I am Dr. Dipali Shende, working as Assistant Professor in Pimpri Chinchwad College of Engineering and Research, Ravet. I have altogether Experience of 20 Years; I have UGC approval of 15 years, and recognized PG Teacher since 2024. My keen interest is in Embedded System Design, Internet of Things, IoT, AI, Assembly and Embedded C. I have received different awards at National, International and Institute level.
I have completed a Ph. D. in E & Tc Engineering on the topic “Energy and Trust Aware Metaheuristic Multicast Routing for IoT Application” from Savitribai Phule Pune University under the guidance of Dr. Yogesh Angal, in 2023.I received a M. E. in E & Tc Engineering specialization VLSI-Embedded in 2011 and B.E in E & Tc Engineering with distinction in 2003, from Dr. Babasaheb Ambedkar Marathwada University.
Sewage water management and healthcare monitoring in IoT using Optimized deep residual network Dipali Shende, Yogesh S. Angal Journal of Experimental and Theoretical Artificial Intelligence, 2025 The Internet of Things (IoT) is termed as the interconnection of different smart objects with respect to devices. In this research, two different application scenarios are considered to show the efficiency of the Deep Residual Network (DRN) through multicast routing. The entities involved in the process are IoT nodes, IoT heads, and base stations (BS). The nodes are allowed to capture the information, and the collected data are routed to BS through the head node. The process of routing is made using the CrowWhale optimisation algorithm that enables to transfer the data packets from IoT nodes to BS. In the sewage water management system, entering sewage water into fresh water is detected by DRN which is trained using an optimisation algorithm. In the healthcare system, heart disease prediction is done using DRN to detect normal and abnormal cases more effectively. The adopted CrowWhale-ETR+DRN offered energy, accuracy and sensitivity as 82.54, 0.967, and 0.978 with 100 nodes for the environmental protection dataset. The energy, accuracy, and sensitivity obtained by the proposed model are 83.232, 0.964, and 0.974 using 100 nodes for the heart disease dataset, respectively.
An efficient fault diagnosis model using Lappet Falco optimisation based on a deep neural network for the VSI under varying load conditions Vaishali Baste, Dipali Shende, Seema Idhate, Arya Deshpande International Journal of Power Electronics, 2025 Numerous industrial applications employ three-phase converters that are based on insulated-gate bipolar transistors (IGBTs). However, the functioning and safety of power electronic devices and loads can be considerably impacted by IGBT faults. Maintaining high-power quality and system availability requires timely and accurate detection of power inverter failures. Constantly monitoring the failures in three-phase voltage source inverter (VSI) has greatly improved maintenance efficiency and stability. Hence, the developed research employs the discrete wavelet transform (DWT) and Lappet Falco optimised deep neural network (LFO-DNN) model to create an open circuit fault detection model for the VSI circuit. Data collection involves extracting features such as three-phase voltage, current, speed, and torque from erroneous data. The DNN classifier trained on these features uses the average three-phase current value to identify faulty switches. The VSI acting as a load with variable frequency reference is connected to a three-phase induction motor. The proposed Lappet Falco optimisation accurately yields impressive results in terms of prediction accuracy of 96.34%, precision of 96.34%, recall of 96.24%, F1 measure of 94.23%, MSE of 3.66, and specificity of 95.28%, demonstrating high efficiency for both 90% training and a k-fold value of 10.
Performance evaluation and comparative analysis of CrowWhale-energy and trust aware multicast routing algorithm Dipali K. Shende, Yogesh S. Angal Web Intelligence, 2023 Multipath routing helps to establish various quality of service parameters, which is significant in helping multimedia broadcasting in the Internet of Things (IoT). Traditional multicast routing in IoT mainly concentrates on ad hoc sensor networking environments, which are not approachable and vigorous enough for assisting multimedia applications in an IoT environment. For resolving the challenging issues of multicast routing in IoT, CrowWhale-energy and trust-aware multicast routing (CrowWhale-ETR) have been devised. In this research, the routing performance of CrowWhale-ETR is analyzed by comparing it with optimization-based routing, routing protocols, and objective functions. Here, the optimization-based algorithm, namely the Spider Monkey Optimization algorithm (SMO), Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO) algorithm, Water Wave Optimization (WWO) algorithm, Crow Search Algorithm (CSA), and, routing protocols, like Ad hoc On-Demand Distance Vector (AODV), CTrust-RPL, Energy-Harvesting-Aware Routing Algorithm (EHARA), light-weight trust-based Quality of Service (QoS) routing, and Energy-awareness Load Balancing-Faster Local Repair (ELB-FLR) and the objective functions, such as energy, distance, delay, trust, link lifetime (LLT) and EDDTL (all objectives) are utilized for comparing the performance of CrowWhale-ETR. In addition, the performance of CrowWhale-ETR is analyzed in terms of delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput, and it achieved better values of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using EDDTL as fitness.
An Iterative CrowWhale-Based Optimization Model for Energy-Aware Multicast Routing in IoT Dipali K. Shende, Yogesh S. Angal, S.C. Patil. International Journal of Information Security and Privacy, 2022 This paper proposes an energy-aware multicast routing protocol (MRP) based on the optimization algorithm named iterative Crow Whale-Energy Trust routing (iterative CrowWhale-ETR). The CrowWhale-ETR is developed by including the historical terms from Taylor series in the CrowWhale optimization algorithm. Initially, the effective nodes for the multicast routing process are considered by measuring the trust and energy level of nodes. Based on the fitness factor, the protected nodes are selected relies on the trust and energy level of individual nodes. Once the secure nodes are selected, route detection and route selection is performed based on iterative CrowWhale-ETR. Finally, the route maintenance is done as per the remaining energy and trust factors of the nodes in the network. The comparative analysis of developed iterative CrowWhale-ETR is performed with the evaluation metrics, like energy, delay, throughput and detection rate using 50 and 100 nodes in the presence as well as absence of attacks.
1) Funding Proposal titled-Design and Development of an AI-Driven Workstation for Early Detection of Pancreatic Cancer through Advanced Image Analysis for healthcare applications Accepted for Evaluation on 19 Dec 2024 under ANRF ECRG category of 48Lakh.
2) Funding Proposal titled Design of Metaheuristic Sewage Water Contamination Alert Intelligent Device Using Deep Learning and Internet of Things submitted to SERB under Power Fellowship.
3) Received grant of 1,50000/- for International Conference titled “ConvergenceX: Uniting the World Through IoT and AI Innovation", during 19-20 April 2024 from PCET Management under Research Innovation cell
4) Received grant of 20,000/- to attend 3 days workshop on Research Information and Management System[RIMS] at Inflibnet Center Ahmadabad Gujrat during 11-13 Dec 2024.
4)Received QIP funding of 1,80000/- from SPPU in A.Y. 2018-19 for workshop on "IOT Transformation"
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Intellectual Property Rights [IPR] Contribution [Patent=07, Copyright=3]
1. Design Patent is filed titled “Wearable device to monitor health of Neuro-Patients” in UK Patent Journal with Design on 19 October 2024.
2. Utility Patent filed on Enhanced Wireless Connectivity Input Device for Visually Impaired Persons, Patent No. :202421080466 in Indian Patent Journal dated 22 October 2024.
3. Design Patent Granted on titled “Multipurpose Bottle” with Design number 380738-001 on 2 May 2024 in Indian Patent Journal.
4. Patent Granted titled “System to Alarm Freshwater Contamination Based on Intelligent Device with IoT-Based Multicast Routing” with Patent Number: - 500978 in the Indian Patent Office Journal on 18th January 2024.
5. Patent Granted titled “A DEVICE FOR RESCUE FROM BORE-WELL” with Patent Number-397178-Application Number-202021022910 on 11 December 2020 at the Indian Patent Office Journal No. 50/2020 dated 19 May 2022.
6. Patent Published on “Smart & Intelligent lamp pole consisting of Insect Killer Whether Station WIFI and EV charging station” in Indian Patent journal with Patent NO: -202121059138 on 23 Dec 2021.
7. Patent Published on “An Electric Auto Toggle Holder” in Indian Patent journal with Patent NO: -202121057198 on 12 Sept 2021.
INDUSTRY EXPERIENCE
Industrial Experience:2 years
1. 2004 Aug to Dec 2006
Working as a Junior Engg in department of Design & Development at Intellect Micro controls .
Job Profile: Junior Engg, [D & D Dept.]
Platform:’C’ Language,Linux
