iosr-JCE   Volume-26 ~ Issue-6 ~ Nov. - Dec. 2024

Abstract: In today's music streaming landscape, personalized song recommendations are crucial for enhancing user experiences. This research study combines heart rate monitoring and facial expression detection to present a novel deep learning method for song suggestion. With a 92% accuracy rate, a Convolutional Neural Network (CNN) model is used to identify emotional states from facial expressions, and heart rate information adds more context to the user's emotional intensity. The large music library is matched to these emotional cues, guaranteeing that suggested songs.....

Keywords — User experience, Deep learning, Song recommendation, Convolutional Neural Network, heart rate analysis facial expressions..

[1] Y. Kodama Et Al., "A Music Recommendation System," 2005 Digest Of Technical Papers. International Conference On Consumer Electronics, 2005. Icce., Las Vegas, Nv, 2005, Pp. 219-220, Doi: 10.1109/Icce.2005.1429796.
[2] V. P. Sharma, A. S. Gaded, D. Chaudhary, S. Kumar And S. Sharma, "Emotion-Based Music Recommendation System," 2021 9th International Conference On Reliability, Infocom Technologies And Optimization (Trends And Future Directions) (Icrito), Noida, India, 2021, Pp. 1-5, Doi: 10.1109/Icrito51393.2021.9596276.
[3] R. Kumar And Rakesh, "Music Recommendation System Using Machine Learning," 2022 4th International Conference On Advances In Computing, Communication Control And Networking (Icac3n), Greater Noida, India, 2022, Pp. 572-576, Doi: 10.1109/Icac3n56670.2022.10074362.
[4] R. R. Jaison C And M. Rajeswari, "Song Recommendation Based On Voice Tone Analysis," 2023 Second International Conference On Electronics And Renewable Systems (Icears), Tuticorin, India, 2023, Pp. 708-712, Doi:
[5] K. Vayadande, P. Narkhede, S. Nikam, N. Punde, S. Hukare And R. Thakur, "Facial Emotion Based Song Recommendation System," 2023 International Conference On Computational Intelligence And Sustainable Engineering Solutions (Cises), Greater Noida, India, 2023, Pp. 240-248, Doi: 10.1109/Cises58720.2023.10183606.

Abstract: The objective of our statistical anomaly detection project is to develop an IDS that notices behavior which is based on observed action, detects abnormalities or possible intrusions. The system utilizes statistical analysis techniques, specifically features to discover anomalous patterns of activity, and receives input data in the form of stats.txt and event.txt files. The project's goal is to offer a dependable and efficient IDS that can assist in identifying and averting cyberattacks on networks and computer systems.....

Keywords — anomaly; network intrusion; statistical analysis; intrusion detection system; and.txt

[1] A Survey On Anomaly Detection Was Conducted By Banerjee, Kumar, And Chandola In 2009 And Published In Acm Computing Surveys, Vol. 41, No. 3, Pp. 1-58.
[2] In 2018, The International Journal Of Computer Applications Published A Comprehensive Assessment Of An Intrusion Detection System, Titled "Intrusion Detection System: A Comprehensive Review," Written By Mohammed Alkhazraji, Alkhazraji, And Abdulkareem, A. M.
[3] In 2017, Sahu And Reddy Conducted A Thorough Analysis Of Anomaly-Based Intrusion Detection Systems In The Journal Of Network And Computer Applications, Volume 94, Pages 62–82.
[4] "Review Of Anomaly Detection Techniques For Intrusion Detection System," A. L. N. Reddy And D. D. K. R. Chowdary, International Journal Of Engineering Research And Applications, Vol. 6, No. 8, Pp. 58-63, 2016.
[5] "Intrusion Detection System: A Review," S. K. Jha And R. K. Pandey, International Journal Of Computer Applications, Vol. 64, No. 4, Pp. 1-7, 2013..

Abstract: Résumé Cet article s'est proposé d'analyser l'impact de l'inflation sur les prix des produits de première nécessité. Partant d'un petit survol théorique sur l'inflation et quelques considérations empiriques, dans notre étude les données retenues ont étés tirées des différentes sources des données sur l'économie congolaise, entre autres : la division économique du Haut-Katanga pour les prix des divers produits retenus, d'une part et la Banque Centrale du Congo à travers le bulletin d'information statistique à ce qui concerne le taux de change indicatif, d'autre part. Après analyse des.....

[1] Bakala, T. A. (2015). Les Théories Monétaires Avant La Théorie Générale De Keynes. La Monnaie Endogène, 31-88.
[2] Banza Ilunga, B. M. (2023). L'inflation : Analyse De Ses Effets Sur Les Prix Des Produits De Première Nécessité. Doi:10.52502/Ijesm.V1i2.230: International Journal Of Economic Studies And Management (Ijesm).
[3] Claude-Daniele. (2009). Dictionnaire D'économie Et De Sciences Sociales. Italie: Nathan. Degryse, C. (2019). Inflation. L'économie En 100 Mots D'actualité, 120-121.
[4] Diop, M. (2014). Étude Critique De La Théorie Quantitative De La Monnaie Dans L'histoire Des Crises Des Prix (Thèse Doctorale). Paris: Centre D'études Monétaires Et Financières (Cemf) , Laboratoire D'économie De Dijon (Ledi) - Umr Cnrs 6307.
[5] Éric Nasica, C. G. (2018). Adolphe Landry, Interprète Original De La Théorie Quantitative De La Monnaie. L'harmattan, 35-62.

Abstract: The progression of automatous automobile navigation systems relies on the study of Traffic Sign Recognition (TSR). Because of the need to constantly analyze the situation to make decisions, all autonomous driving systems lack the capacity to run complex image processing algorithms as well as large-scale computing responsibilities. However, they need the efficient calculations of input data in order to generate outputs in the shortest time possible. This is due the fact that there are a lot of traffic signs, advertisements, parked cars, individuals and many other vehicles and objects both in motion and stationery in urban environments. While the strategies discussed in literature for the implementation....

Keywords: Road Sign Detection, Machine Learning, R-CNN, Image Classification, Deep Learning

[1] Haque, W. A., Arefin, S., Shihavuddin, A. S. M., & Hasan, M. A. (2021). Deepthin: A Novel Lightweight Cnn Architecture For Traffic Sign Recognition Without Gpu Requirements. Expert Systems With Applications, 168, 114481.
[2] Dewi, C., Chen, R. C., Jiang, X., & Yu, H. (2022). Deep Convolutional Neural Network For Enhancing Traffic Sign Recognition Developed On Yolo V4. Multimedia Tools And Applications, 81(26), 37821- 37845.
[3] Hijji, M., Iqbal, R., Pandey, A. K., Doctor, F., Karyotis, C., Rajeh, W., ... & Aradah, F. (2023). 6g Connected Vehicle Framework To Support Intelligent Road Maintenance Using Deep Learning Data Fusion. Ieee Transactions On Intelligent Transportation Systems.
[4] Ahmed, S., Kamal, U., & Hasan, M. K. (2021). Dfr-Tsd: A Deep Learning Based Framework For Robust Traffic Sign Detection Under Challenging Weather Conditions. Ieee Transactions On Intelligent Transportation Systems, 23(6), 5150-5162.
[5] Mall, P. K., Narayan, V., Pramanik, S., Srivastava, S., Faiz, M., Sriramulu, S., & Kumar, M. N. (2023). Fuzzynet-Based Modelling Smart Traffic System In Smart Cities Using Deep Learning Models. In Handbook Of Research On Data-Driven Mathematical Modeling In Smart Cities (Pp. 76-95). Igi Global.

Abstract: Deep learning has revolutionized many fields, but caused the 'black-box' problem, where model prediction is not interpretable and transparent. Explainable Artificial Intelligence (XAI) attempts to overcome this problem with the help of Interpretability and Transparency in AI systems. We review important XAI methods focusing on LIME, SHAP and saliency maps that explain the elements behind model predictions. The paper discusses about the role of Explainable Artificial Intelligence (XAI) in high-stake fields such as healthcare, finance and autonomous systems, emphasizing on why trust is important for these sectors and how they help adhere to regulations while promoting ethical AI use.....

Keywords: Explainable Artificial Intelligence (XAI), Deep Learning Interpretability, Black-Box Problem, Ethical AI, Transparency in AI.

[1]. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness And Machine Learning: Limitations And Opportunities. Mit Press.
[2]. Chaddad, A., Hassan, M. E., Desrosiers, C., & Toews, M. (2023). Explainable Ai For Diagnostic Support In Healthcare: A Review Of Recent Advancements. Computers In Biology And Medicine, 142, 105158. Https://Doi.Org/10.1016/J.Compbiomed.2021.105158
[3]. Cheng, J., Wu, Y., & Gao, X. (2021). A Survey Of Explainable Artificial Intelligence In Deep Learning. Artificial Intelligence Review, 54(6), 3555–3575. Https://Doi.Org/10.1007/S10462-020-09848-8
[4]. Cheng, L., Ma, Y., Zhang, X., Liu, H., Chen, X., & Zhang, H. (2021). Explanation Of Artificial Intelligence: A Review Of Interpretability Of Deep Learning. Ieee Access, 9, 58024–58039. Https://Doi.Org/10.1109/Access.2021.3071121
[5]. Choubisa, R. (2024). Ethics And Transparency In Ai-Driven Decision-Making. Journal Of Ethics In Ai Research, 12(1), 54–70.

Abstract: As the need for early detection and mitigation of potential threats from near-Earth objects grows, this study presents a comprehensive approach to predicting hazardous asteroids using machine learning techniques. Given the increasing importance of protecting our planet from potential impact events, accurately classifying and predicting hazardous asteroids is cru- cial. The study proposes a comprehensive system for predicting and mitigating asteroid hazards using advanced data processing, machine learning, and simulation techniques. The proposed study collect real-time and historical asteroid data, process and clean this data, extract relevant features, and train machine learning models to predict potentially hazardous asteroids....

Keywords: Asteroid Impact, Prediction, Potentially Haz- ardous Asteroids, Machine Learning, Random Forest Classifica- tion, – minor planets, Planetary defense.

[1] Surekha, Nemmaluri Purna, Preethi Nanjundan, And Shahid Bashir. "Prediction Of Hazardous Asteroids Using Machine Learning." In 2024 International Conference On Emerging Systems And Intelligent Comput- Ing (ESIC), Pp. 169-174. IEEE, 2024.
[2] Bahel, Vedant, Pratik Bhongade, Jagrity Sharma, Samiksha Shukla, And Mahendra Gaikwad. "Supervised Classification For Analysis And Detection Of Potentially Hazardous Asteroid." In 2021 International Conference On Computational Intelligence And Computing Applications (ICCICA), Pp. 1-4. IEEE, 2021.
[3] Sandeep, Avala, Panta Jeevan Reddy, And T. Madhu Perkin. "Planetary Defense Using Machine Learning Early Warning Systems For Hazardous Asteroids." In 2024 International Conference On Advances In Data Engineering And Intelligent Computing Systems (ADICS), Pp. 1-6. IEEE, 2024.
[4] Bhavsar, Rushir, Nilesh Kumar Jadav, Umesh Bodkhe, Rajesh Gupta, Sudeep Tanwar, Gulshan Sharma, Pitshou N. Bokoro, And Ravi Sharma. "Classification Of Potentially Hazardous Asteroids Using Supervised Quan- Tum Machine Learning." IEEE Access (2023).
[5] Si, Anish. "Hazardous Asteroid Classification Through Various Machine Learning Techniques." International Research Journal Of Engineering And Technology 7, No. 3 (2020): 5388-5390.