iosr-JECE   Volume-17 ~ Issue-5 ~ Sep. - Oct. 2022

Abstract: Rice is a primary food and Encounter has an Essential role in providing food security worldwide. However, the existing Disease diagnosis method for rice i s neither accurate norefficient, and special equipment is often required. In this study, The disease classification is done by an SVM classifier and therefore the detection accuracy is improved by optimizing the info exploitation. In this proposed system we are using image processing techniques to classify diseases. This approach will enhance the productivity of crops. Furthermore in precision agriculture, The accurate segmentation of crops and weeds has been always been the center of attention. This work proposes a segmentation method based on a.....

Keywords: semantic segmentation; k-means algorithm; precision agriculture; leaf disease detection SVM.

[1]. Zhang, S. W., Shang, Y. J., & Wang, L. (2015). "Plant disease recognition based on plant leaf image." Journal of Animal & Plant Sciences, 25(3), 42-45.
[2]. K. Jagan Mohan, M. Balasubramanian, S. Palanivel, "Detection and Recognition of Diseases from Paddy Plant Leaf Images ", IJCA, Volume 144 – Number 12, Page No. 34-41
[3]. Santiago, W. E., Leite, N. J., Teruel, B. J., Karkee, M., Azania, C. A. M. et al. (2019). Evaluation of bag-of- features (BOF) technique for weed management in sugarcane production. Australian Journal of Crop Science, 130(11), 0 1819. https://doi.org/10.21475/
[4]. Bakhshipour, A., & Jafari, A. (2018). Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Computers and Electronics in Agriculture, 145, 0 153–160. https://doi.org/10.1016/j.compag.2017.12.032
[5]. Diptesh Majumdar, Dipak Kumar Kole, Aruna Chakraborty, Dwijesh Dutta Majumder, REVIEW: DETECTION & DIAGNOSIS OF PLANT LEAF DISEASE USING INTEGRATED IMAGE PROCESSING APPROACH, International Journal of Computer Engineering and Applications, Volume VI, Issue-III June 2014

Abstract: Educational Data Mining is a prominent area toexplore information in educational fields using data miningalgorithms. In this paper, we have used a few learning algorithmsto effectively rate the faculty belonging to an educational instituteon the basis of feedback submitted by the students. Our proposedmodel uses sentimental analysis and machine learning classifieralgorithms for capturing the emotions from the student'sfeedback system. This model gives an accurate and efficient wayto rate the faculty belonging of a particular educational institute.Sentimental Analysis is a reference to the task of natural languageprocessing to determine whether a text contains subjective information and what information it expresses i.e.,whether the attitude behind the text is positive, negative orneutral, examine the sentiments present in the text document forclassification of students' feedback based on polarity (positive/negative/ neutral) using machine learning and NLP methods..

Keywords: Educational Data Mining,sentimental analysis, classifier algorithms,NLP methods

[1]. L.Kousalya and R.Subhashini, "Sentimental Analysis for Students' Feedback using Machine Learning Approach", International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2395-0056, Volume-06 Issue-04 -04-2019 .
[2]. Daneena Deeksha Dsouza, Deepika, Divya P Nayak," Sentimental Analysis of Student Feedback using Machine Learning Techniques ", International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-1S4, June 2019.
[3]. K. S.Krishnaveni, Rohit R Pai, V. Iyer, "Faculty Rating System Based on Student Feedbacks Using Sentimental Analysis", International Conference on Advances on Computing, Communication and Informatics, pp.1648-1653, 2017.
[4]. B. K.Bhavitha, A. P. Rodrigues, N. N Chiplunkar, "Comparative Study of Machine Learning Techniques in Sentimental Analysis". International Conference on Inventive Communication and Computational Technologies, pp.216- 221, 2017.
[5]. Zarmeen Nasim, Quratulain Rajput and Sajjad Haide," Sentiment Analysis of Student Feedback Using Machine Learning and Lexicon Based Approaches", Institute of Electrical and Electronics Engineers (IEEE),e-ISSN:2324-8157

Abstract: The idea of developing this project comes from social responsibilitytowardsthe society. Bike riding is fun, but accidents are inevitable. In India, more than 37 million people are usingtwo-wheelervehicles. Since usage is high accident percentage oftwo-wheelers are also high. This project aims for accident avoidance, safety andsecurity of the bike rider. The main purpose of this project is to encourage wearinghelmets. The proposed system willensurethat the motorbikedoes not start unless therider iswearing a helmet and has not consumedalcohol. Hence, making sure that therider is fit to ride..

Keywords: Safety, Smart Helmet, IoT

[1]. H.C.Impana, M. Hamsaveni and H.T. Chethana" A Review on Smart Helmet for AccidentDetection using IOT", EAI Endorsed Transactions on Internet of Things.
[2]. Mr.Sethuramrao, Vishnupriya.S.M , Mirnalini.Y, Padmapriya.R.S" The High SecuritySmart Helmet Using Internet Of Things", International Journal of Pure and AppliedMathematics Volume 119 No. 12 2018, 14439-14450.
[3]. Divyasudha N, Arulmozhivarman P, Rajkumar E.R, Analysis of Smart helmets andDesigning an IoT based smart helmet: A cost effective solution for Riders.
[4]. Jesudoss A, Vybhavi R and Anusha B, "Design of Smart Helmet for Accident Avoidance",International Conference on Communication and Signal Processing, April 4- 6, 2019,India.
[5]. Sreenithy Chandran, Sneha Chandrasekar, N Edna Elizabeth, "Konnect: An Internet ofThings (IoT) based smart helmet for accident detection and notification" 2016 IEEEAnnual India Conference (INDICON).

Abstract: This research work explores the growth, and success factors of family-owned businesses originated in India versus public business.As a case study for our research we have used Adani Power as a family owned business and ReNew Power as a public business.
Adani Power was established in August 1996. The company develops and maintains power projects and is the largest private sector power generation company in India. It has a combined installed capacity of 12.45 GW with four thermal power projects across India.
ReNew Power was established in August 2011....

Keywords: Growth Analysis, Case study of Entrepreneurship, Case study of Family-Owned business

[1] "Adani Power registers Rs 634.64 crore profit in Q4". The Economic Times. 29 May 2019.
[2] "Adani Power shares jump nearly 7 pc post Q4 results". The Financial Express. 30 May 2019.
[3] Chandna, Himani. "The Rise of the Tycoon". BW Businessworld.
[4] Thakurta, Paranjoy Guha. "The Incredible Rise and Rise of Gautam Adani: Part One". The Citizen.
[5] "Adani Power Limited". swapdial.com..

Abstract: Minimum mean-square error frequency domain equalizer (MMSE-FDE) is a promising technique that can handle the effect of a multi-path propagation channel and hence enhance the bit error rate (BER) performance in MIMO systems. MMSE-FDE is complex because its adapted weights require the calculation of channel state information (CSI) and signal-to interference plus noise power ratio (SINR). Both are considered as difficult tasks and important issues to obtain especially in long term evolution (LTE) and massive MIMO systems. To tackle these issues, adaptive affine projection frequency domain equalizer (AAPFDE) used within overlap-save method (OLSM....

Keywords: Frequency domain equalizer; MIMO; overlap-add, overlap-save; SC-FDMA

[1]. N. Iqbal, et al., "Adaptive Frequency-Domain RLS DFE for Uplink MIMO SC-FDMA," IEEE Transaction on Vehicular Technology, vol. 64, no. 7, pp. 2819–2833, July 2015.
[2]. H-S. Wang, Fang-Biau Heng and Yu-Kuan Chang,"Novel Turbo Receiver for MU-MIMO SC-FDMA System," ETRI Journal, vol. 40, no. 3, pp.309–317, April 2018.
[3]. M. Anbar, et al., "Iterative SC-FDMA Frequency Domain Equalization and phase Noise Mitigation," IEEE in Proceeding IEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), Ishigaki, Japan, pp. 91–95, Nov. 2018.
[4]. S. Sardar, et al.," A Framework for Iterative frequency Domain EP-Based Receiver Design," IEEE Transactions on Communications, vol. 66, no. 12, pp. 6478-6493, Dec. 2018.
[5]. B. Dhivagar, K. Kuchi, and K. Giridhar, "An Iterative DFE Receiver for MIMO SC-FDMA Uplink," IEEE Communications Letters, vol.18, no. 12, pp. 2141-2144, Dec. 2014.

Abstract: This paper aims at increasing energy efficiency of CR system by minimizing sensing and interference time. With accurate PU behaviour information optimum system can be achieved. As first step through DQN synchronization of SU with PU reduces the interference. Secondly, SU will be synchronized at that instant of PU change. Thirdly, SU is synchronized with PU transitions through DQN timeseries analysis. Making SU to change the states in synchronization with PU, with low sensing and interference maximising energy efficiency.

Keywords: Cognitive Radio, Time series, Reinforcement Learning

[1] M. Bkassiny, S. Member, Y. Li, S. Member, S. K. Jayaweera, and S. Member, "A..A Survey on Machine-Learning Techniques in," IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1136–1159, 2013.
[2] T. Chen, H. H. Chen, Z. Chang, and S. Mao, "Machine-Learning-Based Opportunistic Spectrum Access in Cognitive Radio Networks," IEEE Wireless Communications, vol. 27, no. 1, pp. 6–8, 2020.
[3] S. Zhang, T. Wu, M. Pan, C. Zhang, and Y. Yu, "A-SARSA: A Predictive Container Auto-Scaling Algorithm Based on Reinforcement Learning," Proceedings - 2020 IEEE 13th International Conference on Web Services, ICWS 2020, pp. 489–497, 2020, doi: 10.1109/ICWS49710.2020.00072.
[4] T. Qiu, R. Qiao, and D. O. Wu, "EABS: An event-aware backpressure scheduling scheme for emergency internet of things," IEEE Transactions on Mobile Computing, vol. 17, no. 1, pp. 72–84, 2018, doi: 10.1109/TMC.2017.2702670.
[5] T. Qiu, X. Wang, C. Chen, M. Atiquzzaman, and L. Liu, "TMED: A spider-web-like transmission mechanism for emergency data in vehicular Ad Hoc networks," IEEE Transactions on Vehicular Technology, vol. 67, no. 9, pp. 8682–8694, 2018, doi: 10.1109/TVT.2018.2841348.

Abstract: This work focused on the deployment of 5G network for efficient spectrum utilization in Alakahia,Obio/Akpor Local Government, Rivers State Nigeria. 5G is yet to be deployed for mobile communication in Nigeria, hence the need to investigate its deployment requirement and challenges. The choice of the region is influenced by its population density to accommodate greater capacity and high traffic demand of 5G network. The work demonstrates the ability of transmit base station to adequately sight the receiver stations in the designated area based on a non-standalone approach. MATLAB 2021 software was used to simulate antenna scenarios defined by different transmitters and receivers. From the results obtained, the region is strongly covered with the following measured.....

Keywords: 5G Network, Cell towers, Latency, Network capacity, spectrum utilization

[1]. C. Y. Chang and M. H. Li, "A placement mechanism for relay stations in 802.16j WiMAX networks," Wirel. Networks, vol. 20, no. 2, pp. 227–243, 2014, doi: 10.1007/s11276-013-0604-y.
[2]. F. Tonini, M. Fiorani, C. Raffaelli, L. Wosinska, and P. Monti, "Benefits of joint planning of small cells and fiber backhaul in 5G dense cellular networks," in IEEE International Conference on Communications, 2017, pp. 1–6, doi: 10.1109/ICC.2017.7997216.
[3]. T. E. Bogale and L. B. Le, "Massive MIMO and mmWave for 5G Wireless HetNet: Potential Benefits and Challenges," IEEE Veh. Technol. Mag., vol. 11, no. 1, pp. 64–75, 2016, doi: 10.1109/MVT.2015.2496240.
[4]. M. Ozturk, M. Gogate, O. Onireti, A. Adeel, A. Hussain, and M. A. Imran, "A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA)," Neurocomputing, vol. 358, no. September, pp. 479–489, 2019, doi: 10.1016/j.neucom.2019.01.031.
[5]. R. Falkenberg, B. Sliwa, N. Piatkowski, and C. Wietfeld, "Machine Learning Based Uplink Transmission Power Prediction for LTE and Upcoming 5G Networks Using Passive Downlink Indicators," IEEE Veh. Technol. Conf., vol. 2018-Augus, 2018, doi: 10.1109/VTCFall.2018.8690629..