Dr. Makarand Shahade
@svkm-iot.ac.in
Associate Professor and HOD Computer Engineering
SVKM's Institute of Technology, Dhule
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Networks and Communications, Artificial Intelligence, Computer Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Hemant B. Mahajan, Nilesh Uke, Priya Pise, Makarand Shahade, Vandana G. Dixit, Swapna Bhavsar, and Sarita D. Deshpande
Springer Science and Business Media LLC
Nandwalkar Bhushan, Pardeshi Sukruta, Shahade Makarand, and Awate Ashish
Totem Publisher, Inc.
Vijay A. Kotkar, Avinash L. Golande, Kirti V. Deshpande, Makarand Shahade, and Vinodkumar H. Bhutnal
Springer Science and Business Media LLC
Vijaylaxmi Bittal, Anuradha Sanjay Bachhav, Makarand Shahade, Pradnya Rajendra Chavan, Bhavesh Anil Nikam, and Ajinkya Anil Pawar
IEEE
Most of the technical concepts written in textual way and these are communicated orally by teachers to students through limited images. Students not able to understand some of the complex topics in expected way. This will create a gap between subject and student's subject understandability. Representing and conveying through images is a convenient and effective way for Teaching Learning process. Filling this gap is very essential because understanding through visualization will enhance learning process. Though, the combo of speech encoding and computational translation in only one model causes a huge difficulty and many complexities. In order to reduce those efforts, speech to image translation framework plays significant role. With this motivation authors designed a framework for teachers-students learning process. In this framework, authors designed framework to abstract characteristics from audio files and trained model using unsupervised machine learning algorithm. In this firstly, a speech encoder encodes the audio and extract some significant and highlighted features from it and applying algorithm on this feature, Authors perform mapping process on these parameters and features of images which are present in dataset. After this process, GAN comes into play which produce corresponding image as output. In comparison to other methods like speech-text-image, text-image, speech-text models, our model approaches on synthesized dataset and reach to higher expectations of user.
Khalid Alfatmi, Falguni Shashikant Shinde, Makarand Shahade, Shalaka Sanjay Sharma, Sejal Sanjay Aruja, and Tanmay Yograj Chaudhari
IEEE
Technology and related fields are advancing rapidly in today’s era, which is leading to the increased usage of electronic equipment especially mobile phones. This rapid growth in the usage of technological equipment gives rise to E-waste generation since the advancement in technology forces the users to replace their existing equipment with the advanced ones. This throw on the scrapheap is increasing exponentially, leading to the immense generation of E-waste. The growing amount of waste must be properly handled and disposed off. E-waste mainly consists of chemicals and metals, which can be harmful or toxic in nature. Giving a blind eye to the handling of this waste can lead to great mishaps. Here, it mainly focuses on the technologies used for the handling of E-waste and provide a technical solution, which is an Android Application. This will take the image for processing and based on YOLO algorithms, it will identify the electronic device from image and will list out the components of identified device. The final output of the application will make the E-waste management process easier for managing data and will work as an awareness tool to make people aware about the E-Waste threats.
C. A. Dhote, R. S. Mangrulkar, and M. Shahade
ACM
Mobile ad hoc networks (MaNeT) play an important role in connecting devices in pervasive environments. Each node in MaNet can act as source and router. In this paper, we propose a hybrid routing protocol with Unicast Reply(HRP-UR) which combines the merits of both proactive and reactive approach. Like proactive approach, it maintains routing table at every node. However, it differs from proactive approach; that the routing table is not built prior to communication. Routing table are built in incremental steps during route discovery. Route discovery takes place like reactive approach only on demand. HRP-BR takes advantage of broadcast nature in MaNet for route discovery and store maximum information in the routing tables at each node. Broadcast nature avoids handshaking of RTS and CTS and effectively utilize trans-receiver antennas which reduce power consumption and effectively utilize bandwidth. HRP-BR is compared with existing AODV routing protocol which shows significant reduction in routing overhead, end-to-end delay and increases packet delivery ratio.