin, Francesca, and Lenz, Heinz-Josef

Published
2025
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37. Impact of expression of metabolic genes on patient (pts) outcomes in metastatic colorectal cancer (mCRC): Data from CALGB/SWOG 80405 (Alliance).

Author

Battaglin, Francesca, Millstein, Joshua, Yang, Yan, Ashouri, Karam, Soni, Shivani, Algaze, Sandra, Mittal, Pooja, Torres-Gonzalez, Lesly, Jayachandran, Priya, Shah, Unnati Hermant, Zhang, Wu, Hochster, Howard S., Ou, Fang-Shu, Venook, Alan P., Innocenti, Federico, Finley, Stacey D, and Lenz, Heinz-Josef

Published
2025
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39. Gait-based Parkinson's disease diagnosis and severity classification using force sensors and machine learning.

Author

Navita, Mittal, Pooja, Sharma, Yogesh Kumar, Rai, Anjani Kumar, Simaiya, Sarita, Lilhore, Umesh Kumar, and Kumar, Vimal

Subjects
*PARKINSON'S disease, *STANDARD deviations, *GROUND reaction forces (Biomechanics), *EARLY diagnosis, *RANDOM forest algorithms, *GAIT in humans
Abstract

A dual-stage model for classifying Parkinson's disease severity, through a detailed analysis of Gait signals using force sensors and machine learning approaches, is proposed in this study. Parkinson's disease is the primary neurodegenerative disorder that results in a gradual reduction in motor function. Early detection and monitoring of the disease progression is highly challenging due to the gradual progression of symptoms and the inadequacy of conventional methods in identifying subtle changes in mobility. The proposed dual-stage model utilized a hypertuned Random Forest Tree (RFT) to classify the subjects into PD and non-PD classes at Stage 1 and a hypertuned Ensemble Regressor (ER) to predict the severity of illness at Stage 2. Further, we have implemented the proposed model on the data signals gathered from both feet of 166 participants using Vertical Ground Reaction Force Sensors (VGRF). The dataset comprised 93 persons with Parkinson's disease and 73 healthy controls. The dataset (imbalance) collected from both feet is passed to the preprocessing phase (for balancing data using the SMOTE method), followed by the feature extraction phase to extract features related to time, frequency, spatial, and temporal features domains that are highly effective for detecting and assigning severity levels of PD. A Recursive Feature Elimination method is also used to select the optimal set of features to improve the model performance. It is acknowledged that the early detection of Parkinson's disease is contingent upon critical parameters, including stride length, stance duration, swing interval, double limb support, step time, and step length. The crucial evaluation metrics used for evaluating model performance include accuracy, mean absolute error, and root mean square error. The findings indicate that the suggested model significantly surpasses current methodologies. It attained an accuracy of 97.5 ± 2.1%, Sensitivity of 97% ± 2.5%, and average Specificity of 95% ± 2.2% in differentiating between PD and non-PD participants utilizing RFT and evaluated disease severity with an average accuracy of 96.4 ± 2.3%, an average mean absolute error of 0.065 ± 0.024, and a root mean square error of 0.080 ± 0.06. The results indicate that the proposed dual-stage model is exceptionally successful in the early detection and severity assessment of Parkinson's disease and demonstrates better efficacy than alternative models. [ABSTRACT FROM AUTHOR]

Published
2025
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40. Hybrid parking space prediction model: integrating ARIMA, Long short-term memory (LSTM), and backpropagation neural network (BPNN) for smart city development.

Author

Dahiya, Anchal, Mittal, Pooja, Sharma, Yogesh Kumar, Lilhore, Umesh Kumar, Simaiya, Sarita, Haq, Mohd Anul, Aleisa, Mohammed A., and Alenizi, Abdullah

Subjects
LONG short-term memory, SMART parking systems, MEAN square algorithms, BOX-Jenkins forecasting, STANDARD deviations
Abstract

Parking space prediction is a significant aspect of smart cities. It is essential for addressing traffic congestion challenges and low parking availability in urban areas. The present research mainly focuses on proposing a novel scalable hybrid model for accurately predicting parking space. The proposed model works in two phases: in first phase, auto-regressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are integrated. Further, in second phase, backpropagation neural network (BPNN) is used to improve the accuracy of parking space prediction by reducing number of errors. The model utilizes the ARIMA model for handling linear values and the LSTM model for targeting non-linear values of the dataset. The Melbourne Internet of Things (IoT) based dataset, is used for implementing the proposed hybrid model. It consists of the data collected from the sensors that are employed in smart parking areas of the city. Before analysis, data was pre-processed to remove noise from the dataset and real time information collected from different sensors to predict the results accurately. The proposed hybrid model achieves the minimum mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) values of 0.32, 0.48, and 0.56, respectively. Further, to verify the generalizability of the proposed hybrid model, it is also implemented on the Harvard IoT-based dataset. It achieves the minimum MSE, MAE, and RMSE values of 0.31, 0.47, and 0.56, respectively. Therefore, the proposed hybrid model outperforms both datasets by achieving minimum error, even when compared with the performance of other existing models. The proposed hybrid model can potentially improve parking space prediction, contributing to sustainable and economical smart cities and enhancing the quality of life for citizens. [ABSTRACT FROM AUTHOR]

Published
2025
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46. Contributors

Author

Abdullaev, Bekhzod, primary, Afzal, Mohammad, additional, Ahmad, Faizan, additional, Ahmad, Javed, additional, Alam, Manzar, additional, Ali, Sabeeha, additional, Annadurai, Narendran, additional, Anwar, Saleha, additional, Ara, Shabnam Anjum, additional, Badar, Sufian, additional, Bagheri, Soghra, additional, Banerjee, Subhamoy, additional, Bhat, Eijaz Ahmed, additional, Bhatt, Hardik, additional, Bisht, Deepa, additional, Dan, Siddhartha, additional, Das, Debasis, additional, Das, Viswanath, additional, Dhankhar, Poonam, additional, Dube, Divya, additional, Gupta, Parth, additional, Gupta, Varsha, additional, Hassan, Md. Imtaiyaz, additional, Hekmat, Azadeh, additional, Hyderi, Zeeshan, additional, Islam, Asimul, additional, James, Michael N.G., additional, Jha, Hem Chandra, additional, Jit, Bimal Prasad, additional, Kashyap, Dharmendra, additional, Kaur, Lajpreet, additional, Khan, Md Khurshid Alam, additional, Khan, Mohammad Ahmed, additional, Kumar, Dhiraj, additional, Kumar, Pravindra, additional, Kumar, Vinod, additional, Kumari, Varsha, additional, Mishra, Parul, additional, Misra, Ashish, additional, Mittal, Disha, additional, Mittal, Pooja, additional, Mohammad, Taj, additional, Musvi, Syed Shahid, additional, Nagar, Garima, additional, Naqi, Asma, additional, Nayak, Chirasmita, additional, Niveria, Karishma, additional, Noor, Saba, additional, Ojha, Himanshu, additional, Padhy, Amrita Arpita, additional, Pathak, Mallika, additional, Pathak, Upasana, additional, Payoe, Roshani, additional, Rai, Sandhya, additional, Rani, Annu, additional, Rankawat, Sourbh, additional, Raut, Rajnikant, additional, Raza, Ali, additional, Rustagi, Vanshika, additional, Saboury, Ali Akbar, additional, Sachan, Manisha, additional, Sahoo, Subhashree, additional, Saini, Taruna, additional, Sajjad, Nasreena, additional, Shakeel, Ilma, additional, Shamsi, Anas, additional, Shamsi, Tooba Naz, additional, Sharma, Saroj, additional, Shaw, Suman, additional, Shivani, Kummari, additional, Singh, Archana, additional, Singh, Indrakant Kumar, additional, Singh, Sanjeev Kumar, additional, Singh, Vishakha, additional, Singhal, Rahul, additional, Srivastava, Ankit, additional, Srivastava, Sameer, additional, Tiwari, Deeksha, additional, Tiwari, Vishvanath, additional, Varshney, Nidhi, additional, Verma, Anita Kamra, additional, Vishwakarma, Keerti, additional, and Zaidi, Yeshfa, additional

Published
2022
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49. List of contributors

Author

Aftab, Bushra, primary, Aggarwal, Geeta, additional, Ajmal, Gufran, additional, Alex, Thomson Santosh, additional, Alexander, Amit, additional, Arora, Vimal, additional, Arya, K.R., additional, Babu, R. Jayachandra, additional, Bairagi, Ujjawal, additional, Balamuralidhara, V., additional, Baldi, Ashish, additional, Basetty, Vindhya, additional, Bhatt, Shailendra, additional, Bhatti, Rajbir, additional, Chakraborty, Souvik, additional, Chaturvedi, Sushma, additional, Chandran, C. Sarath, additional, Chaudhary, Sagar, additional, Chauhan, Sonal, additional, Chellappan, Dinesh Kumar, additional, Deruiter, Jack, additional, Dhanasekaran, Muralikrishnan, additional, Dhiman, Anju, additional, Dua, Kamal, additional, Dureja, Harish, additional, Farooq, Uzma, additional, Gautam, Rupesh K., additional, Gulati, Monica, additional, Gulati, Nisha, additional, Gupta, Gaurav, additional, Gupta, N. Vishal, additional, Gupta, Rishabh, additional, Halder, Nabamita, additional, Harrell, Erin, additional, Hassan, Nazia, additional, Iqbal, Zeenat, additional, Jain, Pooja, additional, Jha, Niraj Kumar, additional, Kabra, Atul, additional, Kanojia, Neha, additional, Kapoor, Devesh U., additional, Kapoor, Ramit, additional, Kappally, Shijina, additional, Khursheed, Rubiya, additional, Kumar, Ashutosh, additional, Kumar, Deepak, additional, Kumar, Nitesh, additional, Kumar, Vijay, additional, Lawson, Joseph, additional, Mahajan, Dhriti, additional, Maurya, Pawan Kumar, additional, Mehra, Akansha, additional, Mirza, Mohd. Aamir, additional, Mishra, Brahmeshwar, additional, Mishra, Monalisa, additional, Mittal, Pooja, additional, Mohapatra, Sradhanjali, additional, Naman, Subh, additional, Naryal, Srishti, additional, Neethimohan, Nishitha, additional, Paliwal, Shivani Rai, additional, Palliwal, Rishi, additional, Parise, Rachel, additional, Pathak, Kamla, additional, Pathak, Suhrud, additional, Pondugula, Satyanarayana, additional, Prabitha Priyadharshini, A., additional, Prabu, S. Lakshmana, additional, Prasher, Parteek, additional, Priya, Sakshi, additional, Puratchikody, A., additional, Puri, Ritika, additional, Raizaday, Abhay, additional, Raj, Alan, additional, Rawat, Sushama, additional, Sajayan, Krishnameera, additional, Sapra, Bharti, additional, Sara Josen, Teenu, additional, Sastri, K. Trideva, additional, Sharadha, M., additional, Sharma, Jai Bharti, additional, Sharma, Ram Babu, additional, Sharma, Vijay, additional, Shelke, Triveni, additional, Singh, Lovedeep, additional, Singh, Mahaveer, additional, Singh, Manvi, additional, Singh, Sachin Kumar, additional, Singh, Santosh Kumar, additional, Singla, Chhavi, additional, Singla, Rajeev K., additional, Srivastava, Nimisha, additional, Swathy, K.K., additional, Talegaonkar, Sushma, additional, Thapa, Komal, additional, Tiwary, Ashok Kumar, additional, Tyagi, Anchal, additional, Umamaheswari, A., additional, Upadhyay, Mansi, additional, Vats, Vishal, additional, Verma, Nitin, additional, Verma, Priyanka, additional, Vijayalakshmi, M., additional, Vishwas, Sukriti, additional, Vyas, Govind, additional, and Zakir, Foziyah, additional

Published
2022
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50. List of contributors

Author

Adassooriya, Nadeesh M., primary, Azcune, Germán, additional, Banerjee, Priyabrata, additional, Barrantes, Kenia, additional, Bej, Sourav, additional, Bhattacharyya, Liza Handique, additional, Bhattacharyya, Pranaba N., additional, Borah, Siddhartha Narayan, additional, Campana, Eloiza H., additional, Chacón, Luz, additional, Chande, Othman, additional, Cid, Ángeles, additional, Coelho, Susana, additional, Colazzo, Marcos, additional, Dhir, Bhupinder, additional, Esperanza, Marta, additional, Eunice, Mureithi, additional, García-Córcoles, María Teresa, additional, Ghahari, Saeid, additional, Ghahari, Sajjad, additional, Ghahari, Somayeh, additional, Gómez-Regalado, María del Carmen, additional, Grobelak, Anna, additional, Gündoğdu, Sedat, additional, Harimawan, Ardiyan, additional, Hazra, Abhijit, additional, Herrero, Concepción, additional, Hossein, Miraji, additional, Indarto, Antonius, additional, Islam, Nazim Forid, additional, Karwal, Preeti, additional, Khan, Farhan R., additional, Koch, Niharika, additional, Kowalska, Aneta, additional, Madanayake, Nadun H., additional, Mançano, Stella M.C.N., additional, Markley, Laura A., additional, Martín-Bueno, Julia, additional, Martín-Pozo, Laura, additional, Mihai, Florin-Constantin, additional, Mittal, Pooja, additional, Mondal, Amita, additional, Mondal, Udayan, additional, Moscoso-Ruiz, Inmaculada, additional, Moura, Leonardo, additional, Muela, Agustina, additional, Nag, Somrita, additional, Nagar, Garima, additional, Nematzadeh, Ghorban Ali, additional, Ngassapa, Faustin, additional, Olivelli, Arianna, additional, Pareja, Lucía, additional, Pastorinho, M. Ramiro, additional, Pathak, Pankaj, additional, Paul, Suparna, additional, Perera, Niranjalie, additional, Pérez-Parada, Andrés, additional, Picão, Renata C., additional, Prasad, Ram, additional, Reyes, Liliana, additional, Rioboo, Carmen, additional, Rivera-Montero, Luis, additional, Saikia, Rashmi Rekha, additional, Sarma, Hemen, additional, Sen, Suparna, additional, Seoane, Marta, additional, Setiadi, Tjandra, additional, Sharma, Poonam, additional, Singh, Archana, additional, Singh, Indrakant K., additional, Sonowal, Songita, additional, Sousa, Ana Catarina A., additional, Sumampouw, Giovanni A., additional, Sungur, Şana, additional, Sushil, Kumar, additional, Tripathi, Charu, additional, Valente, João Vasco, additional, van Emmerik, Tim, additional, and Zafra-Gómez, Alberto, additional

Published
2022
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